diff --git "a/data/resources.csv" "b/data/resources.csv"
--- "a/data/resources.csv"
+++ "b/data/resources.csv"
@@ -1,541 +1,541 @@
 row_id,section,section_slug,resource_type,marker,title,url,url_kind,domain,annotation,description,key_contribution,novelty,impact,signal,signal_strength,source_readme,source_line,source_url,date_added,collection,collection_slug,user_goal,lifecycle_stages,audience,evidence_class,source_status,canonical_url,source_title,source_description,authors,publication_date,publication_year,publication_venue,publisher,doi,publication_note,primary_category,metadata_source,github_repo,github_stars,github_forks,github_license,github_created_at,github_updated_at,arxiv_id,audited_at
-ale-0001,Concept Guides,concept-guides,Template,🧾,Canonical Definition,DEFINITION.md,local_path,,"Short definition, positioning, minimal loop test, and citation note.","Short definition, positioning, minimal loop test, and citation note.","Short definition, positioning, minimal loop test, and citation note.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Short definition, positioning, minimal loop test, and citation note.","Gives readers a concrete source in Canonical Definition when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,259,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L259,,Learn,learn,Understand the field and its boundaries.,verification,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/DEFINITION.md,Canonical Definition,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0002,Concept Guides,concept-guides,Template,🧾,Loop Engineering Manifesto,MANIFESTO.md,local_path,,"Concise statement of the concept, commitments, non-goals, and success standard.","Concise statement of the concept, commitments, non-goals, and success standard.","Concise statement of the concept, commitments, non-goals, and success standard.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Concise statement of the concept, commitments, non-goals, and success standard.","Gives readers a concrete source in Loop Engineering Manifesto when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,260,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L260,,Learn,learn,Understand the field and its boundaries.,objective,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/MANIFESTO.md,Loop Engineering Manifesto,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0003,Concept Guides,concept-guides,Template,🧾,Loop Engineering Taxonomy,TAXONOMY.md,local_path,,"Classification by trigger, intake, verification, state model, topology, and operating domain.","Classification by trigger, intake, verification, state model, topology, and operating domain.","Classification by trigger, intake, verification, state model, topology, and operating domain.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Classification by trigger, intake, verification, state model, topology, and operating domain.","Gives readers a concrete source in Loop Engineering Taxonomy when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,261,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L261,,Learn,learn,Understand the field and its boundaries.,trigger;intake;verification;state,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/TAXONOMY.md,Loop Engineering Taxonomy,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0004,Concept Guides,concept-guides,Critique,⚠️,Loop Engineering Anti-Patterns,ANTI-PATTERNS.md,local_path,,"Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Gives readers a concrete source in Loop Engineering Anti-Patterns when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,262,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L262,,Learn,learn,Understand the field and its boundaries.,state;budget;escalation,newcomer,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/ANTI-PATTERNS.md,Loop Engineering Anti-Patterns,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0005,Concept Guides,concept-guides,Template,🧾,Comparison Guide,COMPARISON.md,local_path,,"Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Gives readers a concrete source in Comparison Guide when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,263,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L263,,Learn,learn,Understand the field and its boundaries.,context;verification,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/COMPARISON.md,Comparison Guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0006,Concept Guides,concept-guides,Template,🧾,Sourced Signals And Quotes,QUOTES.md,local_path,,Short sourced signals from linked public materials that anchor the emerging concept.,Short sourced signals from linked public materials that anchor the emerging concept.,Short sourced signals from linked public materials that anchor the emerging concept.,Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Short sourced signals from linked public materials that anchor the emerging concept.,"Gives readers a concrete source in Sourced Signals And Quotes when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,264,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L264,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/QUOTES.md,Sourced Signals And Quotes,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0007,Concept Guides,concept-guides,Template,🧾,Outreach Kit,meta/OUTREACH.md,local_path,,"Conservative messages for inviting corrections, sources, and real-world loop patterns.","Conservative messages for inviting corrections, sources, and real-world loop patterns.","Conservative messages for inviting corrections, sources, and real-world loop patterns.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Conservative messages for inviting corrections, sources, and real-world loop patterns.","Gives readers a concrete source in Outreach Kit when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,265,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L265,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/OUTREACH.md,Outreach Kit,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0008,Start Here,start-here,Blog,📝,Loop Engineering by Addy Osmani,https://addyosmani.com/blog/loop-engineering/,external,addyosmani.com,"Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Gives readers a concrete source in Loop Engineering by Addy Osmani when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,345,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L345,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://addyosmani.com/blog/loop-engineering/,AddyOsmani.com - Loop Engineering,You don't really need to be good at prompting anymore. The thing to get good at is the loop that does the prompting for you. It's five building blocks plus s...,Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0009,Start Here,start-here,Blog,📝,Peter Steinberger on designing loops,https://x.com/steipete/status/2063697162748260627,external,x.com,"The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","Gives readers a concrete source in Peter Steinberger on designing loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,346,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L346,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://x.com/steipete/status/2063697162748260627,"Peter Steinberger 🦞 on X: ""Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents."" / X",Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.,,2026-06-07,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0010,Start Here,start-here,Blog,📝,Boris Cherny: five tips for running Opus autonomously for hours or days,https://x.com/bcherny/status/2063792263067754658,external,x.com,"The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","Gives readers a concrete source in Boris Cherny: five tips for running Opus autonomously for hours or days when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,347,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L347,,Learn,learn,Understand the field and its boundaries.,objective;workspace;verification,newcomer,practitioner-analysis,ok,https://x.com/bcherny/status/2063792263067754658,"Boris Cherny on X: ""Seeing a number of benchmarks showing Opus is the best model for long-running work. Five tips for running Opus autonomously for hours/days: 1. Use auto mode for permissions, so Claude doesn’t ask for approval 2. Use dynamic workflows, to have Claude orchestrate"" / X","Seeing a number of benchmarks showing Opus is the best model for long-running work. Five tips for running Opus autonomously for hours/days: 1. Use auto mode for permissions, so Claude doesn’t ask for approval 2. Use dynamic workflows, to have Claude orchestrate",,2026-06-08,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0011,Start Here,start-here,Blog,📝,Loop Engineering by Cobus Greyling,https://cobusgreyling.substack.com/p/loop-engineering,external,cobusgreyling.substack.com,"Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Gives readers a concrete source in Loop Engineering by Cobus Greyling when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from cobusgreyling.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,348,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L348,,Learn,learn,Understand the field and its boundaries.,intake;delegation;verification;state,newcomer,practitioner-analysis,ok,https://cobusgreyling.substack.com/p/loop-engineering,Loop Engineering,The core of Loop Engineering,Cobus Greyling,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0012,Start Here,start-here,Blog,📝,Stop Prompting. Design the Loop.,https://www.pulumi.com/blog/stop-prompting-design-the-loop/,external,www.pulumi.com,"Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Gives readers a concrete source in Stop Prompting. Design the Loop. when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from www.pulumi.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,349,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L349,,Learn,learn,Understand the field and its boundaries.,workspace;context;delegation;verification;exit,newcomer,practitioner-analysis,ok,https://www.pulumi.com/blog/stop-prompting-design-the-loop/,Stop Prompting. Design the Loop. | Pulumi Blog,"The unit of work moved from the prompt to the loop. The five pieces of loop engineering, the memory that makes it compound, and what it won't do for you.",Engin Diri,2026-06-09,2026,,pulumi,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0013,Start Here,start-here,Blog,📝,"Writing Loops, Not Prompts, Explained",https://rico.codes/loops-not-prompts,external,rico.codes,"Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Gives readers a concrete source in Writing Loops, Not Prompts, Explained when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from rico.codes; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,350,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L350,,Learn,learn,Understand the field and its boundaries.,exit,newcomer,practitioner-analysis,ok,https://rico.codes/loops-not-prompts,"Writing Loops, Not Prompts, Explained | rico.codes","Loop engineering is not about abandoning prompts. It is about moving repeated steering work into verifiable systems so attention can stay on judgment, review, and taste.",,,,,rico.codes,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0014,Start Here,start-here,Blog,📝,Loop Engineering: A Guide for Engineers and Practitioners,https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943,external,medium.com,"Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","The resource is directly reusable as a starting artifact. Resource-specific angle: Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Gives readers a concrete source in Loop Engineering: A Guide for Engineers and Practitioners when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,351,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L351,,Learn,learn,Understand the field and its boundaries.,trigger;budget;exit,newcomer,practitioner-analysis,ok,https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943,"Medium Loop Engineering: A Guide for Engineers and Practitioners | by Adnan Masood, PhD. | Jun, 2026 | Medium","Loop engineering: designing the control system that prompts, verifies, and stops AI agents in production. A field guide for engineers.","Adnan Masood, PhD.",2026-06-24,2026,,Medium,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0015,Start Here,start-here,Blog,📝,"Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive",https://sderosiaux.substack.com/p/loop-engineering-cheap-generation,external,sderosiaux.substack.com,"Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Gives readers a concrete source in Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from sderosiaux.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,352,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L352,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://sderosiaux.substack.com/p/loop-engineering-cheap-generation,"Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive","Agentic loops make code, plans, and PRs abundant. The scarce part is knowing what is right.",Stephane Derosiaux,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0016,Start Here,start-here,Blog,📝,Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development,https://x.com/AndrewYNg/status/2071988145667928442,external,x.com,"Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Gives readers a concrete source in Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,353,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L353,,Learn,learn,Understand the field and its boundaries.,state;escalation,newcomer,practitioner-analysis,ok,https://x.com/AndrewYNg/status/2071988145667928442,"Andrew Ng on X: ""“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d https://t.co/bhuRw8lrFC"" / X","“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d",,2026-06-30,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0017,Start Here,start-here,Blog,📝,From Prompting Agents to Loop Engineering,https://x.com/omarsar0/status/2068008743153832264,external,x.com,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,"Gives readers a concrete source in From Prompting Agents to Loop Engineering when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,354,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L354,,Learn,learn,Understand the field and its boundaries.,exit,newcomer,practitioner-analysis,ok,https://x.com/omarsar0/status/2068008743153832264,"elvis on X: ""https://t.co/d8LgEwfVH6"" / X",https://t.co/d8LgEwfVH6,,2026-06-19,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0018,Start Here,start-here,Blog,📝,My Lord! AI Programming Undergoes Another Major Shift,https://eu.36kr.com/en/p/3844224911346184,external,eu.36kr.com,"Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Gives readers a concrete source in My Lord! AI Programming Undergoes Another Major Shift when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from eu.36kr.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,355,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L355,,Learn,learn,Understand the field and its boundaries.,state,newcomer,practitioner-analysis,ok,https://eu.36kr.com/en/p/3844224911346184,My Lord! AI Programming Undergoes Another Major Shift: Claude Code Father & Lobster Founder Endorse New Paradigm - Could It Kill Prompt Engineering?,Stop writing prompts for programming agents now.,,,,,eu.36kr.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0019,Start Here,start-here,Blog,📝,The Anthropic leader who built Claude Code ditched prompting - now he writes loops,https://thenewstack.io/loop-engineering/,external,thenewstack.io,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,"Gives readers a concrete source in The Anthropic leader who built Claude Code ditched prompting - now he writes loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from thenewstack.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,356,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L356,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://thenewstack.io/loop-engineering/,The Anthropic leader who built Claude Code says he ditched prompting — now he just writes loops. - The New Stack,Loop engineering — the practice of designing automated agent workflows instead of prompting manually — is reshaping how developers use Claude Code and OpenAI Codex in 2026.,Janakiram MSV,2026-06-10,2026,,The New Stack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0020,Start Here,start-here,Blog,📝,Engineering for Agents That Never Sleep,https://nader.substack.com/p/engineering-for-agents-that-never,external,nader.substack.com,"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Gives readers a concrete source in Engineering for Agents That Never Sleep when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from nader.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,357,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L357,,Learn,learn,Understand the field and its boundaries.,trigger;verification;escalation,newcomer,practitioner-analysis,ok,https://nader.substack.com/p/engineering-for-agents-that-never,Engineering for Agents That Never Sleep - by Nader Dabit,Originally posted on X.,Nader Dabit,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0021,Start Here,start-here,Blog,📝,Loop Engineering Orange Book,https://github.com/alchaincyf/loop-engineering-orange-book,external,github.com,"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","The resource is directly reusable as a starting artifact. Resource-specific angle: Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Gives readers a concrete source in Loop Engineering Orange Book when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Inspectable GitHub source (1,022 stars; 98 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,358,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L358,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://github.com/alchaincyf/loop-engineering-orange-book,GitHub - alchaincyf/loop-engineering-orange-book: 别再问我什么是 Loop Engineering — 橙皮书系列。A plain-language guide to loop engineering (中文 + English PDF). Free. · GitHub,别再问我什么是 Loop Engineering — 橙皮书系列。A plain-language guide to loop engineering (中文 + English PDF). Free. - alchaincyf/loop-engineering-orange-book,,2026-06-15,2026,alchaincyf/loop-engineering-orange-book,GitHub,,,,github-api,alchaincyf/loop-engineering-orange-book,1022,98,NOASSERTION,2026-06-15T05:27:07Z,2026-07-17T02:30:11Z,,2026-07-17T05:43:07+00:00
-ale-0022,Start Here,start-here,Blog,📝,How I AI: How to Write AI Agent Loops in Claude Code and Codex,https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops,external,www.lennysnewsletter.com,"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Gives readers a concrete source in How I AI: How to Write AI Agent Loops in Claude Code and Codex when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from www.lennysnewsletter.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,359,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L359,,Learn,learn,Understand the field and its boundaries.,objective;trigger;delegation,newcomer,practitioner-analysis,ok,https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops,🎙️ How I AI: How to write AI agent loops in Claude Code and Codex + How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead,"Your weekly listens from How I AI, part of the Lenny’s Podcast Network",Lenny Rachitsky,,,,lennysnewsletter.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0023,Start Here,start-here,Paper,📄,"Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control",https://arxiv.org/abs/2607.14890,external,arxiv.org,"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Gives readers a concrete source in Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.14890; inspect its method and evaluation before treating results as production evidence.,medium,README.md,360,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L360,2026-07-17,Learn,learn,Understand the field and its boundaries.,verification;exit,newcomer;researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14890,"[2607.14890] Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control","Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence. We present Proof-or-Stop Lifecycle Control, a method that permits lifecycle transitions only when fresh, tracked-source-state-bound, mechanically verifiable evidence satisfies the relevant gate. The method treats agent outputs as claims rather than lifecycle state, and uses proof operationally to mean gate-admissible evidence under a stated trust model, not semantic program correctness. We evaluate an open-source implementation through mechanism tests, a powered control-policy ablation, and operated self-application evidence. The unattended-loop engine passed 10 of 10 scenarios with zero false-DONE, and local-key receipt bundles rejected 18 tamper classes with zero false accepts. In a 9,240-cell ablation, the pre-registered A4 versus A2-prime comparison reduced visible-pass/hidden-fail amplification from 31 of 1,800 injected cells under a compute-budgeted naive loop to 2 of 1,800 under the gated loop, a 1.6 percentage-point improvement in not-amplified rate with a 95 percent confidence interval of [0.8, 2.5]. A near-compute A3 versus A4 comparison, 14 of 1,800 versus 2 of 1,800, indicates that the gain is associated with enforcing review as a lifecycle gate rather than merely adding a reviewer. The self-application corpus contains 565 stories and 1,007 review findings, with 94.8 percent resolved, plus a 68-row high/critical cross-vendor exhibit. These results support Proof-or-Stop as a model-agnostic, host-neutral control layer for deciding which autonomous-agent claims a lifecycle may act on. The evaluation is limited to one model family, 24 ablation tasks, and a self-hosted corpus.",Jek Huang; Jeffery Hsia; Jiayi Sun; Freddie Shi; Wei Huang; Ian H. White,2026-07-16,2026,arXiv,arXiv,,"48 pages, 10 figures, 29 numbered tables. Preprint v1",cs.AI,arxiv-api,,,,,,,2607.14890,2026-07-17T05:43:07+00:00
-ale-0024,Pattern Library,pattern-library,Pattern,🔁,PR babysitter,patterns/pr-babysitter.md,local_path,,"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.",Gives readers a concrete source in PR babysitter when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,436,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L436,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/pr-babysitter.md,PR babysitter,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0025,Pattern Library,pattern-library,Pattern,🔁,CI repair loop,patterns/ci-repair-loop.md,local_path,,"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.",Gives readers a concrete source in CI repair loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,437,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L437,,Design,design,Specify a loop contract and operating pattern.,escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/ci-repair-loop.md,CI repair loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0026,Pattern Library,pattern-library,Pattern,🔁,Docs drift collector,patterns/docs-drift-collector.md,local_path,,"Finds mismatches between docs and code, proposes small patches, and verifies examples.","Finds mismatches between docs and code, proposes small patches, and verifies examples.","Finds mismatches between docs and code, proposes small patches, and verifies examples.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Finds mismatches between docs and code, proposes small patches, and verifies examples.",Gives readers a concrete source in Docs drift collector when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,438,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L438,,Design,design,Specify a loop contract and operating pattern.,verification,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/docs-drift-collector.md,Docs drift collector,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0027,Pattern Library,pattern-library,Pattern,🔁,Deploy verifier,patterns/deploy-verifier.md,local_path,,"Watches rollout signals, compares them with release expectations, and stops on anomalies.","Watches rollout signals, compares them with release expectations, and stops on anomalies.","Watches rollout signals, compares them with release expectations, and stops on anomalies.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Watches rollout signals, compares them with release expectations, and stops on anomalies.",Gives readers a concrete source in Deploy verifier when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,439,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L439,,Design,design,Specify a loop contract and operating pattern.,exit,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/deploy-verifier.md,Deploy verifier,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0028,Pattern Library,pattern-library,Pattern,🔁,Feedback clusterer,patterns/feedback-clusterer.md,local_path,,"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.",Gives readers a concrete source in Feedback clusterer when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,440,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L440,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/feedback-clusterer.md,Feedback clusterer,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0029,Pattern Library,pattern-library,Pattern,🔁,Dependency triage loop,patterns/dependency-triage-loop.md,local_path,,"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.",Gives readers a concrete source in Dependency triage loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,441,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L441,,Design,design,Specify a loop contract and operating pattern.,intake;verification;escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/dependency-triage-loop.md,Dependency triage loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0030,Pattern Library,pattern-library,Pattern,🔁,Evaluation regression loop,patterns/evaluation-regression-loop.md,local_path,,"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.",Gives readers a concrete source in Evaluation regression loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,442,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L442,,Design,design,Specify a loop contract and operating pattern.,verification,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/evaluation-regression-loop.md,Evaluation regression loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0031,Pattern Library,pattern-library,Pattern,🔁,Security review loop,patterns/security-review-loop.md,local_path,,"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.",Gives readers a concrete source in Security review loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,443,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L443,,Design,design,Specify a loop contract and operating pattern.,workspace;escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/security-review-loop.md,Security review loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0032,Pattern Library,pattern-library,Pattern,🔁,Cost-control loop,patterns/cost-control-loop.md,local_path,,"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.",Gives readers a concrete source in Cost-control loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,444,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L444,,Design,design,Specify a loop contract and operating pattern.,budget,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/cost-control-loop.md,Cost-control loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0033,Pattern Library,pattern-library,Pattern,🔁,Bug hunting loop,patterns/bug-hunting-loop.md,local_path,,"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Discovers, reproduces, minimizes, and reports bugs with concrete evidence.",Gives readers a concrete source in Bug hunting loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,445,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L445,,Design,design,Specify a loop contract and operating pattern.,intake,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/bug-hunting-loop.md,Bug hunting loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0034,Pattern Library,pattern-library,Pattern,🔁,Enterprise approval loop,patterns/enterprise-approval-loop.md,local_path,,Drives a permissioned change through required gates and approvers with a full audit trail.,Drives a permissioned change through required gates and approvers with a full audit trail.,Drives a permissioned change through required gates and approvers with a full audit trail.,"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Drives a permissioned change through required gates and approvers with a full audit trail.",Gives readers a concrete source in Enterprise approval loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,446,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L446,,Design,design,Specify a loop contract and operating pattern.,escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/enterprise-approval-loop.md,Enterprise approval loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0035,Pattern Library,pattern-library,Pattern,🔁,Incident response loop,patterns/incident-response-loop.md,local_path,,"Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Triages an alert into an owned, evidence-backed incident with a postmortem seed.",Gives readers a concrete source in Incident response loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,447,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L447,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/incident-response-loop.md,Incident response loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0036,Pattern Library,pattern-library,Pattern,🔁,Data-quality loop,patterns/data-quality-loop.md,local_path,,Validates each dataset refresh against quality rules and quarantines bad versions.,Validates each dataset refresh against quality rules and quarantines bad versions.,Validates each dataset refresh against quality rules and quarantines bad versions.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Validates each dataset refresh against quality rules and quarantines bad versions.,Gives readers a concrete source in Data-quality loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,448,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L448,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/data-quality-loop.md,Data-quality loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0037,Pattern Library,pattern-library,Pattern,🔁,Release-note loop,patterns/release-note-loop.md,local_path,,"Drafts release notes from merged commits, issues, and PRs with linked evidence.","Drafts release notes from merged commits, issues, and PRs with linked evidence.","Drafts release notes from merged commits, issues, and PRs with linked evidence.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Drafts release notes from merged commits, issues, and PRs with linked evidence.",Gives readers a concrete source in Release-note loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,449,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L449,,Design,design,Specify a loop contract and operating pattern.,intake,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/release-note-loop.md,Release-note loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0038,Pattern Library,pattern-library,Pattern,🔁,Model-routing loop,patterns/model-routing-loop.md,local_path,,"Routes tasks across models on measured quality, latency, privacy, and cost.","Routes tasks across models on measured quality, latency, privacy, and cost.","Routes tasks across models on measured quality, latency, privacy, and cost.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Routes tasks across models on measured quality, latency, privacy, and cost.",Gives readers a concrete source in Model-routing loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,450,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L450,,Design,design,Specify a loop contract and operating pattern.,budget,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/model-routing-loop.md,Model-routing loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0039,Core Loop Primitives,core-loop-primitives,Docs,📚,Scheduled tasks - ChatGPT Learn,https://learn.chatgpt.com/docs/automations?surface=app,external,learn.chatgpt.com,"Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.",Gives readers a concrete source in Scheduled tasks - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,458,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L458,,Design,design,Specify a loop contract and operating pattern.,trigger;intake;workspace,builder,official-documentation,ok,https://learn.chatgpt.com/docs/automations?surface=app,Scheduled tasks | ChatGPT Learn,Schedule recurring tasks in ChatGPT,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0040,Core Loop Primitives,core-loop-primitives,Docs,📚,Follow a goal - ChatGPT Learn,https://learn.chatgpt.com/use-cases/follow-goals,external,learn.chatgpt.com,"Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.",Gives readers a concrete source in Follow a goal - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,459,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L459,,Design,design,Specify a loop contract and operating pattern.,objective;state;exit,builder,official-documentation,ok,https://learn.chatgpt.com/use-cases/follow-goals,Follow a goal | ChatGPT use cases,Use `/goal` when a task needs Codex to keep working across turns toward a verifiable stopping condition.,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0041,Core Loop Primitives,core-loop-primitives,Docs,📚,Git worktrees - ChatGPT Learn,https://learn.chatgpt.com/docs/environments/git-worktrees,external,learn.chatgpt.com,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Primary-source operational guidance rather than commentary. Resource-specific angle: Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Gives readers a concrete source in Git worktrees - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,460,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L460,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/environments/git-worktrees,Worktrees | ChatGPT Learn,Use Git worktrees in Codex in the ChatGPT desktop app to run chats in parallel,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0042,Core Loop Primitives,core-loop-primitives,Docs,📚,Prompting - ChatGPT Learn,https://learn.chatgpt.com/docs/prompting,external,learn.chatgpt.com,"Explains the Codex loop, threads, context, and goal-oriented prompting.","Explains the Codex loop, threads, context, and goal-oriented prompting.","Explains the Codex loop, threads, context, and goal-oriented prompting.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Explains the Codex loop, threads, context, and goal-oriented prompting.",Gives readers a concrete source in Prompting - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,461,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L461,,Design,design,Specify a loop contract and operating pattern.,objective;context,builder,official-documentation,ok,https://learn.chatgpt.com/docs/prompting,Prompting | ChatGPT Learn,"Write useful prompts for Chat mode, Work mode, and Codex",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0043,Core Loop Primitives,core-loop-primitives,Docs,📚,Customization overview - ChatGPT Learn,https://learn.chatgpt.com/docs/customization/overview,external,learn.chatgpt.com,"Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.",Gives readers a concrete source in Customization overview - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,462,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L462,,Design,design,Specify a loop contract and operating pattern.,context;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/customization/overview,Customization | ChatGPT Learn,"How to customize Codex with project guidance, skills, MCP, and subagents",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0044,Core Loop Primitives,core-loop-primitives,Docs,📚,Build skills - ChatGPT Learn,https://learn.chatgpt.com/docs/build-skills,external,learn.chatgpt.com,"Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.",Gives readers a concrete source in Build skills - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,463,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L463,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://learn.chatgpt.com/docs/build-skills,Build skills | ChatGPT Learn,Give Codex new capabilities and expertise,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0045,Core Loop Primitives,core-loop-primitives,Docs,📚,Plugins - ChatGPT Learn,https://learn.chatgpt.com/docs/plugins,external,learn.chatgpt.com,"Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Bundles skills, app integrations, and MCP servers into reusable loop capabilities.",Gives readers a concrete source in Plugins - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,464,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L464,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://learn.chatgpt.com/docs/plugins,Plugins | ChatGPT Learn,"Browse, install, and use plugins on supported ChatGPT and Codex surfaces",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0046,Core Loop Primitives,core-loop-primitives,Tool,🧰,dotskills,https://github.com/vincentkoc/dotskills,external,github.com,"A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.",Gives readers a concrete source in dotskills when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (95 stars; 9 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,465,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L465,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,source-implementation,ok,https://github.com/vincentkoc/dotskills,"GitHub - vincentkoc/dotskills: 🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns. · GitHub","🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns. - vincentkoc/dotskills",,2026-02-17,2026,vincentkoc/dotskills,GitHub,,,,github-api,vincentkoc/dotskills,95,9,MIT,2026-02-17T05:08:29Z,2026-07-16T23:51:40Z,,2026-07-17T05:43:07+00:00
-ale-0047,Core Loop Primitives,core-loop-primitives,Docs,📚,Developer commands - ChatGPT Learn,https://learn.chatgpt.com/docs/developer-commands?surface=cli,external,learn.chatgpt.com,"CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.",Gives readers a concrete source in Developer commands - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,466,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L466,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/developer-commands?surface=cli,Developer commands | ChatGPT Learn,Reference for commands and slash commands in Codex developer surfaces,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0048,Core Loop Primitives,core-loop-primitives,Pattern,🔁,Autonomous Loops,https://claudecodeguide.dev/docs/patterns/autonomous-loops,external,claudecodeguide.dev,"Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.",Gives readers a concrete source in Autonomous Loops when they need to turn a recurring-agent idea into an explicit loop contract.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,467,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L467,,Design,design,Specify a loop contract and operating pattern.,exit,builder,operational-pattern,ok,https://claudecodeguide.dev/docs/patterns/autonomous-loops,Claude Code Autonomous Loops | Claude Code Guide,"Point Claude Code at a problem, walk away, come back to a green build. Task templates, kill switches, and why boundaries matter more than anything else.",,,,,claudecodeguide.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0049,Core Loop Primitives,core-loop-primitives,Docs,📚,Claude Code Glossary,https://code.claude.com/docs/en/glossary.md,external,code.claude.com,"Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.",Gives readers a concrete source in Claude Code Glossary when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,468,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L468,,Design,design,Specify a loop contract and operating pattern.,delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/glossary.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0050,Core Loop Primitives,core-loop-primitives,Docs,📚,Keep Claude working toward a goal,https://code.claude.com/docs/en/goal,external,code.claude.com,`/goal` runs turn after turn until a completion condition is met by a verifier.,`/goal` runs turn after turn until a completion condition is met by a verifier.,`/goal` runs turn after turn until a completion condition is met by a verifier.,Verification is promoted from a final check to a loop-control signal. Resource-specific angle: `/goal` runs turn after turn until a completion condition is met by a verifier.,Gives readers a concrete source in Keep Claude working toward a goal when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,469,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L469,,Design,design,Specify a loop contract and operating pattern.,objective;exit,builder,official-documentation,ok,https://code.claude.com/docs/en/goal,Keep Claude working toward a goal - Claude Code Docs,Set a completion condition with /goal and Claude keeps working across turns until the condition is met.,,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0051,Core Loop Primitives,core-loop-primitives,Docs,📚,Run prompts on a schedule,https://code.claude.com/docs/en/scheduled-tasks,external,code.claude.com,"`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: `/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.",Gives readers a concrete source in Run prompts on a schedule when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,470,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L470,,Design,design,Specify a loop contract and operating pattern.,trigger;workspace,builder,official-documentation,ok,https://code.claude.com/docs/en/scheduled-tasks,Run prompts on a schedule - Claude Code Docs,"Use /loop and the cron scheduling tools to run prompts repeatedly, poll for status, or set one-time reminders within a Claude Code session.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0052,Core Loop Primitives,core-loop-primitives,Docs,📚,Automate work with routines,https://code.claude.com/docs/en/routines,external,code.claude.com,"Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.",Gives readers a concrete source in Automate work with routines when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,471,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L471,,Design,design,Specify a loop contract and operating pattern.,trigger;state,builder,official-documentation,ok,https://code.claude.com/docs/en/routines,Automate work with routines - Claude Code Docs,"Put Claude Code on autopilot. Define routines that run on a schedule, trigger on API calls, or react to GitHub events from Anthropic-managed cloud infrastructure.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0053,Core Loop Primitives,core-loop-primitives,Docs,📚,Desktop scheduled tasks,https://code.claude.com/docs/en/desktop-scheduled-tasks,external,code.claude.com,"Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.",Gives readers a concrete source in Desktop scheduled tasks when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,472,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L472,,Design,design,Specify a loop contract and operating pattern.,trigger;workspace;state,builder,official-documentation,ok,https://code.claude.com/docs/en/desktop-scheduled-tasks,Schedule recurring tasks in Claude Code Desktop - Claude Code Docs,"Set up scheduled tasks in Claude Code Desktop to run Claude automatically on a recurring basis for daily code reviews, dependency audits, or morning briefings.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0054,Core Loop Primitives,core-loop-primitives,Docs,📚,Run parallel sessions with worktrees,https://code.claude.com/docs/en/worktrees,external,code.claude.com,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,"Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.",Gives readers a concrete source in Run parallel sessions with worktrees when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,473,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L473,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/worktrees,Run parallel sessions with worktrees - Claude Code Docs,"Isolate parallel Claude Code sessions in separate git worktrees so changes don't collide. Covers the --worktree flag, subagent isolation, .worktreeinclude, cleanup, and non-git VCS hooks.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0055,Core Loop Primitives,core-loop-primitives,Docs,📚,Automate actions with hooks,https://code.claude.com/docs/en/hooks-guide,external,code.claude.com,Claude Code hooks guide for deterministic lifecycle control around model actions.,Claude Code hooks guide for deterministic lifecycle control around model actions.,Claude Code hooks guide for deterministic lifecycle control around model actions.,The resource is directly reusable as a starting artifact. Resource-specific angle: Claude Code hooks guide for deterministic lifecycle control around model actions.,Gives readers a concrete source in Automate actions with hooks when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,474,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L474,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/hooks-guide,Automate actions with hooks - Claude Code Docs,"Run shell commands automatically when Claude Code edits files, finishes tasks, or needs input. Format code, send notifications, validate commands, and enforce project rules.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0056,Core Loop Primitives,core-loop-primitives,Docs,📚,Hooks reference,https://code.claude.com/docs/en/hooks.md,external,code.claude.com,"Event-level reference for session, turn, tool-call, and subagent hooks.","Event-level reference for session, turn, tool-call, and subagent hooks.","Event-level reference for session, turn, tool-call, and subagent hooks.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Event-level reference for session, turn, tool-call, and subagent hooks.",Gives readers a concrete source in Hooks reference when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,475,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L475,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/hooks.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0057,Core Loop Primitives,core-loop-primitives,Docs,📚,Common workflows - Claude Code,https://code.claude.com/docs/en/common-workflows,external,code.claude.com,"Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.",Gives readers a concrete source in Common workflows - Claude Code when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,476,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L476,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/common-workflows,Common workflows - Claude Code Docs,"Step-by-step guides for exploring codebases, fixing bugs, refactoring, testing, and other everyday tasks with Claude Code.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0058,Core Loop Primitives,core-loop-primitives,Docs,📚,Manage multiple agents with agent view,https://code.claude.com/docs/en/agent-view.md,external,code.claude.com,"Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Dashboard for dispatching, monitoring, and attaching to background agent sessions.",Gives readers a concrete source in Manage multiple agents with agent view when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,477,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L477,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-view.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0059,Core Loop Primitives,core-loop-primitives,Docs,📚,Run agents in parallel,https://code.claude.com/docs/en/agents.md,external,code.claude.com,"Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.",Gives readers a concrete source in Run agents in parallel when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,478,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L478,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/agents.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0060,Core Loop Primitives,core-loop-primitives,Docs,📚,Orchestrate subagents at scale with dynamic workflows,https://code.claude.com/docs/en/workflows,external,code.claude.com,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Gives readers a concrete source in Orchestrate subagents at scale with dynamic workflows when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,479,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L479,,Design,design,Specify a loop contract and operating pattern.,context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/workflows,Orchestrate subagents at scale with dynamic workflows - Claude Code Docs,"Dynamic workflows orchestrate many subagents from a script Claude writes and you can rerun. Use them for codebase audits, large migrations, and cross-checked research.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0061,Core Loop Primitives,core-loop-primitives,Docs,📚,Create plugins,https://code.claude.com/docs/en/plugins,external,code.claude.com,"Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.",Gives readers a concrete source in Create plugins when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,480,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L480,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/plugins,Create plugins - Claude Code Docs,"Create custom plugins to extend Claude Code with skills, agents, hooks, and MCP servers.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0062,Core Loop Primitives,core-loop-primitives,Docs,📚,Model Context Protocol,https://modelcontextprotocol.io/docs/getting-started/intro,external,modelcontextprotocol.io,Standard protocol for exposing tools and data sources to agent loops.,Standard protocol for exposing tools and data sources to agent loops.,Standard protocol for exposing tools and data sources to agent loops.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Standard protocol for exposing tools and data sources to agent loops.,Gives readers a concrete source in Model Context Protocol when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from modelcontextprotocol.io; use it for current product or standard behavior.,high,README.md,481,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L481,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,official-documentation,ok,https://modelcontextprotocol.io/docs/getting-started/intro,What is the Model Context Protocol (MCP)? - Model Context Protocol,,,,,,Model Context Protocol,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0063,Core Loop Primitives,core-loop-primitives,Docs,📚,Allowing GitHub Copilot CLI to work autonomously,https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot,external,docs.github.com,"Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.",Gives readers a concrete source in Allowing GitHub Copilot CLI to work autonomously when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from docs.github.com; use it for current product or standard behavior.,high,README.md,482,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L482,,Design,design,Specify a loop contract and operating pattern.,trigger,builder,official-documentation,ok,https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot,Allowing GitHub Copilot CLI to work autonomously - GitHub Docs,"The CLI's autopilot mode lets Copilot CLI work autonomously on a task, carrying out multiple steps until the task is complete.",,,,,GitHub Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0064,Core Loop Primitives,core-loop-primitives,Tool,🧰,opencode-scheduler,https://github.com/different-ai/opencode-scheduler,external,github.com,"OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.",Gives readers a concrete source in opencode-scheduler when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (441 stars; 30 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,483,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L483,,Design,design,Specify a loop contract and operating pattern.,budget,builder,source-implementation,ok,https://github.com/different-ai/opencode-scheduler,GitHub - different-ai/opencode-scheduler: OpenCode plugin for scheduling recurring jobs using launchd (Mac) or systemd (Linux) · GitHub,OpenCode plugin for scheduling recurring jobs using launchd (Mac) or systemd (Linux) - different-ai/opencode-scheduler,,2026-01-04,2026,different-ai/opencode-scheduler,GitHub,,,,github-api,different-ai/opencode-scheduler,441,30,MIT,2026-01-04T03:04:58Z,2026-07-17T02:28:49Z,,2026-07-17T05:43:07+00:00
-ale-0065,Core Loop Primitives,core-loop-primitives,Tool,🧰,Agent-Loop-Skills,https://github.com/gaasher/Agent-Loop-Skills,external,github.com,"Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.",Gives readers a concrete source in Agent-Loop-Skills when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (133 stars; 15 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,484,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L484,,Design,design,Specify a loop contract and operating pattern.,verification;exit,builder,source-implementation,ok,https://github.com/gaasher/Agent-Loop-Skills,"GitHub - gaasher/Agent-Loop-Skills: Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts. · GitHub","Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts. - gaasher/Agent-Loop-Skills",,2026-06-15,2026,gaasher/Agent-Loop-Skills,GitHub,,,,github-api,gaasher/Agent-Loop-Skills,133,15,MIT,2026-06-15T02:01:33Z,2026-07-17T01:28:31Z,,2026-07-17T05:43:07+00:00
-ale-0066,Core Loop Primitives,core-loop-primitives,Tool,🧰,launch-your-agent,https://github.com/anthropics/launch-your-agent,external,github.com,"Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Primary-source operational guidance rather than commentary. Resource-specific angle: Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.",Gives readers a concrete source in launch-your-agent when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (808 stars; 154 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,485,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L485,,Design,design,Specify a loop contract and operating pattern.,trigger;verification;exit,builder,source-implementation,ok,https://github.com/anthropics/launch-your-agent,"GitHub - anthropics/launch-your-agent: Claude Code skills that take a founder from idea to a live Claude Managed Agent: interview, scope a v0, launch in their own account, grade it, iterate, and schedule it · GitHub","Claude Code skills that take a founder from idea to a live Claude Managed Agent: interview, scope a v0, launch in their own account, grade it, iterate, and schedule it - anthropics/launch-your-agent",,2026-06-16,2026,anthropics/launch-your-agent,GitHub,,,,github-api,anthropics/launch-your-agent,808,154,Apache-2.0,2026-06-16T14:49:50Z,2026-07-17T03:04:34Z,,2026-07-17T05:43:07+00:00
-ale-0067,Official Runtime Guides,official-runtime-guides,Docs,📚,Run long horizon tasks with Codex,https://developers.openai.com/blog/run-long-horizon-tasks-with-codex,external,developers.openai.com,"OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.",Gives readers a concrete source in Run long horizon tasks with Codex when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,498,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L498,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,official-documentation,ok,https://developers.openai.com/blog/run-long-horizon-tasks-with-codex,Run long horizon tasks with Codex | OpenAI Developers,OpenAI Developer Blog,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0068,Official Runtime Guides,official-runtime-guides,Docs,📚,Best practices - ChatGPT Learn,https://learn.chatgpt.com/guides/best-practices,external,learn.chatgpt.com,"Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.",Gives readers a concrete source in Best practices - ChatGPT Learn when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,499,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L499,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/guides/best-practices,Best practices | ChatGPT Learn,Getting started with Codex and proven practices for better results,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0069,Official Runtime Guides,official-runtime-guides,Docs,📚,Agents SDK,https://developers.openai.com/api/docs/guides/agents,external,developers.openai.com,"OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.",Gives readers a concrete source in Agents SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,500,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L500,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;state;escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents,Agents SDK | OpenAI API,Learn how the OpenAI Agents SDK fits together and which docs to read next.,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0070,Official Runtime Guides,official-runtime-guides,Docs,📚,Agents - OpenAI Agents SDK,https://openai.github.io/openai-agents-python/agents/,external,openai.github.io,"SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.",Gives readers a concrete source in Agents - OpenAI Agents SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from openai.github.io; use it for current product or standard behavior.,high,README.md,501,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L501,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,official-documentation,ok,https://openai.github.io/openai-agents-python/agents/,Agents - OpenAI Agents SDK,,,,,,openai.github.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0071,Official Runtime Guides,official-runtime-guides,Docs,📚,Running agents,https://developers.openai.com/api/docs/guides/agents/running-agents,external,developers.openai.com,"OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.",Gives readers a concrete source in Running agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,502,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L502,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/running-agents,Running agents | OpenAI API,"Learn how to run agents, stream output, and choose the right conversation-state strategy in the OpenAI Agents SDK.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0072,Official Runtime Guides,official-runtime-guides,Docs,📚,Integrations and observability,https://developers.openai.com/api/docs/guides/agents/integrations-observability,external,developers.openai.com,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,Gives readers a concrete source in Integrations and observability when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,503,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L503,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/integrations-observability,Integrations and observability | OpenAI API,Learn how to integrate MCP into Agents SDK workflows and how to trace and debug runs.,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0073,Official Runtime Guides,official-runtime-guides,Docs,📚,Sandbox Agents,https://developers.openai.com/api/docs/guides/agents/sandboxes,external,developers.openai.com,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Gives readers a concrete source in Sandbox Agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,504,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L504,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/sandboxes,Sandbox Agents | OpenAI API,"Learn how sandboxes fit into Agents SDK workflows, when to use them, and how orchestration stays separate from execution.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0074,Official Runtime Guides,official-runtime-guides,Docs,📚,Guardrails and human review,https://developers.openai.com/api/docs/guides/agents/guardrails-approvals,external,developers.openai.com,Approval and validation boundaries for sensitive agent actions.,Approval and validation boundaries for sensitive agent actions.,Approval and validation boundaries for sensitive agent actions.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Approval and validation boundaries for sensitive agent actions.",Gives readers a concrete source in Guardrails and human review when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,505,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L505,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/guardrails-approvals,Guardrails and human review | OpenAI API,"Learn how to use guardrails and human review in the OpenAI Agents SDK for safer, more controlled workflows.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0075,Official Runtime Guides,official-runtime-guides,Blog,📝,ChatGPT Work and the Codex Desktop App,https://openai.com/index/chatgpt-for-your-most-ambitious-work/,external,openai.com,"OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.",Gives readers a concrete source in ChatGPT Work and the Codex Desktop App when they need to choose an implementation surface for repeatable agent work.,"Contextual source from openai.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,506,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L506,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective,builder,practitioner-analysis,restricted,https://openai.com/index/chatgpt-for-your-most-ambitious-work/,,,,,,,OpenAI,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0076,Official Runtime Guides,official-runtime-guides,Docs,📚,Building agents with the Claude Agent SDK,https://code.claude.com/docs/en/agent-sdk/overview.md,external,code.claude.com,"Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.",Gives readers a concrete source in Building agents with the Claude Agent SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,511,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L511,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-sdk/overview.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0077,Official Runtime Guides,official-runtime-guides,Docs,📚,How the agent loop works,https://code.claude.com/docs/en/agent-sdk/agent-loop,external,code.claude.com,Official walkthrough of the inner agent loop that outer recurring loops build on.,Official walkthrough of the inner agent loop that outer recurring loops build on.,Official walkthrough of the inner agent loop that outer recurring loops build on.,Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of the inner agent loop that outer recurring loops build on.,Gives readers a concrete source in How the agent loop works when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,512,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L512,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-sdk/agent-loop,How the agent loop works - Claude Code Docs,"Understand the message lifecycle, tool execution, context window, and architecture that power your SDK agents.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0078,Official Runtime Guides,official-runtime-guides,Docs,📚,Extend Claude with skills,https://code.claude.com/docs/en/skills,external,code.claude.com,Claude Code skill system for reusable loop instructions and assets.,Claude Code skill system for reusable loop instructions and assets.,Claude Code skill system for reusable loop instructions and assets.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Claude Code skill system for reusable loop instructions and assets.",Gives readers a concrete source in Extend Claude with skills when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,513,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L513,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/skills,Extend Claude with skills - Claude Code Docs,"Create, manage, and share skills to extend Claude's capabilities in Claude Code. Includes custom commands and bundled skills.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0079,Official Runtime Guides,official-runtime-guides,Docs,📚,Create custom subagents,https://code.claude.com/docs/en/sub-agents,external,code.claude.com,"Claude Code custom subagents with isolated context, model choice, and tool permissions.","Claude Code custom subagents with isolated context, model choice, and tool permissions.","Claude Code custom subagents with isolated context, model choice, and tool permissions.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Claude Code custom subagents with isolated context, model choice, and tool permissions.",Gives readers a concrete source in Create custom subagents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,514,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L514,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/sub-agents,Create custom subagents - Claude Code Docs,Create and use specialized AI subagents in Claude Code for task-specific workflows and improved context management.,,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0080,Official Runtime Guides,official-runtime-guides,Docs,📚,Writing effective tools for AI agents,https://www.anthropic.com/engineering/writing-tools-for-agents,external,www.anthropic.com,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.",Gives readers a concrete source in Writing effective tools for AI agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,515,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L515,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,official-documentation,ok,https://www.anthropic.com/engineering/writing-tools-for-agents,Writing effective tools for AI agents—using AI agents \ Anthropic,Writing effective tools for AI agents—using AI agents,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0081,Official Runtime Guides,official-runtime-guides,Docs,📚,Introducing advanced tool use on the Claude Developer Platform,https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3,external,www.anthropic.com,"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.",Gives readers a concrete source in Introducing advanced tool use on the Claude Developer Platform when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,516,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L516,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context,builder,official-documentation,ok,https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3,Introducing advanced tool use on the Claude Developer Platform \ Anthropic,"Claude can now discover, learn, and execute tools dynamically to enable agents that take action in the real world. Here’s how.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0082,Official Runtime Guides,official-runtime-guides,Docs,📚,Effective harnesses for long-running agents,https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents,external,www.anthropic.com,"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.",Gives readers a concrete source in Effective harnesses for long-running agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,517,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L517,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,official-documentation,ok,https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents,Effective harnesses for long-running agents \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0083,Official Runtime Guides,official-runtime-guides,Docs,📚,Claude Code best practices,https://code.claude.com/docs/en/best-practices,external,code.claude.com,Widely cited workflow guidance that underlies many recurring Claude Code loops.,Widely cited workflow guidance that underlies many recurring Claude Code loops.,Widely cited workflow guidance that underlies many recurring Claude Code loops.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Widely cited workflow guidance that underlies many recurring Claude Code loops.",Gives readers a concrete source in Claude Code best practices when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,518,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L518,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/best-practices,Best practices for Claude Code - Claude Code Docs,"Tips and patterns for getting the most out of Claude Code, from configuring your environment to scaling across parallel sessions.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0084,Official Runtime Guides,official-runtime-guides,Docs,📚,Claude Managed Agents: Scheduled Deployments and Vaults,https://claude.com/blog/whats-new-in-claude-managed-agents,external,claude.com,"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.",Gives readers a concrete source in Claude Managed Agents: Scheduled Deployments and Vaults when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from claude.com; use it for current product or standard behavior.,high,README.md,519,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L519,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,official-documentation,ok,https://claude.com/blog/whats-new-in-claude-managed-agents,New in Claude Managed Agents: run agents on a schedule and store environment variables in vaults | Claude by Anthropic,Claude Managed Agents now supports scheduled deployments and vaults: run agents on a cron schedule and securely authenticate CLI tools and other services.,,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0085,Official Runtime Guides,official-runtime-guides,Blog,📝,Getting Started with Loops,https://claude.com/blog/getting-started-with-loops,external,claude.com,"Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.",Gives readers a concrete source in Getting Started with Loops when they need to choose an implementation surface for repeatable agent work.,"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,520,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L520,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;trigger;verification;budget;exit,builder,practitioner-analysis,ok,https://claude.com/blog/getting-started-with-loops,Loop engineering: Getting started with loops | Claude by Anthropic,"Loop engineering with Anthropic's Claude Code: design turn-based, goal, time, and proactive agent loops that run to a stop condition.",,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0086,Official Runtime Guides,official-runtime-guides,Docs,📚,"Claude Code What's New, Week 28",https://code.claude.com/docs/en/whats-new/2026-w28,external,code.claude.com,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.","Gives readers a concrete source in Claude Code What's New, Week 28 when they need to choose an implementation surface for repeatable agent work.",Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,521,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L521,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/whats-new/2026-w28,"Week 28 · July 6–10, 2026 - Claude Code Docs","Browse external sites from the Desktop app's built-in browser, run a full setup checkup with /doctor, and pick up auto mode transcript protections and agent view upgrades.",,,2026,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0087,Official Runtime Guides,official-runtime-guides,Docs,📚,GitHub Agentic Workflows,https://github.github.com/gh-aw/,external,github.github.com,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.",Gives readers a concrete source in GitHub Agentic Workflows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.github.com; use it for current product or standard behavior.,high,README.md,526,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L526,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://github.github.com/gh-aw/,Home | GitHub Agentic Workflows,Write repository automation workflows in natural language using markdown files and run them as GitHub Actions. Use AI agents with strong guardrails to automate your development workflow.,,,,,GitHub Agentic Workflows,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0088,Official Runtime Guides,official-runtime-guides,Docs,📚,Continuous AI,https://githubnext.com/projects/continuous-ai/,external,githubnext.com,"GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.",Gives readers a concrete source in Continuous AI when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from githubnext.com; use it for current product or standard behavior.,high,README.md,527,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L527,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://githubnext.com/projects/continuous-ai/,Continuous AI,Exploring LLM-powered automation in platform-based software collaboration,,,,,githubnext.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0089,Official Runtime Guides,official-runtime-guides,Blog,📝,Automate repository tasks with GitHub Agentic Workflows,https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/,external,github.blog,"Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).",Gives readers a concrete source in Automate repository tasks with GitHub Agentic Workflows when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,528,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L528,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,practitioner-analysis,ok,https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/,Automate repository tasks with GitHub Agentic Workflows - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.","Don Syme, Peli de Halleux",2026-02-13,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0090,Official Runtime Guides,official-runtime-guides,Blog,📝,Continuous AI in practice: What developers can automate today with agentic CI,https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/,external,github.blog,"Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.",Gives readers a concrete source in Continuous AI in practice: What developers can automate today with agentic CI when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,529,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L529,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,practitioner-analysis,ok,https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/,Continuous AI in practice: What developers can automate today with agentic CI - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning.,GitHub Staff,2026-02-05,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0091,Official Runtime Guides,official-runtime-guides,Docs,📚,About GitHub Copilot coding agent,https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent,external,docs.github.com,"GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.",Gives readers a concrete source in About GitHub Copilot coding agent when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.github.com; use it for current product or standard behavior.,high,README.md,530,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L530,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;workspace,builder,official-documentation,ok,https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-cloud-agent,About GitHub Copilot cloud agent - GitHub Docs,"Copilot can research a repository, create an implementation plan, and make code changes on a branch. You can review the diff, iterate, and create a pull request when you're ready.",,,,,GitHub Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0092,Official Runtime Guides,official-runtime-guides,Blog,📝,GitHub Copilot: Meet the new coding agent,https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/,external,github.blog,"Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.",Gives readers a concrete source in GitHub Copilot: Meet the new coding agent when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,531,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L531,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,practitioner-analysis,ok,https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/,GitHub Copilot: Meet the new coding agent - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Copilot has a new feature: a coding agent that can implement a task or issue, run in the background with GitHub Actions, and more.",Thomas Dohmke,2025-05-19,2025,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0093,Official Runtime Guides,official-runtime-guides,Docs,📚,GitHub Copilot for Jira Is Now Generally Available,https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/,external,github.blog,"General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.",Gives readers a concrete source in GitHub Copilot for Jira Is Now Generally Available when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,532,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L532,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,official-documentation,ok,https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/,GitHub Copilot for Jira is now generally available - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Copilot for Jira is now generally available. Since launching the public preview in March 2026, we have shipped a series of enhancements based on your feedback, including model selection,…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0094,Official Runtime Guides,official-runtime-guides,Docs,📚,Copilot Agent Session Streaming (Public Preview),https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/,external,github.blog,"Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.",Gives readers a concrete source in Copilot Agent Session Streaming (Public Preview) when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,533,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L533,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,official-documentation,ok,https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/,Copilot agent session streaming is now in public preview - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Enterprise Cloud customers with enterprise managed users can now access GitHub Copilot agent session data across all Copilot clients, including: Cloud agents operating on github.com and data resident deployments…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0095,Official Runtime Guides,official-runtime-guides,Docs,📚,Security Reviews in the GitHub Copilot App,https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app,external,github.blog,"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.",Gives readers a concrete source in Security Reviews in the GitHub Copilot App when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,534,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L534,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app/,Security reviews now available in the GitHub Copilot app - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"You can now run a security review on your in-flight code changes directly from the GitHub Copilot app. The /security-review slash command is shipping in public preview, bringing the same…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0096,Official Runtime Guides,official-runtime-guides,Docs,📚,Cursor cloud agents,https://cursor.com/docs/cloud-agent,external,cursor.com,Remote agents that work asynchronously in isolated environments and hand results back for review.,Remote agents that work asynchronously in isolated environments and hand results back for review.,Remote agents that work asynchronously in isolated environments and hand results back for review.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Remote agents that work asynchronously in isolated environments and hand results back for review.",Gives readers a concrete source in Cursor cloud agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,539,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L539,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://cursor.com/docs/cloud-agent,Cloud Agents | Cursor Docs,Run Agent in the cloud for continuous coding assistance.,,,,,Cursor Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0097,Official Runtime Guides,official-runtime-guides,Docs,📚,Cursor 3.8: Improvements to Cursor Automations,https://cursor.com/changelog/06-18-26,external,cursor.com,"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.",Gives readers a concrete source in Cursor 3.8: Improvements to Cursor Automations when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,540,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L540,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,official-documentation,ok,https://cursor.com/changelog/06-18-26,Improvements to Cursor Automations · Cursor,,,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0098,Official Runtime Guides,official-runtime-guides,Blog,📝,Expanding Our Long-Running Agents Research Preview,https://cursor.com/blog/long-running-agents,external,cursor.com,"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.",Gives readers a concrete source in Expanding Our Long-Running Agents Research Preview when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,541,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L541,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,practitioner-analysis,ok,https://cursor.com/blog/long-running-agents,Expanding our long-running agents research preview · Cursor,"Long-running agents are now available in the Cursor web app for Ultra, Teams, and Enterprise users.",Cursor Team,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0099,Official Runtime Guides,official-runtime-guides,Docs,📚,"Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks",https://cursor.com/changelog/side-chat,external,cursor.com,"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Gives readers a concrete source in Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks when they need to choose an implementation surface for repeatable agent work.",Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,542,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L542,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;exit,builder,official-documentation,ok,https://cursor.com/changelog/side-chat,Side Chats and Conversation Search · Cursor,,,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0100,Official Runtime Guides,official-runtime-guides,Docs,📚,Jules,https://jules.google/docs,external,jules.google,"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.",Gives readers a concrete source in Jules when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from jules.google; use it for current product or standard behavior.,high,README.md,547,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L547,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://jules.google/docs,Getting started | Jules,Set up and run your first task with Jules,,,,,Jules,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0101,Official Runtime Guides,official-runtime-guides,Docs,📚,Devin Docs,https://docs.devin.ai/get-started/devin-intro,external,docs.devin.ai,"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.",Gives readers a concrete source in Devin Docs when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.devin.ai; use it for current product or standard behavior.,high,README.md,548,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L548,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://docs.devin.ai/get-started/devin-intro,Introducing Devin - Devin Docs,"Devin is the AI software engineer, built to help ambitious engineering teams crush their backlogs.",,,,,Devin Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0102,Official Runtime Guides,official-runtime-guides,Blog,📝,"Amp: Agents, Anywhere",https://ampcode.com/news/agents-anywhere,external,ampcode.com,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","Gives readers a concrete source in Amp: Agents, Anywhere when they need to choose an implementation surface for repeatable agent work.","Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,549,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L549,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,practitioner-analysis,ok,https://ampcode.com/news/agents-anywhere,Amp,Remotely start agents anywhere you can run 'amp',,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0103,Research Foundations,research-foundations,Paper,📄,ReAct: Synergizing Reasoning and Acting in Language Models,https://arxiv.org/abs/2210.03629,external,arxiv.org,Foundational reason-act-observe loop for tool-using language agents.,Foundational reason-act-observe loop for tool-using language agents.,Foundational reason-act-observe loop for tool-using language agents.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Foundational reason-act-observe loop for tool-using language agents.,"Gives readers a concrete source in ReAct: Synergizing Reasoning and Acting in Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2210.03629; inspect its method and evaluation before treating results as production evidence.,medium,README.md,557,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L557,,Learn,learn,Understand the field and its boundaries.,workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2210.03629,[2210.03629] ReAct: Synergizing Reasoning and Acting in Language Models,"While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io",Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du; Izhak Shafran; Karthik Narasimhan; Yuan Cao,2022-10-06,2022,arXiv,arXiv,,v3 is the ICLR camera ready version with some typos fixed. Project site with code: https://react-lm.github.io,cs.CL,arxiv-api,,,,,,,2210.03629,2026-07-17T05:43:07+00:00
-ale-0104,Research Foundations,research-foundations,Paper,📄,Reflexion: Language Agents with Verbal Reinforcement Learning,https://arxiv.org/abs/2303.11366,external,arxiv.org,Converts environment feedback into written reflections stored in memory for future attempts.,Converts environment feedback into written reflections stored in memory for future attempts.,Converts environment feedback into written reflections stored in memory for future attempts.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Converts environment feedback into written reflections stored in memory for future attempts.,"Gives readers a concrete source in Reflexion: Language Agents with Verbal Reinforcement Learning when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2303.11366; inspect its method and evaluation before treating results as production evidence.,medium,README.md,558,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L558,,Learn,learn,Understand the field and its boundaries.,context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2303.11366,[2303.11366] Reflexion: Language Agents with Verbal Reinforcement Learning,"Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning. We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback. Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials. Reflexion is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals, and obtains significant improvements over a baseline agent across diverse tasks (sequential decision-making, coding, language reasoning). For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%. We also conduct ablation and analysis studies using different feedback signals, feedback incorporation methods, and agent types, and provide insights into how they affect performance.",Noah Shinn; Federico Cassano; Edward Berman; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao,2023-03-20,2023,arXiv,arXiv,,v4 contains a few additional experiments,cs.AI,arxiv-api,,,,,,,2303.11366,2026-07-17T05:43:07+00:00
-ale-0105,Research Foundations,research-foundations,Paper,📄,Self-Refine: Iterative Refinement with Self-Feedback,https://arxiv.org/abs/2303.17651,external,arxiv.org,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Generate-feedback-refine loop where a model improves outputs over repeated passes.,"Gives readers a concrete source in Self-Refine: Iterative Refinement with Self-Feedback when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2303.17651; inspect its method and evaluation before treating results as production evidence.,medium,README.md,559,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L559,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2303.17651,[2303.17651] Self-Refine: Iterative Refinement with Self-Feedback,"Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses it to refine itself, iteratively. Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs. Across all evaluated tasks, outputs generated with Self-Refine are preferred by humans and automatic metrics over those generated with the same LLM using conventional one-step generation, improving by ~20% absolute on average in task performance. Our work demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using our simple, standalone approach.",Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark,2023-03-30,2023,arXiv,arXiv,,"Code, data, and demo at https://selfrefine.info/",cs.CL,arxiv-api,,,,,,,2303.17651,2026-07-17T05:43:07+00:00
-ale-0106,Research Foundations,research-foundations,Paper,📄,CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,https://arxiv.org/abs/2305.11738,external,arxiv.org,Uses tools to ground critique and correction rather than relying only on introspection.,Uses tools to ground critique and correction rather than relying only on introspection.,Uses tools to ground critique and correction rather than relying only on introspection.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Uses tools to ground critique and correction rather than relying only on introspection.,"Gives readers a concrete source in CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.11738; inspect its method and evaluation before treating results as production evidence.,medium,README.md,560,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L560,,Learn,learn,Understand the field and its boundaries.,workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.11738,[2305.11738] CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,"Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially ""black boxes"" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.",Zhibin Gou; Zhihong Shao; Yeyun Gong; Yelong Shen; Yujiu Yang; Nan Duan; Weizhu Chen,2023-05-19,2023,arXiv,arXiv,,ICLR 2024,cs.CL,arxiv-api,,,,,,,2305.11738,2026-07-17T05:43:07+00:00
-ale-0107,Research Foundations,research-foundations,Paper,📄,Tree of Thoughts,https://arxiv.org/abs/2305.10601,external,arxiv.org,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,"Gives readers a concrete source in Tree of Thoughts when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.10601; inspect its method and evaluation before treating results as production evidence.,medium,README.md,561,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L561,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.10601,[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models,"Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.",Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Thomas L. Griffiths; Yuan Cao; Karthik Narasimhan,2023-05-17,2023,arXiv,arXiv,,NeurIPS 2023 camera ready version. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm,cs.CL,arxiv-api,,,,,,,2305.10601,2026-07-17T05:43:07+00:00
-ale-0108,Research Foundations,research-foundations,Paper,📄,Graph of Thoughts,https://arxiv.org/abs/2308.09687,external,arxiv.org,"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Gives readers a concrete source in Graph of Thoughts when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2308.09687; inspect its method and evaluation before treating results as production evidence.,medium,README.md,562,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L562,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2308.09687,[2308.09687] Graph of Thoughts: Solving Elaborate Problems with Large Language Models,"We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (""LLM thoughts"") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.",Maciej Besta; Nils Blach; Ales Kubicek; Robert Gerstenberger; Michal Podstawski; Lukas Gianinazzi; Joanna Gajda; Tomasz Lehmann; Hubert Niewiadomski; Piotr Nyczyk; Torsten Hoefler,2023-08-18,2023,Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI'24),arXiv,10.1609/aaai.v38i16.29720,,cs.CL,arxiv-api,,,,,,,2308.09687,2026-07-17T05:43:07+00:00
-ale-0109,Research Foundations,research-foundations,Paper,📄,Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models,https://arxiv.org/abs/2310.04406,external,arxiv.org,"Combines search, action, and environment feedback for language agents.","Combines search, action, and environment feedback for language agents.","Combines search, action, and environment feedback for language agents.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Combines search, action, and environment feedback for language agents.","Gives readers a concrete source in Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2310.04406; inspect its method and evaluation before treating results as production evidence.,medium,README.md,563,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L563,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2310.04406,[2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models,"While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leveraging the in-context learning ability of LMs, we integrate Monte Carlo Tree Search into LATS to enable LMs as agents, along with LM-powered value functions and self-reflections for proficient exploration and enhanced decision-making. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that surpasses the constraints of existing techniques. Our experimental evaluation across diverse domains, including programming, interactive question-answering (QA), web navigation, and math, validates the effectiveness and generality of LATS in decision-making while maintaining competitive or improved reasoning performance. Notably, LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5. Code can be found at https://github.com/lapisrocks/LanguageAgentTreeSearch",Andy Zhou; Kai Yan; Michal Shlapentokh-Rothman; Haohan Wang; Yu-Xiong Wang,2023-10-06,2023,arXiv,arXiv,,Code at https://github.com/lapisrocks/LanguageAgentTreeSearch,cs.AI,arxiv-api,,,,,,,2310.04406,2026-07-17T05:43:07+00:00
-ale-0110,Research Foundations,research-foundations,Paper,📄,Voyager: An Open-Ended Embodied Agent with Large Language Models,https://arxiv.org/abs/2305.16291,external,arxiv.org,"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Gives readers a concrete source in Voyager: An Open-Ended Embodied Agent with Large Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.16291; inspect its method and evaluation before treating results as production evidence.,medium,README.md,564,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L564,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.16291,[2305.16291] Voyager: An Open-Ended Embodied Agent with Large Language Models,"We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.",Guanzhi Wang; Yuqi Xie; Yunfan Jiang; Ajay Mandlekar; Chaowei Xiao; Yuke Zhu; Linxi Fan; Anima Anandkumar,2023-05-25,2023,arXiv,arXiv,,Project website and open-source codebase: https://voyager.minedojo.org/,cs.AI,arxiv-api,,,,,,,2305.16291,2026-07-17T05:43:07+00:00
-ale-0111,Research Foundations,research-foundations,Paper,📄,Generative Agents: Interactive Simulacra of Human Behavior,https://arxiv.org/abs/2304.03442,external,arxiv.org,Introduces reflection and memory mechanisms for long-running agent behavior.,Introduces reflection and memory mechanisms for long-running agent behavior.,Introduces reflection and memory mechanisms for long-running agent behavior.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Introduces reflection and memory mechanisms for long-running agent behavior.,"Gives readers a concrete source in Generative Agents: Interactive Simulacra of Human Behavior when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2304.03442; inspect its method and evaluation before treating results as production evidence.,medium,README.md,565,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L565,,Learn,learn,Understand the field and its boundaries.,context;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2304.03442,[2304.03442] Generative Agents: Interactive Simulacra of Human Behavior,"Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.",Joon Sung Park; Joseph C. O'Brien; Carrie J. Cai; Meredith Ringel Morris; Percy Liang; Michael S. Bernstein,2023-04-07,2023,arXiv,arXiv,,,cs.HC,arxiv-api,,,,,,,2304.03442,2026-07-17T05:43:07+00:00
-ale-0112,Research Foundations,research-foundations,Paper,📄,Measuring AI Ability to Complete Long Software Tasks,https://arxiv.org/abs/2503.14499,external,arxiv.org,"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","Gives readers a concrete source in Measuring AI Ability to Complete Long Software Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2503.14499; inspect its method and evaluation before treating results as production evidence.,medium,README.md,566,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L566,,Learn,learn,Understand the field and its boundaries.,state;budget;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2503.14499,[2503.14499] Measuring AI Ability to Complete Long Software Tasks,"Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can complete with 50% success rate. We first timed humans with relevant domain expertise on a combination of RE-Bench, HCAST, and 66 novel shorter tasks. On these tasks, current frontier AI models such as Claude 3.7 Sonnet have a 50% time horizon of around 50 minutes. Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024. The increase in AI models' time horizons seems to be primarily driven by greater reliability and ability to adapt to mistakes, combined with better logical reasoning and tool use capabilities. We discuss the limitations of our results -- including their degree of external validity -- and the implications of increased autonomy for dangerous capabilities. If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month.",Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney Von Arx; Ryan Bloom; Thomas Broadley; Haoxing Du; Brian Goodrich; Nikola Jurkovic; Luke Harold Miles; Seraphina Nix; Tao Lin; Chris Painter; Neev Parikh; David Rein; Lucas Jun Koba Sato; Hjalmar Wijk; Daniel M. Ziegler; Elizabeth Barnes; Lawrence Chan,2025-03-18,2025,NeurIPS 2025,arXiv,,"v4: added Chris Painter as listed author, consistent with listing in the pdf",cs.AI,arxiv-api,,,,,,,2503.14499,2026-07-17T05:43:07+00:00
-ale-0113,Research Foundations,research-foundations,Blog,📝,Measuring AI Ability to Complete Long Tasks,https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/,external,metr.org,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Accessible summary of the 50% task-completion time horizon and its doubling trend.,"Gives readers a concrete source in Measuring AI Ability to Complete Long Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,567,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L567,,Learn,learn,Understand the field and its boundaries.,exit,builder,practitioner-analysis,ok,https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/,Measuring AI Ability to Complete Long Tasks - METR Substack twitter Bluesky,"We propose measuring AI performance in terms of the *length* of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks.",,2025-03-19,2025,METR Blog,metr.org,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0114,Research Foundations,research-foundations,Paper,📄,Reflection-Driven Control for Trustworthy Code Agents,https://arxiv.org/abs/2512.21354,external,arxiv.org,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,"Gives readers a concrete source in Reflection-Driven Control for Trustworthy Code Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2512.21354; inspect its method and evaluation before treating results as production evidence.,medium,README.md,568,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L568,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2512.21354,[2512.21354] Reflection-Driven Control for Trustworthy Code Agents,"Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs. To address this, we introduce Reflection-Driven Control, a standardized and pluggable control module that can be seamlessly integrated into general agent architectures. Reflection-Driven Control elevates ""self-reflection"" from a post hoc patch into an explicit step in the agent's own reasoning process: during generation, the agent continuously runs an internal reflection loop that monitors and evaluates its own decision path. When potential risks are detected, the system retrieves relevant repair examples and secure coding guidelines from an evolving reflective memory, injecting these evidence-based constraints directly into subsequent reasoning steps. We instantiate Reflection-Driven Control in the setting of secure code generation and systematically evaluate it across eight classes of security-critical programming tasks. Empirical results show that Reflection-Driven Control substantially improves the security and policy compliance of generated code while largely preserving functional correctness, with minimal runtime and token overhead. Taken together, these findings indicate that Reflection-Driven Control is a practical path toward trustworthy AI coding agents: it enables designs that are simultaneously autonomous, safer by construction, and auditable.",Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao; Ivor Tsang,2025-12-22,2025,arXiv,arXiv,,Accepted to AAAI 2026 Workshop on Trust and Control in Agentic AI (TrustAgent),cs.CR,arxiv-api,,,,,,,2512.21354,2026-07-17T05:43:07+00:00
-ale-0115,Research Foundations,research-foundations,Paper,📄,Hyperagents,https://arxiv.org/abs/2603.19461,external,arxiv.org,"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Gives readers a concrete source in Hyperagents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence.,medium,README.md,569,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L569,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.19461,[2603.19461] Hyperagents,"Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\""odel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.",Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina,2026-03-19,2026,arXiv,arXiv,,Code at https://github.com/facebookresearch/Hyperagents,cs.AI,arxiv-api,,,,,,,2603.19461,2026-07-17T05:43:07+00:00
-ale-0116,Research Foundations,research-foundations,Paper,📄,PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks,https://arxiv.org/abs/2512.03549,external,arxiv.org,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,"Gives readers a concrete source in PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence.,medium,README.md,570,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L570,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2512.03549,[2512.03549] PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks,"We introduce PARC, a coding agent for the autonomous and robust execution of long-horizon computational tasks. PARC is built on a hierarchical multi-agent architecture incorporating task planning, execution, and a mechanism that evaluates its own actions and their outcomes from an independent context and provides feedback, namely self-assessment and self-feedback. This design enables PARC to detect and correct high-level strategic errors and sustain progress without human intervention. We evaluate PARC across computational science and data science tasks. In materials science, it autonomously reproduces key results from studies on lithium-ion conduction and alloy segregation. In particular, it coordinates dozens of parallel simulation tasks, each requiring roughly 43 hours of computation, managing orchestration, monitoring, and error correction end-to-end. In Kaggle-based experiments, starting from minimal natural-language instructions, PARC conducts data analysis and implements search strategies, producing solutions competitive with human-engineered baselines. These results highlight the potential of integrating a hierarchical multi-agent system with self-assessment and self-feedback to enable AI systems capable of independent, large-scale scientific and analytical work.",Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara,2025-12-03,2025,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2512.03549,2026-07-17T05:43:07+00:00
-ale-0117,Research Foundations,research-foundations,Paper,📄,When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents,https://arxiv.org/abs/2603.17104,external,arxiv.org,"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Gives readers a concrete source in When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence.,medium,README.md,571,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L571,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.17104,[2603.17104] When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents,"Current coding-agent benchmarks usually pro- vide the full task specification upfront. Real research coding often does not: the intended system is progressively disclosed through in- teraction, requiring the agent to track durable design commitments across a long session. We introduce a benchmark for this setting and study faithfulne Ss Loss U nder eM ergent s Pecification (SLUMP), defined as the reduc- tion in final implementation faithfulness un- der emergent specification relative to a single- shot specification control. The benchmark con- tains 20 recent ML papers (10 ICML 2025, 10 NeurIPS 2025), 371 atomic verifiable compo- nents, and interaction scripts of approximately 60 coding requests that progressively disclose the target design without revealing the paper itself. Final repositories are scored with a five-level component-faithfulness rubric and accompanied by an exposure audit to verify that scored components are recoverable from the visible interaction. Evaluated on Claude Code and Codex, the single-shot specification control achieves higher overall implementation fidelity on 16/20 and 14/20 papers, respectively. Structural integration degrades under emergent specification on both platforms, while seman- tic faithfulness loss is substantial on Claude Code and small on Codex. As a mitigation case study, we introduce ProjectGuard, an exter- nal project-state layer for specification tracking. On Claude Code, ProjectGuard recovers 90% of the faithfulness gap, increases fully faith- ful components from 118 to 181, and reduces severe failures from 72 to 49. These results identify specification tracking as a distinct eval- uation target for long-horizon coding agents.",Lu Yan; Xuan Chen; Xiangyu Zhang,2026-03-17,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2603.17104,2026-07-17T05:43:07+00:00
-ale-0118,Research Foundations,research-foundations,Tool,🧰,Reflexion code,https://github.com/noahshinn/reflexion,external,github.com,Reference implementation and experiments for verbal reinforcement loops.,Reference implementation and experiments for verbal reinforcement loops.,Reference implementation and experiments for verbal reinforcement loops.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Reference implementation and experiments for verbal reinforcement loops.,"Gives readers a concrete source in Reflexion code when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Inspectable GitHub source (3,205 stars; 312 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,572,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L572,,Learn,learn,Understand the field and its boundaries.,whole-loop,builder,source-implementation,ok,https://github.com/noahshinn/reflexion,GitHub - noahshinn/reflexion: [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning · GitHub,[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning - noahshinn/reflexion,,2023-03-22,2023,noahshinn/reflexion,GitHub,,,,github-api,noahshinn/reflexion,3205,312,MIT,2023-03-22T06:38:53Z,2026-07-16T12:01:06Z,,2026-07-17T05:43:07+00:00
-ale-0119,Research Foundations,research-foundations,Paper,📄,Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting,https://arxiv.org/abs/2607.00038,external,arxiv.org,"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Gives readers a concrete source in Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence.,medium,README.md,573,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L573,,Learn,learn,Understand the field and its boundaries.,objective;trigger;context;verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00038,[2607.00038] Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting,"In mid-2026 a slogan reorganized how practitioners talk about coding agents: stop prompting your agent, start designing the loop that prompts it. We take this claim seriously and give it a careful treatment. We call the object of the new practice the loop specification: a bounded, reusable artifact, made of a trigger, a goal, a verification step, a stopping rule and a memory, that a human hands to an agent harness (such as Claude Code or Codex) so the agent pursues a goal on its own, in place of step-by-step prompting. We distinguish this external loop specification from two things it is often confused with: an ordinary programming loop, and the internal perceive-act-observe cycle that the harness already provides as plumbing. We position loop engineering as a new layer in the progression from prompt to context to harness to loop, and we argue, against the stronger headlines, that it does not retire prompt engineering; loop and prompt are distinct tools with distinct uses. We offer four contributions: a definition and scope for the discipline; an anatomy and taxonomy of loop specifications organized around trigger, goal type, a five-level verification ladder, architecture, and named terminal states; a descriptive analysis of the Loop Library, a public corpus of fifty real loops that we code by hand; and a set of design principles and anti-patterns grounded in the scientific literature on self-correction, reward hacking and model-as-judge fragility. The corpus shows that practice has matured most where the discipline says it matters: seventy percent of loops verify in the autonomous zone of the ladder and seventy-four percent name their terminal states, while automated triggering and durable memory remain comparatively underdeveloped. We close with the limits the practice must respect, including the verification burden, comprehension debt and the risk of cognitive surrender.",Sandeco Macedo,2026-06-28,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.00038,2026-07-17T05:43:07+00:00
-ale-0120,Research Foundations,research-foundations,Paper,📄,From Question Answering to Task Completion: A Survey on Agent System and Harness Design,https://arxiv.org/abs/2606.20683,external,arxiv.org,"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Gives readers a concrete source in From Question Answering to Task Completion: A Survey on Agent System and Harness Design when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence.,medium,README.md,574,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L574,,Learn,learn,Understand the field and its boundaries.,context;verification;state;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.20683,[2606.20683] From Question Answering to Task Completion: A Survey on Agent System and Harness Design,"LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. As agent systems have evolved from prompt engineering to workflows and context engineering, harness engineering, and agent-native training with co-evolution, a central question has become increasingly important: where does the bottleneck in agent performance reside, in the foundation model, in the execution harness, or in the coupling between them? This survey examines LLM-based agents through a model-harness lens. We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model coupled with an execution harness. We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities: observation, context, control, action, state, and verification. Using this decomposition, we map task properties and domain pressures to harness configurations, review benchmark and evaluation practices, and synthesize model-harness evidence on how runtime design affects long-horizon task completion, efficiency, and reliability. Finally, we identify open challenges in value-aware evaluation, safety, harness generalization, and model-harness co-evolution. Rather than treating agents as models with auxiliary tools, this survey argues that agent quality -- including success, efficiency, safety, and generalization -- emerges from the interaction between model capability, runtime infrastructure, task structure, and evaluation design. A collection of papers discussed in this survey is provided in https://github.com/ggjy/Awesome-Agent-Engineering.",Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang,2026-06-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.20683,2026-07-17T05:43:07+00:00
-ale-0121,Research Foundations,research-foundations,Paper,📄,MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems,https://arxiv.org/abs/2605.22794,external,arxiv.org,"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Gives readers a concrete source in MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence.,medium,README.md,575,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L575,,Learn,learn,Understand the field and its boundaries.,verification;state;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.22794,[2605.22794] MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems,"Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifacts -- skill files, prompt configurations, memory schemas, workflow graphs -- and leave the agent harness untouched. Since routing, hook ordering, state invariants, and dispatch live in code rather than in any text artifact, an entire class of structural failure is physically unreachable from the text layer. We argue that source-level adaptation is a fundamentally more general medium: it is Turing-complete, a strict superset of every text-mutable scope, takes effect deterministically rather than through base-model compliance, and does not erode under long-context drift. We present MOSS, a system that performs self-rewriting at the source level on production agentic substrates. Each evolution is anchored to an automatically curated batch of production-failure evidence and proceeds through a deterministic multi-stage pipeline; code modification is delegated to a pluggable external coding-agent CLI while MOSS retains stage ordering and verdicts. Candidates are verified by replaying the batch against the candidate image in ephemeral trial workers, then promoted via user-consent-gated, in-place container swap with health-probe-gated rollback. On OpenClaw, MOSS lifts a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention.",Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo,2026-05-21,2026,arXiv,arXiv,,"12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss",cs.AI,arxiv-api,,,,,,,2605.22794,2026-07-17T05:43:07+00:00
-ale-0122,Research Foundations,research-foundations,Blog,📝,METR Time Horizon 1.1,https://metr.org/blog/2026-1-29-time-horizon-1-1/,external,metr.org,"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Gives readers a concrete source in METR Time Horizon 1.1 when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,576,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L576,,Learn,learn,Understand the field and its boundaries.,whole-loop,builder,practitioner-analysis,ok,https://metr.org/blog/2026-1-29-time-horizon-1-1/,Time Horizon 1.1 - METR Substack twitter Bluesky,"We’re releasing a new version of our time horizon estimates (TH1.1), using more tasks and a new eval infrastructure.",,2026-01-29,2026,METR Blog,metr.org,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0123,Research Foundations,research-foundations,Paper,📄,MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution,https://arxiv.org/abs/2607.05297,external,arxiv.org,"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Gives readers a concrete source in MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence.,medium,README.md,577,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L577,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05297,[2607.05297] MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution,"Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability. However, a fixed, hand-authored skill is rarely optimal, and cannot adapt to the diversity of tasks an agent encounters. Self-improving agents address this by rewriting their own skill files from execution traces, yielding meaningful gains on challenging benchmarks. Yet such self-evolution remains non-recursive: it improves only the task skill (what the agent does) while the improvement procedure (how it improves) is authored once and held fixed. We introduce MetaSkill-Evolve, a two-timescale framework that makes agentic skill improvement recursive: every branch carries both a task skill $s$ and a branch-local meta-skill $m=(\psi,\sigma,\alpha,\pi,\varepsilon)$ whose five components parameterise the Analyzer, Retriever, Allocator, Proposer, and Evolver agents of the improvement pipeline. Task skills evolve on a fast loop while the meta-skill evolves on a slower one under the same pipeline applied to itself, with no additional model or objective. With all five pipeline agents sharing a single frozen backbone, MetaSkill-Evolve outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks (OfficeQA, SealQA, ALFWorld), improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.",Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma,2026-07-06,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.05297,2026-07-17T05:43:07+00:00
-ale-0124,Research Foundations,research-foundations,Paper,📄,SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe,https://arxiv.org/abs/2607.03451,external,arxiv.org,"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Gives readers a concrete source in SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence.,medium,README.md,578,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L578,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03451,[2607.03451] SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe,"While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optimization via Zeroth-Order (ZO) optimization, mapping classical counterparts (central difference, trust regions) to recent literature. Noting that unlike blind numerical perturbations in classical ZO, skill trajectories serve as interpretable debugging feedback. Grounded in Claude Code philosophy and PAC learning, we establish three principles for convergence and generalization: file-system-based trajectory exploration, consensus attribute mining, and independent validation gating. Eliminating redundancies, we propose SkillOpt-Lite. It accelerates convergence and outperforms full SkillOpt: improving LiveMath by +8.8 points on GPT-5.5 and +25.4 points on GPT-5.4-nano, allowing the nano model to surpass standard GPT-5.4 optimized by SkillOpt. Finally, we integrate our framework into production coding agents like VSCode Copilot, enabling developers to evolve agent skills via one line of vibe. Because our framework treats all agent components simply as standard editable code, this minimal pipeline naturally generalizes to full harness optimization (HarnessOpt). On SpreadsheetBench, HarnessOpt enables GPT-5.4-nano to achieve 0.7758 accuracy, outperforming the larger GPT-5.5 running standard pipelines (0.7620). Code is available at https://github.com/EvolvingLMMs-Lab/SkillOpt-Lite.",Yifei Shen; Bo Li; Xinjie Zhang,2026-07-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.03451,2026-07-17T05:43:07+00:00
-ale-0125,Research Foundations,research-foundations,Paper,📄,Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops,https://arxiv.org/abs/2607.07663,external,arxiv.org,"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Gives readers a concrete source in Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence.,medium,README.md,579,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L579,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07663,[2607.07663] Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops,"AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself. This literature is described under a vocabulary (""self-refine,"" ""self-reward,"" ""self-play,"" ""self-evolve"") that conflates fundamentally different ambitions. We survey 1,250 arXiv papers (2024-2026) along two axes: what the system improves -- its behavior in deployment, its policy through training, its evaluator, or the research process itself -- and the degree of loop closure (human-in-the-loop to fully closed). The taxonomy separates bounded self-refinement -- convergent, evaluable, and already industrial practice -- from open-ended recursive self-improvement (RSI), which remains bounded by grounding requirements, collapse dynamics, and compute constraints on every measured axis. Its distinctive feature is a dedicated category for self-evaluation: every improvement loop is a claim that some signal can substitute for human judgment. We survey the evaluator design space -- judges, process reward models, verifiers, rubrics, meta-evaluation -- order the signals into a verification hierarchy from formal verifiers (strongest) to intrinsic self-assessment (weakest), and observe that demonstrated self-improvement strength tracks this hierarchy, that its failure modes (self-confirming loops, model collapse, diversity collapse) follow from its violations, and that the ""research direction-setting"" bottleneck keeping humans in the loop sits at the top of that hierarchy. We connect the technical literature to the theory of RSI limits and to the safety and governance questions raised by frontier-lab accounts of closing the loop, and identify governance-grade measurement of self-improvement as the field's most underpopulated niche.",Mingguang Chen; Licheng Wang; Bo Qu,2026-07-08,2026,arXiv,arXiv,,"42 pages, 6 figures",cs.AI,arxiv-api,,,,,,,2607.07663,2026-07-17T05:43:07+00:00
-ale-0126,Research Foundations,research-foundations,Paper,📄,From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents,https://arxiv.org/abs/2607.07321,external,arxiv.org,"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","Gives readers a concrete source in From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence.,medium,README.md,580,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L580,,Learn,learn,Understand the field and its boundaries.,workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07321,[2607.07321] From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents,"Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to reinvent low-level logic for every recurring workflow, leading to increased reasoning overhead and failure rates. In this study, we propose that agents can achieve self-evolution by synthesizing these atomic actions into reusable Standard Operating Procedures (SOPs), which function as callable higher-order tools that encapsulate multi-step logic. We further introduce EvoSOP, a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset through a systematic lifecycle of construction, merging, evaluation, and pruning. Extensive experiments demonstrate that EvoSOP significantly boosts task success rates while substantially reducing the number of interaction rounds compared to baselines. Our analysis also reveals that iterative tool optimization fosters reliable and efficient tool-use patterns, providing a scalable pathway for the development of self-evolving agents.",Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07321,2026-07-17T05:43:07+00:00
-ale-0127,Research Foundations,research-foundations,Paper,📄,TTHE: Test-Time Harness Evolution,https://arxiv.org/abs/2607.08124,external,arxiv.org,"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Gives readers a concrete source in TTHE: Test-Time Harness Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence.,medium,README.md,581,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L581,,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08124,[2607.08124] TTHE: Test-Time Harness Evolution,"The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time. This limits adaptation when the test distribution, failure modes, or tool interactions differ from those seen during development. We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs. We introduce Test-Time Harness Evolution (TTHE), which treats the executable harness as the state of test-time adaptation. During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces, without gold labels or task-specific supervision; a judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs. Crucially, TTHE does not update model weights, require gold labels, or train a separate adaptation model: solver, proposers, and judge are different roles and harnesses around the same frozen LLM, so all adaptation occurs through changes to the surrounding program. Across text-to-SQL, competitive programming, software engineering, data-science coding, and agentic tool-use tasks, TTHE improves fixed ReAct-style baseline harnesses, yielding persistent, inspectable improvements rather than a pre-searched workflow or per-query retries. These results recast test-time adaptation for LLM agents as evolution over executable control programs and identify execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.",Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han,2026-07-09,2026,arXiv,arXiv,,"15 pages, 5 figures",cs.SE,arxiv-api,,,,,,,2607.08124,2026-07-17T05:43:07+00:00
-ale-0128,Research Foundations,research-foundations,Paper,📄,DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment,https://arxiv.org/abs/2607.07820,external,arxiv.org,"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Gives readers a concrete source in DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence.,medium,README.md,582,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L582,,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07820,[2607.07820] DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment,"Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.",Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. Fung,2026-07-08,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.07820,2026-07-17T05:43:07+00:00
-ale-0129,Research Foundations,research-foundations,Paper,📄,What Makes a Good Bug Report for an AI Agent?,https://arxiv.org/abs/2607.07593,external,arxiv.org,"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Gives readers a concrete source in What Makes a Good Bug Report for an AI Agent? when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence.,medium,README.md,583,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L583,,Learn,learn,Understand the field and its boundaries.,intake,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07593,[2607.07593] What Makes a Good Bug Report for an AI Agent?,"Automated program repair (APR) agents are transitioning from research benchmarks to developer workflows, yet they still begin with bug reports written for human developers. While decades of research have established what makes a good bug report for humans (e.g., steps to reproduce, stack traces), it remains unclear whether these features transfer to LLM-based agents. We study this question in two analyses. First, we use statistical modeling to examine associations between 27 bug-report features and repair success across 433 SWE-bench Verified issues attempted by 87 repair agents. We find that fix suggestions, reproduction scripts, repository source code, and localization info are associated with higher resolution likelihood, while longer reports are associated with lower odds. Second, we conduct controlled ablations across 2 models and 17 problem-statement mutations on SWE-bench Pro, varying the information available to an agent while holding the underlying task fixed. We remove or isolate selected bug-report content, delete fault-localization cues, and test structural changes that flatten lists or remove section headers. We find that both models depend on localization cues and expected behavior, and that structural changes alone can reduce solve rates, even without removing any content. The two models diverge in how they handle missing information: Qwen searches more widely and can exhaust its turn budget, while Gemma commits to a plausible interpretation early and patches on it. Our findings indicate that a good bug report for an agent overlaps with, but is not identical to, a good report for a human: agents benefit most from concrete, executable, and well-localized information, whereas some qualities long emphasized for human readers, such as natural language steps to reproduce and readable descriptions, contribute little or even correlate with lower success.",Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07593,2026-07-17T05:43:07+00:00
-ale-0130,Research Foundations,research-foundations,Paper,📄,AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution,https://arxiv.org/abs/2607.08252,external,arxiv.org,"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Gives readers a concrete source in AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08252; inspect its method and evaluation before treating results as production evidence.,medium,README.md,584,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L584,,Learn,learn,Understand the field and its boundaries.,verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08252,[2607.08252] AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution,"Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace this failure to model-level convergence toward high-probability behavioral channels and system-level context gravity from State, memory, history, and environment summaries. We introduce AutoPersonas, a multi-timescale life-environment engine for bounded persona-level recursive self-evolution. It separates environment-side Occurrences, accumulated Observations, and persona State. Its OSO loop admits divergent future-facing material while requiring evidence-governed absorption before State or reachability changes. A three-year compressed simulation exposed environment watermark shells, occurrence-hardening gaps, slow-change accumulation failures, recursive indecision, and weak relationship persistence. An eight-model 40-day stress test generated 1,600 events and found mean rolling 5-day action-category repetition of 95.2%-97.6%, with all models crossing 90% by day 11. Semantic re-keeping found 79.0%-88.0% macro-theme repetition across all direct-loop runs. In a same-runtime 40-day A/B, context-slice masking plus per-sample divergence targeting reduced macro-theme repetition from 61.8% to 36.3% and roughly doubled cumulative theme count. A juvenile-goblin fictional-world run reproduced the anti-fixation regime without hard real-world intrusions. These results support a bounded claim: separating controlled divergence from evidence-governed absorption can reduce persona-environment self-locking while preserving identity continuity.",Mengchen Li,2026-07-09,2026,arXiv,arXiv,,"52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included",cs.AI,arxiv-api,,,,,,,2607.08252,2026-07-17T05:43:07+00:00
-ale-0131,Research Foundations,research-foundations,Paper,📄,Agentic Data Environments,https://arxiv.org/abs/2607.07397,external,arxiv.org,"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Gives readers a concrete source in Agentic Data Environments when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07397; inspect its method and evaluation before treating results as production evidence.,medium,README.md,585,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L585,,Learn,learn,Understand the field and its boundaries.,state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07397,[2607.07397] Agentic Data Environments,"Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While databases remain central to modern computing, agents operate over a broader data environment spanning files, APIs, applications, and system state. In this talk, I will outline early work on Agentic Data Environments -- the execution substrate in which agents operate -- that both amplify agent capabilities and enforce safety guarantees. This perspective reframes data systems from passive stores of state into active substrates for safe, reliable execution.",Elaine Ang; Chenxi Huang; Georgios Liargkovas; Jerry Liu; Jinhui Liu; Nikos Pagonas; Charlie Summers; Haonan Wang; Jiakai Xu; Tianle Zhou; Yusen Zhang; Zhou Yu; Zhuo Zhang; Tianyi Peng; Kostis Kaffes; Eugene Wu,2026-07-08,2026,IEEE Data Bulletin Vol. 50 No. 1 2026,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07397,2026-07-17T05:43:07+00:00
-ale-0132,Research Foundations,research-foundations,Paper,📄,"Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation",https://arxiv.org/abs/2607.08938,external,arxiv.org,"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Gives readers a concrete source in Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08938; inspect its method and evaluation before treating results as production evidence.,medium,README.md,586,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L586,,Learn,learn,Understand the field and its boundaries.,budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08938,"[2607.08938] Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation","Frontier LLM agents are automating many business tasks, but their high inference cost makes large-scale deployment unsustainable. Small language models (SLMs) offer a cheaper alternative, yet they typically fall short when swapped into a harness designed for a frontier LLM. We show that for many routine business tasks, SLM agents can match LLM performance at 90% lower cost, when paired with an adapted harness that can be automatically discovered by a meta agent. The key insight is that much of the task difficulty is shared across instances and can be lifted from the model into the harness via tailored instructions, tools, and orchestration loops. To study this systematically, we create a framework that maps agent failure modes to harness adaptation strategies, and build a harness optimizer that automatically discovers effective adaptations from failure trajectories. Across seven business-oriented agentic tasks and three SLM families, we found optimized harnesses significantly improve performance on 16 of 21 task-SLM pairs, with seven pairs closing the SLM-LLM performance gap and the best SLM agent recovering 89.7% of LLM performance at 4% of the cost. Our analysis further shows that adaptation works best for tasks with more repetitive workflows and for SLMs with sufficient base capabilities. Together, these results suggest that harness adaptation can expand the practical deployment range of SLM agents in routine business tasks.",Chenyang Yang; Xinran Zhao; Tongshuang Wu; Christian Kästner,2026-07-09,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.08938,2026-07-17T05:43:07+00:00
-ale-0133,Research Foundations,research-foundations,Paper,📄,Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills,https://arxiv.org/abs/2607.09065,external,arxiv.org,"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","Gives readers a concrete source in Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.09065; inspect its method and evaluation before treating results as production evidence.,medium,README.md,587,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L587,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09065,[2607.09065] Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills,"Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances in AI agents have introduced a potentially new reusable artifact: skills. Emerging agent skill repositories and marketplaces enable developers to package, share, and reuse SE expertise as reusable skills. This trend raises a fundamental question: what SE activities are being encapsulated into reusable skills? Existing studies primarily focus on a broad range of skills acquisition, safety, or benchmarking, while lacking a systematic understanding of SE-specific skills and their coverage across the software development lifecycle. To address this gap, we conduct the first large-scale empirical study of SE skills in public repositories and marketplaces. We collect and analyze a large corpus of SE skills, examining the activities they encapsulate, lifecycle coverage, evolution characteristics, and evaluation mechanisms. Our findings reveal that SE activities are increasingly becoming reusable artifacts via skills and suggest promising research opportunities for skill recommendation and engineering-oriented structuring, as well as the need for mechanisms to encapsulate high-context SE activities into reusable skills. Overall, our study provides the first activity-centric characterization of SE skills and reveals how SE activities are increasingly being transformed into reusable skills. These findings offer new insights into skill reuse, ecosystem development, and the future of agent-centric SE.",Jialun Cao; Xinru Yan; Songqiang Chen; Yaojie Lu; Zhongxin Liu; Shing-Chi Cheung,2026-07-10,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.09065,2026-07-17T05:43:07+00:00
-ale-0134,Research Foundations,research-foundations,Blog,📝,Harness Engineering for Self-Improvement,https://lilianweng.github.io/posts/2026-07-04-harness/,external,lilianweng.github.io,"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Gives readers a concrete source in Harness Engineering for Self-Improvement when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from lilianweng.github.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,588,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L588,,Learn,learn,Understand the field and its boundaries.,delegation,builder,practitioner-analysis,ok,https://lilianweng.github.io/posts/2026-07-04-harness/,Harness Engineering for Self-Improvement | Lil'Log,"The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights directly, or more broadly the model improves the training pipeline and the deployment system, which in turn enables a better successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI).",Lilian Weng,2026-07-04,2026,,lilianweng.github.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0135,Research Foundations,research-foundations,Paper,📄,"Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime",https://arxiv.org/abs/2607.11346,external,arxiv.org,"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Gives readers a concrete source in Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.11346; inspect its method and evaluation before treating results as production evidence.,medium,README.md,589,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L589,2026-07-15,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11346,"[2607.11346] Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents","Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM performs semantic execution. A three-arm SOPBench study across six models separates representation from runtime: compiled text never significantly hurts and gains up to 16.0 points where official prose underperforms. Runtime guidance is capability-gated. Two strong models independently show positive seven-domain PG contrasts (58:19 and 75:31 discordant pairs), whereas weak models are harmed. A full-program cursor ablation (active frame first, complete program retained) recovers much of the strong-model refusal gain; selective visibility adds a smaller improvement. Paired probe and audit measurements track this divide to spontaneous state discipline rather than reconstruction ability. On Bank the three primary arms rise from 70.4 to 86.4 to 92.8, with 100% refusal correctness. Practical guidance: compile first; enable active-frame paging only after a model-level discipline check.",Chenglin Yu; Li Yin; Ying Yu; Qingxin Fan; RunyangRay Zhong; Hongxia Yang; Ming Li,2026-07-13,2026,arXiv,arXiv,,"9 pages, 3 figures, 5 tables",cs.AI,arxiv-api,,,,,,,2607.11346,2026-07-17T05:43:07+00:00
-ale-0136,Research Foundations,research-foundations,Paper,📄,Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation,https://arxiv.org/abs/2607.11288,external,arxiv.org,"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Gives readers a concrete source in Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.11288; inspect its method and evaluation before treating results as production evidence.,medium,README.md,590,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L590,2026-07-15,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11288,[2607.11288] Mako: A Self-Evolving Agentic Operating System (SE-AOS) for Autonomous Web Exploitation,"We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-loading them back into itself. Mako is the first SE-AOS instance for security research and the autonomous web exploitation engine developed within LaunchSafe. LaunchSafe builds autonomous security agents for continuous offensive testing and agent-driven security research; Mako is the core engine behind that platform. On the public XBOW validation-benchmarks, 104 containerised, CTF-style web applications spanning 26 vulnerability classes across three difficulty tiers, Mako achieves full-suite coverage: it drives every one of the 104 targets to emit a cryptographically fresh, per-build flag, under a verification regime that makes fabricated or memorised results impossible. Our central result is a law of autonomous exploitation: once a capability exists and is discoverable, difficulty collapses; capability, not reasoning, is what is scarce, together with an architecture and formalism that turn that law into a self-improving system. Mako further runs a gated self-evolution loop that proposes, sandboxes, and commits improvements to its own agents and rules when fitness does not regress. We deliberately withhold the operational results, payloads, exploit chains, and tool source, because a system that reduces full-spectrum web exploitation to a repeatable, machine-speed pipeline is dual-use research of concern. We publish the science; we withhold the weapon.",Praneeth Narisetty; Shiva Nagendra Babu Kore,2026-07-13,2026,arXiv,arXiv,,"13 pages, 10 figures, 8 tables",cs.CR,arxiv-api,,,,,,,2607.11288,2026-07-17T05:43:07+00:00
-ale-0137,Research Foundations,research-foundations,Paper,📄,How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,https://arxiv.org/abs/2607.10856,external,arxiv.org,"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Gives readers a concrete source in How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.10856; inspect its method and evaluation before treating results as production evidence.,medium,README.md,591,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L591,2026-07-15,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10856,[2607.10856] How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,"The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little is known about how developers build these systems in practice: existing studies mine repositories or examine deployment, but few investigate how SE agents are constructed. Through semi-structured interviews with 20 practitioners from 12 organizations and an online survey of 80 practitioners, this paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face. We find that as implementation becomes cheaper, bottlenecks shift rather than disappear: long-standing non-coding work such as requirements, coordination, review, and deployment becomes more visible, while reviewing and evaluating agent output becomes new and central. We characterize a seven-stage workflow and a shift toward evaluation-driven development, in which evaluation steers iteration and specifications become versioned artifacts read by both humans and agents. We further identify six challenges that teams face, together with the practices they adopt to address them, including unreliable evaluation signals, comprehension debt as code outpaces understanding, and behavioral changes introduced by provider-side model updates.",Yunbo Lyu; David Williams; Jieke Shi; Zhensu Sun; Chao Peng; Zhou Yang; Federica Sarro; David Lo,2026-07-12,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.10856,2026-07-17T05:43:07+00:00
-ale-0138,Research Foundations,research-foundations,Paper,📄,Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,https://arxiv.org/abs/2607.10113,external,arxiv.org,"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Gives readers a concrete source in Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.10113; inspect its method and evaluation before treating results as production evidence.,medium,README.md,592,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L592,2026-07-15,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10113,[2607.10113] Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,"Large language model agents increasingly store reusable procedures outside the model. These reusable procedures are often called \emph{skills}: they may be code functions, natural-language instructions, SKILL.md packages, workflow graphs, or learned adapters that a future agent can retrieve and invoke. This taxonomy-driven survey asks how such skill libraries change over time. Across a $124$-paper $2023$--$2026$ audit set, we synthesize dynamic skill systems as \emph{lifecycle-managed, verified, evolving artifact stores}: agents collect evidence from interaction, propose skill updates, verify and admit candidates, organize them for retrieval and composition, repair or prune stale entries, and govern sharing through provenance and rollback. We organize the literature around three survey tools. First, a $\text{six}$-sense taxonomy distinguishes the structurally different artifacts called ``skills'' in current papers. Second, an $\text{eight}$-stage lifecycle architecture identifies the recurring design decisions behind evidence acquisition, proposal, verification/admission, storage, retrieval/composition, maintenance, distillation/portability, and governance. Third, a lightweight skill-record schema and $\text{ten}$-operator vocabulary provide common terms for comparing library updates without elevating them into a separate method contribution. Using this structure, we synthesize evidence-graded patterns with explicit caveats: admission and repair are repeatedly important, verifier quality materially affects skill-aware RL, flat retrieval can degrade as libraries grow, and current benchmarks still under-report library trajectories, usage--utility gaps, and safety surfaces. We close with concrete reporting standards and open problems for evaluating dynamic skills as changing libraries rather than static prompt or tool collections.",Yubo Li,2026-07-11,2026,"Transactions on Machine Learning Research, 2026",arXiv,,"Accepted by TMLR (2026.07), OpenReview Link: https://openreview.net/forum?id=cjU3YbcRr8",cs.AI,arxiv-api,,,,,,,2607.10113,2026-07-17T05:43:07+00:00
-ale-0139,Agent Workflow Patterns,agent-workflow-patterns,Docs,📚,Building Effective Agents,https://www.anthropic.com/engineering/building-effective-agents,external,www.anthropic.com,"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.",Gives readers a concrete source in Building Effective Agents when they need to turn a recurring-agent idea into an explicit loop contract.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,600,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L600,,Design,design,Specify a loop contract and operating pattern.,delegation,builder,technical-documentation,ok,https://www.anthropic.com/engineering/building-effective-agents,Building Effective AI Agents \ Anthropic,"Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0140,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,"Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime",https://www.preprints.org/manuscript/202603.1756,external,www.preprints.org,"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Gives readers a concrete source in Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime when they need to turn a recurring-agent idea into an explicit loop contract.",Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,601,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L601,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,researcher;evaluator,research-paper,restricted,https://www.preprints.org/manuscript/202603.1756,,,Chaoyue He; Xin Zhou; Di Wang; Hong Xu; Wei Liu; Chunyan Miao,2026-04-23,2026,Preprints.org,Preprints.org,10.20944/preprints202603.1756.v2,Version 2; the primary source states that this preprint is not peer-reviewed.,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0141,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,How we built our multi-agent research system,https://www.anthropic.com/engineering/multi-agent-research-system,external,www.anthropic.com,"Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.",Gives readers a concrete source in How we built our multi-agent research system when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,602,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L602,,Design,design,Specify a loop contract and operating pattern.,context;delegation,builder,practitioner-analysis,ok,https://www.anthropic.com/engineering/multi-agent-research-system,How we built our multi-agent research system \ Anthropic,On the the engineering challenges and lessons learned from building Claude's Research system,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0142,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Building Effective AI Agents: Architecture Patterns and Implementation Frameworks,https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf,external,resources.anthropic.com,"PDF overview of agent architecture patterns, including generator-evaluator loops.","PDF overview of agent architecture patterns, including generator-evaluator loops.","PDF overview of agent architecture patterns, including generator-evaluator loops.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: PDF overview of agent architecture patterns, including generator-evaluator loops.",Gives readers a concrete source in Building Effective AI Agents: Architecture Patterns and Implementation Frameworks when they need to turn a recurring-agent idea into an explicit loop contract.,Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,603,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L603,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,researcher;evaluator,research-paper,ok,https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf,,,Anthropic,2025-12-03,2025,Anthropic eBook,Anthropic,,Date verified from the primary PDF creation metadata.,,pdf-metadata,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0143,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,AI Agent Architectures,https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/,external,hld.handbook.academy,"System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.",Gives readers a concrete source in AI Agent Architectures when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from hld.handbook.academy; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,604,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L604,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,practitioner-analysis,ok,https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/,"AI Agent Architectures (ReAct, Reflection, Planning, Tool Use, Memory) - The HLD Handbook","The canonical patterns for turning an LLM into an agent: ReAct's think-act-observe loop, reflection and self-critique, planner-executor decomposition, tool use and function calling, and how agents manage short- and long-term memory.",,,,,The HLD Handbook,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0144,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,What Are Agentic Workflows?,https://weaviate.io/blog/what-are-agentic-workflows,external,weaviate.io,"Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Accessible taxonomy of planning, tool use, reflection, and memory patterns.",Gives readers a concrete source in What Are Agentic Workflows? when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from weaviate.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,605,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L605,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,practitioner-analysis,ok,https://weaviate.io/blog/what-are-agentic-workflows,"What Are Agentic Workflows? Patterns, Memory, Use Cases, and Examples | Weaviate","Agentic workflows combine AI agents, tools, and agent memory to create adaptive systems. Learn the core patterns, use cases, and real-world examples.",,2025-03-06,2025,,weaviate.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0145,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,Agent Planning & Reflection Patterns,https://learnaivisually.com/tracks/ai-agents/planning-reflection,external,learnaivisually.com,"Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.",Gives readers a concrete source in Agent Planning & Reflection Patterns when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from learnaivisually.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,606,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L606,,Design,design,Specify a loop contract and operating pattern.,budget;exit,builder,practitioner-analysis,ok,https://learnaivisually.com/tracks/ai-agents/planning-reflection,Agent Planning & Reflection Patterns | Learn AI Visually LAV LAV,"When agents should plan, retry, pause, or stop. Reasoning budget, ReAct, Reflexion, and termination logic — each tied to a 'when' decision.",,,,,Learn AI Visually,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0146,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,Agentic Design Patterns,https://addyosmani.com/agents/04-agentic-design-patterns/,external,addyosmani.com,"Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.",Gives readers a concrete source in Agentic Design Patterns when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,607,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L607,,Design,design,Specify a loop contract and operating pattern.,workspace,builder,practitioner-analysis,ok,https://addyosmani.com/agents/04-agentic-design-patterns/,AddyOsmani.com - Lesson 4: agentic design patterns,"Addy Osmani is an engineering and evangelism leader who spent over 14 years at Google leading developer experience across Chrome and, in recent years, AI (Gemini, coding agents, and agentic engineering), most recently as a Director at Google Cloud AI.",Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0147,Agent Workflow Patterns,agent-workflow-patterns,Pattern,🔁,12 Factor Agents,https://github.com/humanlayer/12-factor-agents,external,github.com,"Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.",Gives readers a concrete source in 12 Factor Agents when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (24,352 stars; 1,848 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,608,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L608,,Design,design,Specify a loop contract and operating pattern.,state,builder,operational-pattern,ok,https://github.com/humanlayer/12-factor-agents,GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? · GitHub,What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents,,2025-03-30,2025,humanlayer/12-factor-agents,GitHub,,,,github-api,humanlayer/12-factor-agents,24352,1848,NOASSERTION,2025-03-30T22:10:39Z,2026-07-17T04:31:16Z,,2026-07-17T05:43:07+00:00
-ale-0148,Agent Workflow Patterns,agent-workflow-patterns,Pattern,🔁,Durable Execution for Agentic Workflows,https://arizenai.com/durable-execution/,external,arizenai.com,"Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.",Gives readers a concrete source in Durable Execution for Agentic Workflows when they need to turn a recurring-agent idea into an explicit loop contract.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,609,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L609,,Design,design,Specify a loop contract and operating pattern.,state,builder,operational-pattern,ok,https://arizenai.com/durable-execution/,Durable Execution for Agentic Workflows | Arizen,A while loop is at-most-once across process boundaries. Production agents need exactly-once. The architecture must encode the guarantee.,,2026-03-30,2026,,Arizen,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0149,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Code as Agent Harness,https://arxiv.org/abs/2605.18747,external,arxiv.org,"Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.",Gives readers a concrete source in Code as Agent Harness when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2605.18747; inspect its method and evaluation before treating results as production evidence.,medium,README.md,610,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L610,,Design,design,Specify a loop contract and operating pattern.,delegation;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.18747,[2605.18747] Code as Agent Harness,"Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.",Xuying Ning; Katherine Tieu; Dongqi Fu; Tianxin Wei; Zihao Li; Yuanchen Bei; Jiaru Zou; Mengting Ai; Zhining Liu; Ting-Wei Li; Lingjie Chen; Yanjun Zhao; Ke Yang; Bingxuan Li; Cheng Qian; Gaotang Li; Xiao Lin; Zhichen Zeng; Ruizhong Qiu; Sirui Chen; Yifan Sun; Xiyuan Yang; Ruida Wang; Rui Pan; Chenyuan Yang; Dylan Zhang; Liri Fang; Zikun Cui; Yang Cao; Pan Chen; Dorothy Sun; Ren Chen; Mahesh Srinivasan; Nipun Mathur; Yinglong Xia; Hong Li; Hong Yan; Pan Lu; Lingming Zhang; Tong Zhang; Hanghang Tong; Jingrui He,2026-05-18,2026,arXiv,arXiv,,GitHub: https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers,cs.CL,arxiv-api,,,,,,,2605.18747,2026-07-17T05:43:07+00:00
-ale-0150,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Agentic Agile-V: From Vibe Coding to Verified Engineering,https://arxiv.org/abs/2605.20456,external,arxiv.org,"Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.",Gives readers a concrete source in Agentic Agile-V: From Vibe Coding to Verified Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2605.20456; inspect its method and evaluation before treating results as production evidence.,medium,README.md,611,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L611,,Design,design,Specify a loop contract and operating pattern.,delegation;verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.20456,[2605.20456] Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development,"Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim that autonomous code generation automatically improves engineering outcomes. Controlled studies report productivity gains in some enterprise tasks, slowdowns in mature open-source work, moderate but heterogeneous meta-analytic effects, and persistent failures in repository setup, dependency handling, permission gating, and hardware verification. This paper argues that the central problem is no longer prompt engineering; it is engineering process control. It synthesizes evidence from agentic software engineering, GitHub-scale adoption studies, repository-level agent configuration, productivity trials, issue-resolution benchmarks, and hardware/RTL verification research. It proposes Agentic Agile-V, a process framework that uses Agile-V as the lifecycle backbone and a task-level SCOPE-V loop - Specify, Constrain, Orchestrate, Prove, Evolve, and Verify - to convert conversational intent into structured engineering artifacts and acceptance evidence. The paper contributes: (i) a taxonomy of minimum input artifacts for agentic software, firmware, and hardware work; (ii) a conversation-to-contract gate that separates exploratory dialogue from implementation; (iii) risk-adaptive feature, bug-fix, testing, and hardware workflows; and (iv) an evidence-bundle acceptance model for agent-generated artifacts. The paper concludes that agentic AI does not eliminate engineering discipline; it increases the value of requirements, constraints, traceability, independent verification, and human approval.",Christopher Koch,2026-05-19,2026,arXiv,arXiv,,"7 pages, 1 figure",cs.SE,arxiv-api,,,,,,,2605.20456,2026-07-17T05:43:07+00:00
-ale-0151,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Agentic Software Engineering: Foundational Pillars and a Research Roadmap,https://arxiv.org/abs/2509.06216,external,arxiv.org,"Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.",Gives readers a concrete source in Agentic Software Engineering: Foundational Pillars and a Research Roadmap when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2509.06216; inspect its method and evaluation before treating results as production evidence.,medium,README.md,612,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L612,,Design,design,Specify a loop contract and operating pattern.,delegation;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2509.06216,[2509.06216] Agentic Software Engineering: Foundational Pillars and a Research Roadmap,"Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Agentic SE era, comprising two symbiotic modalities: SE for Humans and SE for Agents. This duality demands a radical reimagining of the foundational pillars of SE (actors, processes, tools, and artifacts) which manifest differently across each modality. We propose two purpose-built workbenches to support this vision. The Agent Command Environment (ACE) serves as a command center where humans orchestrate and mentor agent teams, handling outputs such as Merge-Readiness Packs (MRPs) and Consultation Request Packs (CRPs). The Agent Execution Environment (AEE) is a digital workspace where agents perform tasks while invoking human expertise when facing ambiguity or complex trade-offs. This bi-directional partnership, which supports agent-initiated human callbacks and handovers, gives rise to new, structured engineering activities (i.e., processes) that redefine human-AI collaboration, elevating the practice from agentic coding to true agentic software engineering. This paper presents the Structured Agentic Software Engineering (SASE) vision, outlining several of the foundational pillars for the future of SE. The paper culminates in a research roadmap that identifies a few key challenges and opportunities while briefly discussing the resulting impact of this future on SE education. Our goal is not to offer a definitive solution, but to provide a conceptual scaffold with structured vocabulary to catalyze a community-wide dialogue, pushing the SE community to think beyond its classic, human-centric tenets toward a disciplined, scalable, and trustworthy agentic future.",Ahmed E. Hassan; Hao Li; Dayi Lin; Bram Adams; Tse-Hsun Chen; Yutaro Kashiwa; Dong Qiu,2025-09-07,2025,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2509.06216,2026-07-17T05:43:07+00:00
-ale-0152,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,The Art of Loop Engineering,https://www.langchain.com/blog/the-art-of-loop-engineering,external,www.langchain.com,"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.",Gives readers a concrete source in The Art of Loop Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,613,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L613,,Design,design,Specify a loop contract and operating pattern.,trigger;verification,builder,practitioner-analysis,ok,https://www.langchain.com/blog/the-art-of-loop-engineering,The Art of Loop Engineering,"Agents automate real-world work, but reliable performance requires more than a good model, it requires a carefully designed harness built for specific tasks. This post explores the core agent loop, how stacking and extending loops builds more effective agents, and how to instrument each level with LangChain primitives.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0153,Agent Workflow Patterns,agent-workflow-patterns,Tool,🧰,Loopy,https://github.com/Forward-Future/loopy,external,github.com,"Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.",Gives readers a concrete source in Loopy when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (2,731 stars; 237 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,614,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L614,,Design,design,Specify a loop contract and operating pattern.,verification;exit,builder,source-implementation,ok,https://github.com/Forward-Future/loopy,"GitHub - Forward-Future/loopy: A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. · GitHub","A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. - Forward-Future/loopy",,2026-06-12,2026,Forward-Future/loopy,GitHub,,,,github-api,Forward-Future/loopy,2731,237,MIT,2026-06-12T14:44:22Z,2026-07-17T01:28:31Z,,2026-07-17T05:43:07+00:00
-ale-0154,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,The Factory Model: How Coding Agents Changed Software Engineering,https://addyosmani.com/blog/factory-model/,external,addyosmani.com,"Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.",Gives readers a concrete source in The Factory Model: How Coding Agents Changed Software Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,615,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L615,,Design,design,Specify a loop contract and operating pattern.,verification,builder,practitioner-analysis,ok,https://addyosmani.com/blog/factory-model/,AddyOsmani.com - The Factory Model: How Coding Agents Changed Software Engineering,Software engineering is not about writing code anymore. It is about building the factory that builds your software.,Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0155,Agent Workflow Patterns,agent-workflow-patterns,Docs,📚,2026 Agentic Coding Trends Report,https://resources.anthropic.com/2026-agentic-coding-trends-report,external,resources.anthropic.com,"Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Primary-source operational guidance rather than commentary. Resource-specific angle: Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).",Gives readers a concrete source in 2026 Agentic Coding Trends Report when they need to turn a recurring-agent idea into an explicit loop contract.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,616,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L616,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,builder,technical-documentation,ok,https://resources.anthropic.com/2026-agentic-coding-trends-report,2026 Agentic Coding Trends Report,"How coding agents are transforming software development - and what it means for engineering teams in 2026. Insights on multi-agent systems, human-AI collaboration, and scaling agentic coding across organizations. Includes case studies from Rakuten, TELUS, Zapier, and more.",,,2026,,Anthropic,,,,url-date,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0156,Agent Workflow Patterns,agent-workflow-patterns,Tool,🧰,HomeRail,https://github.com/xiaotianfotos/homerail,external,github.com,"TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.",Gives readers a concrete source in HomeRail when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (579 stars; 133 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,617,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L617,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,builder,source-implementation,ok,https://github.com/xiaotianfotos/homerail,GitHub - xiaotianfotos/homerail: Voice-first local agent orchestration runtime for auditable DAG workflows. · GitHub,Voice-first local agent orchestration runtime for auditable DAG workflows. - xiaotianfotos/homerail,,2026-07-07,2026,xiaotianfotos/homerail,GitHub,,,,github-api,xiaotianfotos/homerail,579,133,MIT,2026-07-07T12:11:18Z,2026-07-17T05:43:04Z,,2026-07-17T05:43:07+00:00
-ale-0157,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,"Old and New Apps, via Modern Coding Agents",https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/,external,terrytao.wordpress.com,"Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Gives readers a concrete source in Old and New Apps, via Modern Coding Agents when they need to turn a recurring-agent idea into an explicit loop contract.","Contextual source from terrytao.wordpress.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,618,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L618,,Design,design,Specify a loop contract and operating pattern.,verification;escalation,builder,practitioner-analysis,ok,https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/,"Old and new apps, via modern coding agents | What's new","I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0, both for my complex analysis and linear alge…",,2026-07-11,2026,,What's new,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0158,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,"Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable",https://arxiv.org/abs/2607.13285,external,arxiv.org,"Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Gives readers a concrete source in Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable when they need to turn a recurring-agent idea into an explicit loop contract.",Research source arXiv:2607.13285; inspect its method and evaluation before treating results as production evidence.,medium,README.md,619,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L619,2026-07-17,Design,design,Specify a loop contract and operating pattern.,budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13285,"[2607.13285] Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable","The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modified. Before such a change can be made, a developer or coding agent must identify all code locations that implement the target behavior. This is difficult because production harnesses are large, tightly coupled, and behaviorally distributed, while modification requests describe what the system should do and repositories are organized by files and modules. Code search, repository indexing, and long-context processing ease inspection, but still leave this behavior-to-code mapping to be recovered by hand. Behavior localization is therefore a central bottleneck in harness evolution. We introduce the Harness Handbook, a behavior-centric representation synthesized automatically from a harness codebase via static analysis and LLM-assisted structuring, linking each behavior to its corresponding source. We also introduce Behavior-Guided Progressive Disclosure (BGPD), which guides agents from high-level behaviors to relevant implementation details and verifies candidate locations against the current source. On diverse modification requests from two open-source harnesses, Handbook-Assisted planning improves behavior localization and edit-plan quality while using fewer planner tokens, with the largest gains on scattered sites, rarely executed paths, and cross-module interactions. Evolving complex agentic systems thus depends not only on generating edits, but also on determining where those edits should be made.",Ruhan Wang; Yucheng Shi; Zongxia Li; Zhongzhi Li; Yue Yu; Junyao Yang; Kishan Panaganti; Haitao Mi; Dongruo Zhou; Leoweiliang,2026-07-14,2026,arXiv,arXiv,,"29 pages, 6 figures. Project page: https://ruhan-wang.github.io/Harness-Handbook/",cs.AI,arxiv-api,,,,,,,2607.13285,2026-07-17T05:43:07+00:00
-ale-0159,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,MemoHarness: Agent Harnesses That Learn from Experience,https://arxiv.org/abs/2607.14159,external,arxiv.org,"Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.",Gives readers a concrete source in MemoHarness: Agent Harnesses That Learn from Experience when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2607.14159; inspect its method and evaluation before treating results as production evidence.,medium,README.md,620,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L620,2026-07-17,Design,design,Specify a loop contract and operating pattern.,context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14159,[2607.14159] MemoHarness: Agent Harnesses That Learn from Experience,"An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness into six editable control dimensions, stores per-case diagnoses and distilled global patterns in a dual-layer experience bank, and adapts the learned harness to each test case using retrieved experience without test-time labels, feedback, or additional search. In our evaluation across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness improves over the fixed harnesses we compare against and shows selective transfer to unseen suites and base models. Its additional context can also remain cost-competitive when much of the retrieved experience is cacheable. These results provide evidence that execution experience is a practical substrate for building agent harnesses that are more adaptive than a single static configuration, while leaving broader claims about statistical robustness and component attribution to future work.",Yue Huang; Wenjie Wang; Han Bao; Yuchen Ma; Xiaonan Luo; Yi Nian; Haomin Zhuang; Zheyuan Liu; Yue Zhao; Xiangliang Zhang,2026-07-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.14159,2026-07-17T05:43:07+00:00
-ale-0160,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,SWE-agent,https://github.com/SWE-agent/SWE-agent,external,github.com,Agent-computer interface and autonomous software engineering agent for repository tasks.,Agent-computer interface and autonomous software engineering agent for repository tasks.,Agent-computer interface and autonomous software engineering agent for repository tasks.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Agent-computer interface and autonomous software engineering agent for repository tasks.,Gives readers a concrete source in SWE-agent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (19,832 stars; 2,167 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,629,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L629,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/SWE-agent/SWE-agent,"GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] · GitHub","SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] - GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]",,2024-04-02,2024,SWE-agent/SWE-agent,GitHub,,,,github-api,SWE-agent/SWE-agent,19832,2167,MIT,2024-04-02T04:09:47Z,2026-07-17T04:00:43Z,,2026-07-17T05:43:07+00:00
-ale-0161,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,https://arxiv.org/abs/2405.15793,external,arxiv.org,Paper behind SWE-agent and its interface design.,Paper behind SWE-agent and its interface design.,Paper behind SWE-agent and its interface design.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Paper behind SWE-agent and its interface design.,Gives readers a concrete source in SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2405.15793; inspect its method and evaluation before treating results as production evidence.,medium,README.md,630,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L630,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2405.15793,[2405.15793] SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,"Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that LM agents represent a new category of end users with their own needs and abilities, and would benefit from specially-built interfaces to the software they use. We investigate how interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively, far exceeding the previous state-of-the-art achieved with non-interactive LMs. Finally, we provide insight on how the design of the ACI can impact agents' behavior and performance.",John Yang; Carlos E. Jimenez; Alexander Wettig; Kilian Lieret; Shunyu Yao; Karthik Narasimhan; Ofir Press,2024-05-06,2024,arXiv,arXiv,,"Code, data, and demo available at https://swe-agent.com",cs.SE,arxiv-api,,,,,,,2405.15793,2026-07-17T05:43:07+00:00
-ale-0162,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,mini-SWE-agent,https://mini-swe-agent.com/latest/,external,mini-swe-agent.com,Minimal coding agent that is useful for understanding the core loop without a large framework.,Minimal coding agent that is useful for understanding the core loop without a large framework.,Minimal coding agent that is useful for understanding the core loop without a large framework.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Minimal coding agent that is useful for understanding the core loop without a large framework.,Gives readers a concrete source in mini-SWE-agent when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,631,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L631,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,implementation,ok,https://mini-swe-agent.com/latest/,Overview - mini-SWE-agent documentation,,,,,,mini-swe-agent.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0163,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,OpenHands,https://github.com/All-Hands-AI/OpenHands,external,github.com,Open platform for AI software developers as generalist agents.,Open platform for AI software developers as generalist agents.,Open platform for AI software developers as generalist agents.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Open platform for AI software developers as generalist agents.,Gives readers a concrete source in OpenHands when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (81,043 stars; 10,361 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,632,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L632,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/OpenHands/OpenHands,GitHub - OpenHands/OpenHands: 🙌 OpenHands: AI-Driven Development · GitHub,🙌 OpenHands: AI-Driven Development. Contribute to OpenHands/OpenHands development by creating an account on GitHub.,,2024-03-13,2024,All-Hands-AI/OpenHands,GitHub,,,,github-api,All-Hands-AI/OpenHands,81043,10361,NOASSERTION,2024-03-13T03:33:31Z,2026-07-17T04:50:17Z,,2026-07-17T05:43:07+00:00
-ale-0164,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,OpenHands: An Open Platform for AI Software Developers as Generalist Agents,https://arxiv.org/abs/2407.16741,external,arxiv.org,"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.",Gives readers a concrete source in OpenHands: An Open Platform for AI Software Developers as Generalist Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2407.16741; inspect its method and evaluation before treating results as production evidence.,medium,README.md,633,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L633,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.16741,[2407.16741] OpenHands: An Open Platform for AI Software Developers as Generalist Agents,"Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. In this paper, we introduce OpenHands (f.k.a. OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, coordination between multiple agents, and incorporation of evaluation benchmarks. Based on our currently incorporated benchmarks, we perform an evaluation of agents over 15 challenging tasks, including software engineering (e.g., SWE-BENCH) and web browsing (e.g., WEBARENA), among others. Released under the permissive MIT license, OpenHands is a community project spanning academia and industry with more than 2.1K contributions from over 188 contributors.",Xingyao Wang; Boxuan Li; Yufan Song; Frank F. Xu; Xiangru Tang; Mingchen Zhuge; Jiayi Pan; Yueqi Song; Bowen Li; Jaskirat Singh; Hoang H. Tran; Fuqiang Li; Ren Ma; Mingzhang Zheng; Bill Qian; Yanjun Shao; Niklas Muennighoff; Yizhe Zhang; Binyuan Hui; Junyang Lin; Robert Brennan; Hao Peng; Heng Ji; Graham Neubig,2024-07-23,2024,arXiv,arXiv,,Accepted by ICLR 2025; Code: https://github.com/All-Hands-AI/OpenHands,cs.SE,arxiv-api,,,,,,,2407.16741,2026-07-17T05:43:07+00:00
-ale-0165,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Agentless,https://github.com/OpenAutoCoder/Agentless,external,github.com,"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Workflow-based approach for software issue resolution using localization, repair, and patch validation.",Gives readers a concrete source in Agentless when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,083 stars; 235 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,634,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L634,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/OpenAutoCoder/Agentless,GitHub - OpenAutoCoder/Agentless: Agentless🐱: an agentless approach to automatically solve software development problems · GitHub,Agentless🐱: an agentless approach to automatically solve software development problems - OpenAutoCoder/Agentless,,2024-06-30,2024,OpenAutoCoder/Agentless,GitHub,,,,github-api,OpenAutoCoder/Agentless,2083,235,MIT,2024-06-30T23:37:17Z,2026-07-16T06:42:42Z,,2026-07-17T05:43:07+00:00
-ale-0166,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Agentless: Demystifying LLM-based Software Engineering Agents,https://arxiv.org/abs/2407.01489,external,arxiv.org,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Gives readers a concrete source in Agentless: Demystifying LLM-based Software Engineering Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2407.01489; inspect its method and evaluation before treating results as production evidence.,medium,README.md,635,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L635,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.01489,[2407.01489] Agentless: Demystifying LLM-based Software Engineering Agents,"Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic three-phase process of localization, repair, and patch validation, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (32.00%, 96 correct fixes) and low cost ($0.70) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction.",Chunqiu Steven Xia; Yinlin Deng; Soren Dunn; Lingming Zhang,2024-07-01,2024,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2407.01489,2026-07-17T05:43:07+00:00
-ale-0167,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoCodeRover,https://github.com/AutoCodeRoverSG/auto-code-rover,external,github.com,"Autonomous program improvement system for issue localization, patch generation, and validation.","Autonomous program improvement system for issue localization, patch generation, and validation.","Autonomous program improvement system for issue localization, patch generation, and validation.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Autonomous program improvement system for issue localization, patch generation, and validation.",Gives readers a concrete source in AutoCodeRover when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,096 stars; 334 forks; NOASSERTION license; updated 2026-07-14); popularity is context, not proof of reliability.",medium,README.md,636,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L636,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/AutoCodeRoverSG/auto-code-rover,GitHub - AutoCodeRoverSG/auto-code-rover: A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. · GitHub,A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. - AutoCodeRoverSG/auto-code-rover,,2024-04-08,2024,AutoCodeRoverSG/auto-code-rover,GitHub,,,,github-api,AutoCodeRoverSG/auto-code-rover,3096,334,NOASSERTION,2024-04-08T05:34:14Z,2026-07-14T08:02:04Z,,2026-07-17T05:43:07+00:00
-ale-0168,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,AutoCodeRover: Autonomous Program Improvement,https://arxiv.org/abs/2404.05427,external,arxiv.org,Paper on autonomous code repair loops over real repositories.,Paper on autonomous code repair loops over real repositories.,Paper on autonomous code repair loops over real repositories.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Paper on autonomous code repair loops over real repositories.,Gives readers a concrete source in AutoCodeRover: Autonomous Program Improvement when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2404.05427; inspect its method and evaluation before treating results as production evidence.,medium,README.md,637,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L637,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2404.05427,[2404.05427] AutoCodeRover: Autonomous Program Improvement,"Researchers have made significant progress in automating the software development process in the past decades. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. bug fixing) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving GitHub issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM's understanding of the issue's root cause, and effectively retrieve a context via iterative search. The use of spectrum-based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on SWE-bench-lite (300 real-life GitHub issues) show increased efficacy in solving GitHub issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported SWE-agent. In addition, AutoCodeRover achieved this efficacy with significantly lower cost (on average, $0.43 USD), compared to other baselines. We posit that our workflow enables autonomous software engineering, where, in future, auto-generated code from LLMs can be autonomously improved.",Yuntong Zhang; Haifeng Ruan; Zhiyu Fan; Abhik Roychoudhury,2024-04-08,2024,arXiv,arXiv,,To appear in ISSTA 2024,cs.SE,arxiv-api,,,,,,,2404.05427,2026-07-17T05:43:07+00:00
-ale-0169,Coding-Agent Loop Systems,coding-agent-loop-systems,List,🧭,SWE-bench reading list,https://github.com/SWE-bench/reading-list,external,github.com,Maintained map of software engineering agent systems and related papers.,Maintained map of software engineering agent systems and related papers.,Maintained map of software engineering agent systems and related papers.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Maintained map of software engineering agent systems and related papers.,Gives readers a concrete source in SWE-bench reading list when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (15 stars; 4 forks; updated 2026-06-30); popularity is context, not proof of reliability.",medium,README.md,638,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L638,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,curated-index,ok,https://github.com/SWE-bench/reading-list,GitHub - SWE-bench/reading-list: Academic papers and works related to SWE-bench and SWE-agents · GitHub,Academic papers and works related to SWE-bench and SWE-agents - SWE-bench/reading-list,,2025-06-26,2025,SWE-bench/reading-list,GitHub,,,,github-api,SWE-bench/reading-list,15,4,,2025-06-26T22:03:02Z,2026-06-30T13:06:47Z,,2026-07-17T05:43:07+00:00
-ale-0170,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code,https://arxiv.org/abs/2602.06875,external,arxiv.org,"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.",Gives readers a concrete source in TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2602.06875; inspect its method and evaluation before treating results as production evidence.,medium,README.md,639,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L639,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2602.06875,[2602.06875] TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code,"Large Language Models (LLMs) often generate code with subtle but critical bugs, especially for complex tasks. Existing automated repair methods typically rely on superficial pass/fail signals, offering limited visibility into program behavior and hindering precise error localization. In addition, without a way to learn from prior failures, repair processes often fall into repetitive and inefficient cycles. To overcome these challenges, we present TraceCoder, a collaborative multi-agent framework that emulates the observe-analyze-repair process of human experts. The framework first instruments the code with diagnostic probes to capture fine-grained runtime traces, enabling deep insight into its internal execution. It then conducts causal analysis on these traces to accurately identify the root cause of the failure. This process is further enhanced by a novel Historical Lesson Learning Mechanism (HLLM), which distills insights from prior failed repair attempts to inform subsequent correction strategies and prevent recurrence of similar mistakes. To ensure stable convergence, a Rollback Mechanism enforces that each repair iteration constitutes a strict improvement toward the correct solution. Comprehensive experiments across multiple benchmarks show that TraceCoder achieves up to a 34.43\% relative improvement in Pass@1 accuracy over existing advanced baselines. Ablation studies verify the significance of each system component, with the iterative repair process alone contributing a 65.61\% relative gain in accuracy. Furthermore, TraceCoder significantly outperforms leading iterative methods in terms of both accuracy and cost-efficiency.",Jiangping Huang; Wenguang Ye; Weisong Sun; Jian Zhang; Mingyue Zhang; Yang Liu,2026-02-06,2026,arXiv,arXiv,10.1145/3744916.3773187,,cs.SE,arxiv-api,,,,,,,2602.06875,2026-07-17T05:43:07+00:00
-ale-0171,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase,https://arxiv.org/abs/2603.25697,external,arxiv.org,"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.",Gives readers a concrete source in The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.25697; inspect its method and evaluation before treating results as production evidence.,medium,README.md,640,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L640,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.25697,[2603.25697] The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase,"Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User x 1000', where an LLM agent exercises that surface as a synthetic power user at 1,000x human cadence; (3) Unbeatable Tests, ground-truth verification the code author cannot fake; and (4) Drift Control, continuous quality measurement with automated pause gates. We validate across two production systems over 285+ iterations, producing 1,094+ merged pull requests with zero regressions detected by the regression oracle (methodology in Section 6.1). We observe emergent properties at scale: multi-iteration self-correction chains, autonomous infrastructure healing, and monotonically improving quality gates. The primitives are not new; our contribution is their composition into a production-tested system with the operational discipline that makes long-running autonomous evolution safe.",Yannick Roy,2026-03-26,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2603.25697,2026-07-17T05:43:07+00:00
-ale-0172,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures,https://arxiv.org/abs/2604.03515,external,arxiv.org,"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.",Gives readers a concrete source in Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2604.03515; inspect its method and evaluation before treating results as production evidence.,medium,README.md,641,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L641,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification;state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.03515,[2604.03515] Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures,"LLM-based coding agents can localize bugs, generate patches, and run tests with diminishing human oversight, yet the scaffolding code that surrounds the language model (the control loop, tool definitions, state management, and context strategy) remains poorly understood. Existing surveys classify agents by abstract capabilities (tool use, planning, reflection) that cannot distinguish between architecturally distinct systems, and trajectory studies observe what agents do without examining the scaffold code that determines why. This paper presents a source-code-level architectural taxonomy derived from analysis of 13 open-source coding agent scaffolds at pinned commit hashes. Each agent is characterized across 12 dimensions organized into three layers: control architecture, tool and environment interface, and resource management. The analysis reveals that scaffold architectures resist discrete classification: control strategies range from fixed pipelines to Monte Carlo Tree Search, tool counts range from 0 to 37, and context compaction spans seven distinct strategies. Five loop primitives (ReAct, generate-test-repair, plan-execute, multi-attempt retry, tree search) function as composable building blocks that agents layer in different combinations; 11 of 13 agents compose multiple primitives rather than relying on a single control structure. Dimensions converge where external constraints dominate (tool capability categories, edit formats, execution isolation) and diverge where open design questions remain (context compaction, state management, multi-model routing). All taxonomic claims are grounded in file paths and line numbers, providing a reusable reference for researchers studying agent behavior and practitioners designing new scaffolds.",Benjamin Rombaut,2026-04-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2604.03515,2026-07-17T05:43:07+00:00
-ale-0173,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,A Self-Improving Coding Agent,https://arxiv.org/abs/2504.15228,external,arxiv.org,"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.",Gives readers a concrete source in A Self-Improving Coding Agent when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2504.15228; inspect its method and evaluation before treating results as production evidence.,medium,README.md,642,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L642,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2504.15228,[2504.15228] A Self-Improving Coding Agent,"Recent advancements in Large Language Models (LLMs) have spurred interest in deploying LLM agents to undertake tasks in the world. LLMs are often deployed in agent systems: code that orchestrates LLM calls and provides them with tools. We demonstrate that an agent system, equipped with basic coding tools, can autonomously edit itself, and thereby improve its performance on benchmark tasks. We find performance gains from 17% to 53% on a random subset of SWE Bench Verified, with additional performance gains on LiveCodeBench, as well as synthetically generated agent benchmarks. Our work represents an advancement in the automated and open-ended design of agentic systems, and demonstrates a data-efficient, non gradient-based learning mechanism driven by LLM reflection and code updates.",Maxime Robeyns; Martin Szummer; Laurence Aitchison,2025-04-21,2025,arXiv,arXiv,,Submitted as a preprint to NeurIPS 2025,cs.AI,arxiv-api,,,,,,,2504.15228,2026-07-17T05:43:07+00:00
-ale-0174,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,https://arxiv.org/abs/2607.03691,external,arxiv.org,"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.",Gives readers a concrete source in Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.03691; inspect its method and evaluation before treating results as production evidence.,medium,README.md,643,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L643,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03691,[2607.03691] Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,"Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these scaffoldings evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after scaffolding updates, yet consistently attribute them to the underlying model rather than the scaffolding itself. In this paper, we address this gap by conducting the first controlled longitudinal study that isolates the scaffolding's contribution. Unlike prior work that fixes the scaffolding and varies the model, we fix the model and vary only the scaffolding, evaluating 35 sequential releases to measure their impact on agent effectiveness and efficiency. We first empirically study the development and release evolution of five major open-source scaffoldings (i.e., Codex, Qwen Code, Gemini, OpenCode, and OpenHands), revealing extreme release velocities exceeding two releases per day and thousands of issues within months. We then perform a controlled deep dive into 35 sequential releases of the Qwen Code CLI, evaluating each against 50 stratified SWE-bench Verified tasks while holding the underlying LLM constant. We trace the resulting quality fluctuations to specific development patterns and architectural components, and illustrate our findings with concrete qualitative evidence linking individual pull requests to measured quality shifts.",Oussama Ben Sghaier; Hao Li; Bram Adams; Ahmed E. Hassan,2026-07-04,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.03691,2026-07-17T05:43:07+00:00
-ale-0175,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,"ToFu: A White-Box, Token-Efficient Agent Harness for Researchers",https://arxiv.org/abs/2607.11423,external,arxiv.org,"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","Gives readers a concrete source in ToFu: A White-Box, Token-Efficient Agent Harness for Researchers when they need to choose an implementation surface for repeatable agent work.",Research source arXiv:2607.11423; inspect its method and evaluation before treating results as production evidence.,medium,README.md,644,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L644,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11423,"[2607.11423] ToFu: A White-Box, Token-Efficient Agent Harness for Researchers","Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent's behavior. We present ToFu, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools. ToFu plays a dual role in research. As a research assistant, it supports practical research workflows with superior token efficiency, lower cost, and multilingual capability compared with existing agentic harnesses. Its release under the MIT License further enables local deployment for privacy-sensitive users. As a research object, ToFu provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design, while retaining strong benchmark performance and an application-level user experience.",Junhao Ruan; Yuan Ge; Bei Li; Yongjing Yin; Yuchun Fan; Xin Chen; Jingang Wang; Chenglong Wang; Jingbo Zhu; Tong Xiao,2026-07-13,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.11423,2026-07-17T05:43:07+00:00
-ale-0176,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,When Does Restricting a Coding Agent to execute_code Help?,https://arxiv.org/abs/2607.10569,external,arxiv.org,"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.",Gives readers a concrete source in When Does Restricting a Coding Agent to execute_code Help? when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.10569; inspect its method and evaluation before treating results as production evidence.,medium,README.md,645,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L645,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10569,[2607.10569] When Does Restricting a Coding Agent to execute_code Help? A Regime $\times$ Agent-Design Ablation,"Modern coding agents expose multiple tool surfaces -- IDE primitives, bash, and Model Context Protocol (MCP) code-execution -- and the field has shipped three contradictory claims about which one matters. We run the missing crossed comparison: an integrity-clean three-arm ablation (baseline / bash_only / code_only) on synthetic computation tasks and SWE-bench Mini modification tasks, holding model, harness, and prompts fixed, with two agents (Claude Code, OpenAI Codex CLI) so the comparison spans both regime and agent-design axes. Across the four resulting (regime, agent) cells, restricting the agent to a single execute_code MCP tool is cheaper than -- or statistically tied with -- its cheapest tool-rich rival in three cells (significantly on Artifact/Claude and SWE-bench/Codex; directionally on Artifact/Codex), with pass rates statistically tied within each cell. The lone exception is SWE-bench/Claude, where code_only is directionally costlier (+14.4%, not significant); a conditional-cost analysis localizes that gap to failure-cost on doomed-run trajectories, not a per-edit tax on successful runs. Two implications: the cheapest tool surface is jointly determined by task regime and agent design rather than by either axis alone, and the headline cost signal lives in cache-adjusted cost -- not pass rate, which is invariant across surfaces at the model sizes we evaluate. The benchmark harness, task suite, and analysis code are available at https://github.com/hyang0129/onlycodes.",Hong Yang; Qi Yu; Travis Desell,2026-07-12,2026,arXiv,arXiv,,"9 pages (excluding references), 4 figures, 4 tables. Accepted to the Agentic Software Engineering (SE 3.0) Workshop at KDD 2026 (non-archival)",cs.SE,arxiv-api,,,,,,,2607.10569,2026-07-17T05:43:07+00:00
-ale-0177,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Ralph,https://ghuntley.com/ralph/,external,ghuntley.com,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Gives readers a concrete source in Ralph when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,650,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L650,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://ghuntley.com/ralph/,"Ralph Wiggum as a ""software engineer""","How Ralph Wiggum went from 'The Simpsons' to the biggest name in AI right now - Venture Beat 😎Here's a cool little field report from a Y Combinator hackathon event where they put Ralph Wiggum to the test. ""We Put a Coding Agent in a While Loop and It Shipped",,2025-07-14,2025,,Geoffrey Huntley,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0178,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,everything is a ralph loop,https://ghuntley.com/loop/,external,ghuntley.com,"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.",Gives readers a concrete source in everything is a ralph loop when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,651,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L651,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,operational-pattern,ok,https://ghuntley.com/loop/,everything is a ralph loop,"I’ve been thinking about how I build software is so very very different how I used to do it three years ago. No, I’m not talking about acceleration through usage of AI but instead at a more fundamental level of approach, techniques and best practices. Standard software practices",,2026-01-17,2026,,Geoffrey Huntley,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0179,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,how-to-ralph-wiggum,https://github.com/ghuntley/how-to-ralph-wiggum,external,github.com,"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.",Gives readers a concrete source in how-to-ralph-wiggum when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,718 stars; 145 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,652,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L652,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/ghuntley/how-to-ralph-wiggum,GitHub - ghuntley/how-to-ralph-wiggum: The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. · GitHub,The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. - ghuntley/how-to-ralph-wiggum,,2026-01-10,2026,ghuntley/how-to-ralph-wiggum,GitHub,,,,github-api,ghuntley/how-to-ralph-wiggum,1718,145,,2026-01-10T04:32:01Z,2026-07-17T03:55:55Z,,2026-07-17T05:43:07+00:00
-ale-0180,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,A Brief History of Ralph,https://www.humanlayer.dev/blog/brief-history-of-ralph,external,www.humanlayer.dev,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Gives readers a concrete source in A Brief History of Ralph when they need to choose an implementation surface for repeatable agent work.,"Contextual source from www.humanlayer.dev; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,653,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L653,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,practitioner-analysis,ok,https://www.humanlayer.dev/blog/brief-history-of-ralph,A Brief History of Ralph | HumanLayer Blog,The Ralph Wiggum Technique went viral in the last week of 2025. Here's the story of ralph since the first time I met Geoff in June of 2025.,,2026,2026,,humanlayer.dev,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0181,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Ralph Copilot,https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf,external,github.com,"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.",Gives readers a concrete source in Ralph Copilot when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (138 stars; 16 forks; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,654,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L654,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf,GitHub - giocaizzi/ralph-copilot at e5b2813cc876c73a8c9d3398c0115da0d15f63cf · GitHub,Copilot implementation of Ralph loop. Contribute to giocaizzi/ralph-copilot development by creating an account on GitHub.,,2026-01-30,2026,giocaizzi/ralph-copilot,GitHub,,,,github-api,giocaizzi/ralph-copilot,138,16,,2026-01-30T11:25:36Z,2026-07-16T14:18:20Z,,2026-07-17T05:43:07+00:00
-ale-0182,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Ralph (snarktank),https://github.com/snarktank/ralph,external,github.com,"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.",Gives readers a concrete source in Ralph (snarktank) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (21,102 stars; 2,040 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,655,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L655,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state,builder,source-implementation,ok,https://github.com/snarktank/ralph,GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. · GitHub,Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. - GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.,,2026-01-07,2026,snarktank/ralph,GitHub,,,,github-api,snarktank/ralph,21102,2040,MIT,2026-01-07T14:45:10Z,2026-07-17T04:08:27Z,,2026-07-17T05:43:07+00:00
-ale-0183,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-claude-code,https://github.com/frankbria/ralph-claude-code,external,github.com,"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.",Gives readers a concrete source in ralph-claude-code when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (9,544 stars; 727 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,656,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L656,,Build,build,"Choose runtimes, tools, and delegation surfaces.",exit,builder,source-implementation,ok,https://github.com/frankbria/ralph-claude-code,GitHub - frankbria/ralph-claude-code: Autonomous AI development loop for Claude Code with intelligent exit detection · GitHub,Autonomous AI development loop for Claude Code with intelligent exit detection - frankbria/ralph-claude-code,,2025-08-27,2025,frankbria/ralph-claude-code,GitHub,,,,github-api,frankbria/ralph-claude-code,9544,727,MIT,2025-08-27T16:03:45Z,2026-07-17T00:57:55Z,,2026-07-17T05:43:07+00:00
-ale-0184,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-orchestrator,https://github.com/mikeyobrien/ralph-orchestrator,external,github.com,"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.",Gives readers a concrete source in ralph-orchestrator when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,007 stars; 281 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,657,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L657,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;escalation;exit,builder,source-implementation,ok,https://github.com/mikeyobrien/ralph-orchestrator,GitHub - mikeyobrien/ralph-orchestrator: An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration · GitHub,An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration - mikeyobrien/ralph-orchestrator,,2025-09-07,2025,mikeyobrien/ralph-orchestrator,GitHub,,,,github-api,mikeyobrien/ralph-orchestrator,3007,281,MIT,2025-09-07T18:17:13Z,2026-07-17T02:05:04Z,,2026-07-17T05:43:07+00:00
-ale-0185,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralphex,https://github.com/umputun/ralphex,external,github.com,"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.",Gives readers a concrete source in ralphex when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,381 stars; 110 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,658,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L658,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/umputun/ralphex,GitHub - umputun/ralphex: Extended Ralph loop for autonomous AI-driven plan execution · GitHub,Extended Ralph loop for autonomous AI-driven plan execution - umputun/ralphex,,2026-01-19,2026,umputun/ralphex,GitHub,,,,github-api,umputun/ralphex,1381,110,MIT,2026-01-19T22:44:35Z,2026-07-16T18:57:15Z,,2026-07-17T05:43:07+00:00
-ale-0186,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph (iannuttall),https://github.com/iannuttall/ralph,external,github.com,"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.",Gives readers a concrete source in ralph (iannuttall) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (932 stars; 90 forks; updated 2026-07-11); popularity is context, not proof of reliability.",medium,README.md,659,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L659,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;state,builder,source-implementation,ok,https://github.com/iannuttall/ralph,"GitHub - iannuttall/ralph: A minimal, file‑based agent loop for autonomous coding. · GitHub","A minimal, file‑based agent loop for autonomous coding. - iannuttall/ralph",,2026-01-12,2026,iannuttall/ralph,GitHub,,,,github-api,iannuttall/ralph,932,90,,2026-01-12T15:31:06Z,2026-07-11T16:27:54Z,,2026-07-17T05:43:07+00:00
-ale-0187,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-loop-agent,https://github.com/vercel-labs/ralph-loop-agent,external,github.com,"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.",Gives readers a concrete source in ralph-loop-agent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (821 stars; 85 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,660,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L660,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;budget;exit,builder,source-implementation,ok,https://github.com/vercel-labs/ralph-loop-agent,GitHub - vercel-labs/ralph-loop-agent: Continuous Autonomy for the AI SDK · GitHub,Continuous Autonomy for the AI SDK. Contribute to vercel-labs/ralph-loop-agent development by creating an account on GitHub.,,2026-01-03,2026,vercel-labs/ralph-loop-agent,GitHub,,,,github-api,vercel-labs/ralph-loop-agent,821,85,Apache-2.0,2026-01-03T21:18:55Z,2026-07-16T19:00:19Z,,2026-07-17T05:43:07+00:00
-ale-0188,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Open Ralph Wiggum,https://github.com/Th0rgal/open-ralph-wiggum,external,github.com,"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.",Gives readers a concrete source in Open Ralph Wiggum when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,841 stars; 141 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,661,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L661,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/Th0rgal/open-ralph-wiggum,"GitHub - Th0rgal/open-ralph-wiggum: Type `ralph ""prompt""` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot · GitHub","Type `ralph ""prompt""` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot - Th0rgal/open-ralph-wiggum",,2026-01-06,2026,Th0rgal/open-ralph-wiggum,GitHub,,,,github-api,Th0rgal/open-ralph-wiggum,1841,141,MIT,2026-01-06T08:06:47Z,2026-07-17T05:05:05Z,,2026-07-17T05:43:07+00:00
-ale-0189,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Compound Engineering,https://every.to/guides/compound-engineering,external,every.to,"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.",Gives readers a concrete source in Compound Engineering when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,666,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L666,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://every.to/guides/compound-engineering,Compound Engineering - Every,The AI-native engineering philosophy,,,,,every.to,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0190,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Gas Town,https://github.com/steveyegge/gastown,external,github.com,"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.",Gives readers a concrete source in Gas Town when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (17,063 stars; 1,569 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,667,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L667,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,source-implementation,ok,https://github.com/gastownhall/gastown,GitHub - gastownhall/gastown: Gas Town - multi-agent workspace manager · GitHub,Gas Town - multi-agent workspace manager. Contribute to gastownhall/gastown development by creating an account on GitHub.,,2025-12-16,2025,steveyegge/gastown,GitHub,,,,github-api,steveyegge/gastown,17063,1569,MIT,2025-12-16T00:33:33Z,2026-07-17T04:12:15Z,,2026-07-17T05:43:07+00:00
-ale-0191,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Amp,https://ampcode.com/,external,ampcode.com,"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.",Gives readers a concrete source in Amp when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,668,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L668,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation,builder,implementation,ok,https://ampcode.com/,Amp,Amp is a frontier coding agent that lets you wield the full power of leading models.,,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0192,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,karl,https://github.com/kayoslab/karl,external,github.com,"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.",Gives readers a concrete source in karl when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (0 stars; 0 forks; MIT license; updated 2026-04-08); popularity is context, not proof of reliability.",medium,README.md,669,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L669,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;budget,builder,source-implementation,ok,https://github.com/kayoslab/karl,GitHub - kayoslab/karl: Autonomous multi-agent development loop · GitHub,Autonomous multi-agent development loop. Contribute to kayoslab/karl development by creating an account on GitHub.,,2026-03-10,2026,kayoslab/karl,GitHub,,,,github-api,kayoslab/karl,0,0,MIT,2026-03-10T09:50:32Z,2026-04-08T07:56:55Z,,2026-07-17T05:43:07+00:00
-ale-0193,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,joelclaw agent-loop skill,https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md,external,github.com,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Gives readers a concrete source in joelclaw agent-loop skill when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (60 stars; 3 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,670,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L670,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,operational-pattern,ok,https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md,joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw · GitHub,"Personal AI operating system — blog, architecture decisions, and the journey from zero to a composable agent system. - joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw",,2026-02-14,2026,joelhooks/joelclaw,GitHub,,,,github-api,joelhooks/joelclaw,60,3,,2026-02-14T22:54:08Z,2026-07-17T04:17:51Z,,2026-07-17T05:43:07+00:00
-ale-0194,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ARIS (Auto-Research-In-Sleep),https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep,external,github.com,"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.",Gives readers a concrete source in ARIS (Auto-Research-In-Sleep) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (13,501 stars; 1,218 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,671,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L671,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;verification,builder,source-implementation,ok,https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep,"GitHub - wanshuiyin/Auto-claude-code-research-in-sleep: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. · GitHub","ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. - wanshuiyin/Auto-claude-code-research-in-sleep",,2026-03-10,2026,wanshuiyin/Auto-claude-code-research-in-sleep,GitHub,,,,github-api,wanshuiyin/Auto-claude-code-research-in-sleep,13501,1218,MIT,2026-03-10T07:31:45Z,2026-07-17T05:25:23Z,,2026-07-17T05:43:07+00:00
-ale-0195,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoAgent,https://github.com/kevinrgu/autoagent,external,github.com,"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","The work turns loop quality into a measurable task or score. Resource-specific angle: Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.",Gives readers a concrete source in AutoAgent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (4,543 stars; 498 forks; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,672,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L672,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification,builder,source-implementation,ok,https://github.com/kevinrgu/autoagent,GitHub - kevinrgu/autoagent: autonomous harness engineering · GitHub,autonomous harness engineering. Contribute to kevinrgu/autoagent development by creating an account on GitHub.,,2026-04-02,2026,kevinrgu/autoagent,GitHub,,,,github-api,kevinrgu/autoagent,4543,498,,2026-04-02T20:44:58Z,2026-07-16T09:09:57Z,,2026-07-17T05:43:07+00:00
-ale-0196,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,zeroshot,https://github.com/the-open-engine/zeroshot,external,github.com,"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.",Gives readers a concrete source in zeroshot when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,647 stars; 140 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,673,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L673,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/the-open-engine/zeroshot,"GitHub - the-open-engine/zeroshot: Your autonomous engineering team in a CLI. The agent loop produces senior-level code that you can actually trust in prod because of non-negotiable feedback from independent reviewers. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI with trivial setup. · GitHub","Your autonomous engineering team in a CLI. The agent loop produces senior-level code that you can actually trust in prod because of non-negotiable feedback from independent reviewers. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI with trivial setup. - the-open-engine/zeroshot",,2025-12-25,2025,the-open-engine/zeroshot,GitHub,,,,github-api,the-open-engine/zeroshot,1647,140,MIT,2025-12-25T17:12:45Z,2026-07-16T20:07:25Z,,2026-07-17T05:43:07+00:00
-ale-0197,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Loki Mode,https://github.com/asklokesh/loki-mode,external,github.com,"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.",Gives readers a concrete source in Loki Mode when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,018 stars; 200 forks; NOASSERTION license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,674,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L674,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state;exit,builder,source-implementation,ok,https://github.com/asklokesh/loki-mode,"GitHub - asklokesh/loki-mode: Multi-agent autonomous SDLC framework. Spec to deployed app. PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. 5 AI providers, 8 quality gates. · GitHub","Multi-agent autonomous SDLC framework. Spec to deployed app. PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. 5 AI providers, 8 quality gates. - asklokesh/loki-mode",,2025-12-26,2025,asklokesh/loki-mode,GitHub,,,,github-api,asklokesh/loki-mode,1018,200,NOASSERTION,2025-12-26T16:26:14Z,2026-07-15T03:59:38Z,,2026-07-17T05:43:07+00:00
-ale-0198,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Looper,https://github.com/ksimback/looper,external,github.com,"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.",Gives readers a concrete source in Looper when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (684 stars; 61 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,675,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L675,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;verification;budget,builder,source-implementation,ok,https://github.com/ksimback/looper,"GitHub - ksimback/looper: Design visual, review-gated agent loops for Claude Code before you run them. · GitHub","Design visual, review-gated agent loops for Claude Code before you run them. - ksimback/looper",,2026-06-18,2026,ksimback/looper,GitHub,,,,github-api,ksimback/looper,684,61,MIT,2026-06-18T19:44:20Z,2026-07-16T17:18:02Z,,2026-07-17T05:43:07+00:00
-ale-0199,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Agent Apprenticeship,https://github.com/Forsy-AI/agent-apprenticeship,external,github.com,"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.",Gives readers a concrete source in Agent Apprenticeship when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,317 stars; 56 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,676,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L676,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/Forsy-AI/agent-apprenticeship,"GitHub - Forsy-AI/agent-apprenticeship: The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents. · GitHub","The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents. - Forsy-AI/agent-apprenticeship",,2026-06-19,2026,Forsy-AI/agent-apprenticeship,GitHub,,,,github-api,Forsy-AI/agent-apprenticeship,1317,56,MIT,2026-06-19T16:19:37Z,2026-07-17T00:34:53Z,,2026-07-17T05:43:07+00:00
-ale-0200,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Scholar Loop,https://github.com/renee-jia/scholar-loop,external,github.com,"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.",Gives readers a concrete source in Scholar Loop when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (461 stars; 35 forks; MIT license; updated 2026-07-08); popularity is context, not proof of reliability.",medium,README.md,677,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L677,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/renee-jia/scholar-loop,"GitHub - renee-jia/scholar-loop: An autonomous AI scientist: a multi-agent loop over literature, experiments, self-critique and write-up, with deterministic guards against reward-hacking and hallucination. · GitHub","An autonomous AI scientist: a multi-agent loop over literature, experiments, self-critique and write-up, with deterministic guards against reward-hacking and hallucination. - renee-jia/scholar-loop",,2026-06-15,2026,renee-jia/scholar-loop,GitHub,,,,github-api,renee-jia/scholar-loop,461,35,MIT,2026-06-15T04:50:58Z,2026-07-08T05:18:54Z,,2026-07-17T05:43:07+00:00
-ale-0201,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,loop-engineering (Cobus Greyling),https://github.com/cobusgreyling/loop-engineering,external,github.com,"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.",Gives readers a concrete source in loop-engineering (Cobus Greyling) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (8,175 stars; 1,071 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,678,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L678,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace;context;state;budget,builder,source-implementation,ok,https://github.com/cobusgreyling/loop-engineering,"GitHub - cobusgreyling/loop-engineering: Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost. · GitHub","Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost. - cobusgreyling/loop-engineering",,2026-06-09,2026,cobusgreyling/loop-engineering,GitHub,,,,github-api,cobusgreyling/loop-engineering,8175,1071,MIT,2026-06-09T06:28:20Z,2026-07-17T05:43:53Z,,2026-07-17T05:43:07+00:00
-ale-0202,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoCVE,https://github.com/larlarua/AutoCVE,external,github.com,"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.",Gives readers a concrete source in AutoCVE when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,289 stars; 83 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,679,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L679,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation;verification;exit,builder,source-implementation,ok,https://github.com/larlarua/AutoCVE,"GitHub - larlarua/AutoCVE: Agent-driven automated CVE discovery platform for source code auditing, vulnerability verification, and report generation. · GitHub","Agent-driven automated CVE discovery platform for source code auditing, vulnerability verification, and report generation. - larlarua/AutoCVE",,2026-06-15,2026,larlarua/AutoCVE,GitHub,,,,github-api,larlarua/AutoCVE,1289,83,AGPL-3.0,2026-06-15T14:50:47Z,2026-07-17T04:42:55Z,,2026-07-17T05:43:07+00:00
-ale-0203,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,LoongFlow (Baidu),https://github.com/baidu-baige/LoongFlow,external,github.com,"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).",Gives readers a concrete source in LoongFlow (Baidu) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (452 stars; 51 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,680,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L680,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/baidu-baige/LoongFlow,"GitHub - baidu-baige/LoongFlow: LoongFlow is an expert-grade Agent framework for Loop Engineering. Through a Plan-Execute-Summary loop and structured experiential memory, it enables AI to continuously think, execute, reflect, and evolve across complex software engineering, mathematical, and machine learning tasks. · GitHub","LoongFlow is an expert-grade Agent framework for Loop Engineering. Through a Plan-Execute-Summary loop and structured experiential memory, it enables AI to continuously think, execute, reflect, and evolve across complex software engineering, mathematical, and machine learning tasks. - baidu-baige/LoongFlow",,2025-12-31,2025,baidu-baige/LoongFlow,GitHub,,,,github-api,baidu-baige/LoongFlow,452,51,Apache-2.0,2025-12-31T05:36:46Z,2026-07-17T02:37:11Z,,2026-07-17T05:43:07+00:00
-ale-0204,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,cc10x,https://github.com/romiluz13/cc10x,external,github.com,"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.",Gives readers a concrete source in cc10x when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (153 stars; 24 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,681,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L681,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,source-implementation,ok,https://github.com/romiluz13/cc10x,"GitHub - romiluz13/cc10x: The Loop Engine for Claude Code — engineer the loop, not the prompt. 1 router · 9 agents · 16 skills · 4 workflows. Fail-closed gates, test honesty, anti-anchored review. · GitHub","The Loop Engine for Claude Code — engineer the loop, not the prompt. 1 router · 9 agents · 16 skills · 4 workflows. Fail-closed gates, test honesty, anti-anchored review. - romiluz13/cc10x",,2025-10-22,2025,romiluz13/cc10x,GitHub,,,,github-api,romiluz13/cc10x,153,24,MIT,2025-10-22T11:54:14Z,2026-07-16T06:46:07Z,,2026-07-17T05:43:07+00:00
-ale-0205,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,RigorLoop,https://github.com/ronikobrosly/RigorLoop,external,github.com,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Gives readers a concrete source in RigorLoop when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (134 stars; 0 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,682,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L682,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/ronikobrosly/RigorLoop,"GitHub - ronikobrosly/RigorLoop: A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting. · GitHub","A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting. - GitHub - ronikobrosly/RigorLoop: A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting.",,2026-07-08,2026,ronikobrosly/RigorLoop,GitHub,,,,github-api,ronikobrosly/RigorLoop,134,0,MIT,2026-07-08T14:38:05Z,2026-07-17T00:53:18Z,,2026-07-17T05:43:07+00:00
-ale-0206,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Open-Inspect,https://github.com/ColeMurray/background-agents,external,github.com,"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.",Gives readers a concrete source in Open-Inspect when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,489 stars; 354 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,683,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L683,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,source-implementation,ok,https://github.com/ColeMurray/background-agents,GitHub - ColeMurray/background-agents: An open-source background agents coding system · GitHub,An open-source background agents coding system. Contribute to ColeMurray/background-agents development by creating an account on GitHub.,,2026-01-25,2026,ColeMurray/background-agents,GitHub,,,,github-api,ColeMurray/background-agents,2489,354,MIT,2026-01-25T18:45:47Z,2026-07-17T05:19:45Z,,2026-07-17T05:43:07+00:00
-ale-0207,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,T3MP3ST,https://github.com/elder-plinius/T3MP3ST,external,github.com,"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.",Gives readers a concrete source in T3MP3ST when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (4,851 stars; 1,017 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,684,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L684,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;state,builder,source-implementation,ok,https://github.com/elder-plinius/T3MP3ST,GitHub - elder-plinius/T3MP3ST: autonomous red teaming platform; multi-agent offensive-security meta-harness · GitHub,autonomous red teaming platform; multi-agent offensive-security meta-harness - elder-plinius/T3MP3ST,,2026-07-02,2026,elder-plinius/T3MP3ST,GitHub,,,,github-api,elder-plinius/T3MP3ST,4851,1017,AGPL-3.0,2026-07-02T17:53:55Z,2026-07-17T05:42:14Z,,2026-07-17T05:43:07+00:00
-ale-0208,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Loom,https://github.com/valkor-ai/loom,external,github.com,"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.",Gives readers a concrete source in Loom when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (579 stars; 62 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,685,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L685,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state,builder,source-implementation,ok,https://github.com/valkor-ai/loom,GitHub - valkor-ai/loom: Loop engineering for agentic software delivery. · GitHub,Loop engineering for agentic software delivery. Contribute to valkor-ai/loom development by creating an account on GitHub.,,2026-06-09,2026,valkor-ai/loom,GitHub,,,,github-api,valkor-ai/loom,579,62,Apache-2.0,2026-06-09T03:46:34Z,2026-07-17T01:09:37Z,,2026-07-17T05:43:07+00:00
-ale-0209,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Inferoa,https://github.com/agentic-in/inferoa,external,github.com,"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.",Gives readers a concrete source in Inferoa when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (486 stars; 83 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,686,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L686,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/agentic-in/inferoa,GitHub - agentic-in/inferoa: Inference-native Tokenmaxxing Agent Harness for Loop Engineering · GitHub,Inference-native Tokenmaxxing Agent Harness for Loop Engineering - agentic-in/inferoa,,2026-06-08,2026,agentic-in/inferoa,GitHub,,,,github-api,agentic-in/inferoa,486,83,Apache-2.0,2026-06-08T06:29:56Z,2026-07-16T10:37:09Z,,2026-07-17T05:43:07+00:00
-ale-0210,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,PlanWeave,https://github.com/GaosCode/PlanWeave,external,github.com,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,Gives readers a concrete source in PlanWeave when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (221 stars; 12 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,687,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L687,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/GaosCode/PlanWeave,"GitHub - GaosCode/PlanWeave: PlanWeave is a file-backed loop engineering system for long-running coding agents. It turns fuzzy plans into claimable tasks, routes them through implementation and review agents, records every run, and keeps the loop recoverable. · GitHub","PlanWeave is a file-backed loop engineering system for long-running coding agents. It turns fuzzy plans into claimable tasks, routes them through implementation and review agents, records every run, and keeps the loop recoverable. - GaosCode/PlanWeave",,2026-05-24,2026,GaosCode/PlanWeave,GitHub,,,,github-api,GaosCode/PlanWeave,221,12,MIT,2026-05-24T06:01:56Z,2026-07-17T03:36:41Z,,2026-07-17T05:43:07+00:00
-ale-0211,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,loop.js,https://github.com/loop-js/loop.js,external,github.com,"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.",Gives readers a concrete source in loop.js when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (123 stars; 0 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,688,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L688,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;budget,builder,source-implementation,ok,https://github.com/loop-js/loop.js,"GitHub - loop-js/loop.js: A loop engineering framework — state a Goal; Rounds run until a skeptical, read-only Verify agent settles it. · GitHub","A loop engineering framework — state a Goal; Rounds run until a skeptical, read-only Verify agent settles it. - loop-js/loop.js",,2026-07-10,2026,loop-js/loop.js,GitHub,,,,github-api,loop-js/loop.js,123,0,Apache-2.0,2026-07-10T10:59:54Z,2026-07-17T01:14:01Z,,2026-07-17T05:43:07+00:00
-ale-0212,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ai-trains-ai,https://github.com/Danau5tin/ai-trains-ai,external,github.com,"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.",Gives readers a concrete source in ai-trains-ai when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (181 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,689,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L689,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/Danau5tin/ai-trains-ai,GitHub - Danau5tin/ai-trains-ai: RL-training an AI agent to RL-train AI agents. · GitHub,RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.,,2026-07-07,2026,Danau5tin/ai-trains-ai,GitHub,,,,github-api,Danau5tin/ai-trains-ai,181,14,MIT,2026-07-07T07:45:52Z,2026-07-17T04:48:55Z,,2026-07-17T05:43:07+00:00
-ale-0213,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Factory 2.0: From Coding Agents to Software Factories,https://factory.ai/news/software-factory,external,factory.ai,"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.",Gives readers a concrete source in Factory 2.0: From Coding Agents to Software Factories when they need to choose an implementation surface for repeatable agent work.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,694,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L694,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;context;delegation;state,builder,practitioner-analysis,ok,https://factory.ai/news/software-factory,Factory 2.0: From coding agents to software factories | Factory.ai Factory.ai Logo Arrow Right Icon,"In 2023, we launched Factory with the mission to bring autonomy to software engineering. While others were using models...",Factory,2026-06-15,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0214,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Superpowers 6,https://blog.fsck.com/2026/06/15/Superpowers-6/,external,blog.fsck.com,"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.",Gives readers a concrete source in Superpowers 6 when they need to choose an implementation surface for repeatable agent work.,"Contextual source from blog.fsck.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,695,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L695,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;budget,builder,practitioner-analysis,ok,https://blog.fsck.com/2026/06/15/Superpowers-6/,Superpowers 6 — Massively Parallel Procrastination,"I'm Jesse. I make stuff. Software, hardware. Very occasionally, trouble.",,,2026,,Massively Parallel Procrastination,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0215,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Introducing Devin Security Swarm,https://cognition.com/blog/introducing-devin-security-swarm,external,cognition.com,"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.",Gives readers a concrete source in Introducing Devin Security Swarm when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,696,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L696,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;intake;workspace;verification,builder,practitioner-analysis,ok,https://cognition.com/blog/introducing-devin-security-swarm,Introducing Devin Security Swarm | Cognition,"Devin Security Swarm finds vulnerabilities across the codebase, validates exploitability at runtime, and ships remediation PRs.",,2026-07-01,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0216,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Towards Self-Driving Codebases,https://cursor.com/blog/self-driving-codebases,external,cursor.com,"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.",Gives readers a concrete source in Towards Self-Driving Codebases when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,697,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L697,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,practitioner-analysis,ok,https://cursor.com/blog/self-driving-codebases,Towards self-driving codebases · Cursor,We're making a part of our multi-agent research harness available to try today in preview.,Wilson Lin,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0217,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Factory: Incident Response Automation,https://factory.ai/news/incident-response,external,factory.ai,"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.",Gives readers a concrete source in Factory: Incident Response Automation when they need to choose an implementation surface for repeatable agent work.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,698,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L698,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;state,builder,practitioner-analysis,ok,https://factory.ai/news/incident-response,Incident Response | Factory.ai Factory.ai Logo Arrow Right Icon,"On-call alerts have always been stomach-dropping moments. Someone's dinner, weekend, or launch review gets hijacked for ...",Factory,2026-07-10,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0218,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,A Week-Long Autonomous Voxel Manhattan Build,https://x.com/mattshumer_/status/2075268746315268138,external,x.com,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.",Gives readers a concrete source in A Week-Long Autonomous Voxel Manhattan Build when they need to choose an implementation surface for repeatable agent work.,"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,699,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L699,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,practitioner-analysis,ok,https://x.com/mattshumer_/status/2075268746315268138,"Matt Shumer on X: ""GPT-5.6-Sol one-shotted this voxel-based Manhattan. Just look at the precision... it's insane. It ran for almost a week, completely autonomously, to get the job done. https://t.co/LZgthaBnqL"" / X","GPT-5.6-Sol one-shotted this voxel-based Manhattan. Just look at the precision... it's insane. It ran for almost a week, completely autonomously, to get the job done.",,2026-07-09,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0219,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,https://arxiv.org/abs/2607.13091,external,arxiv.org,"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.",Gives readers a concrete source in Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.13091; inspect its method and evaluation before treating results as production evidence.,medium,README.md,700,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L700,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13091,[2607.13091] Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,"LLM-based coding agents repeat the same classes of mistakes across sessions because they lack a mechanism to retain corrections from human review feedback. We present a closed-loop framework in which every accepted review comment is codified as a persistent behavioral rule, progressively expanding the set of error classes the agent can self-detect. The framework combines an accumulating rule set in a version-controlled instruction file, a self-review checklist executed before code submission, and automated validation that ensures rule set integrity as it grows. In deployment across a 35+ service microservices platform, the rule set grew from 5 to 18 behavioral rules, 15+ language-specific standards, and a 15-item self-review checklist, all derived from real review feedback. We present empirical results from 11 recorded working sessions spanning code generation, PR review, incident investigation, and cross service refactoring. We observe that accumulated rules shift review effort from low-level correctness toward design-level validation, achieve a measured 0% recurrence rate for ruled-against error classes, and transfer across heterogeneous agent interfaces. We compare our approach against related work in experiential LLM learning (Reflexion, ExpeL, Voyager) and automated code review (CodeReviewer, SWE-bench agents), showing that our framework achieves persistent cross-session learning without weight updates, operates on production codebases rather than synthetic benchmarks, and addresses an orthogonal dimension (behavioral consistency over time) that existing benchmarks do not measure. The result is a coding agent that improves with every review cycle, accumulating the engineering wisdom of its human collaborators without changing a single model weight.",Aditya Aggarwal; Nahid Farhady Ghalaty,2026-07-13,2026,arXiv,arXiv,,Already presented and accepted in - 32nd ICE IEEE/ITMC Conference (ICE 2026),cs.SE,arxiv-api,,,,,,,2607.13091,2026-07-17T05:43:07+00:00
-ale-0220,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Why Agentic Systems Must Produce Deterministic Outputs to Scale,https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale,external,streamzero.com,"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.",Gives readers a concrete source in Why Agentic Systems Must Produce Deterministic Outputs to Scale when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from streamzero.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,709,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L709,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale,Why Agentic Systems Must Produce Deterministic Outputs to Scale,"Agentic systems are gaining traction, but their inherent non-determinism poses a significant challenge for production environments. This document argues that deterministic outputs are essential for scaling agentic systems, enabling validation, security, and compliance in critical applications.",,,,,streamzero.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0221,Verification And Feedback Gates,verification-and-feedback-gates,Pattern,🔁,Stop Babysitting Your Coding Agent. Give It Backpressure.,https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent,external,generativeprogrammer.com,"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.",Gives readers a concrete source in Stop Babysitting Your Coding Agent. Give It Backpressure. when they need to measure progress and gate completion with repeatable evidence.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,710,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L710,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,operational-pattern,ok,https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent,Stop Babysitting Your Coding Agent. Give It Backpressure.,Backpressure is feedback that reaches the agent before the agent reaches the human.,Bilgin Ibryam,,,,generativeprogrammer.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0222,Verification And Feedback Gates,verification-and-feedback-gates,Pattern,🔁,How to Build a Self-Verification Loop in Claude Code,https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p,external,dev.to,"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.",Gives readers a concrete source in How to Build a Self-Verification Loop in Claude Code when they need to measure progress and gate completion with repeatable evidence.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,711,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L711,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,operational-pattern,ok,https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p,"How to Build a Self-Verification Loop in Claude Code (3 Layers, 20 Minutes) - DEV Community Navigation menu Search Search Close More... Copy link Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode","Claude Code's Stop hook blocks the agent from finishing until verification passes. Combine it with... Tagged with ai, programming, productivity, claude.",,,,,DEV Community,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0223,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Agentic Code Review,https://addyosmani.com/blog/agentic-code-review/,external,addyosmani.com,"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.",Gives readers a concrete source in Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,712,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L712,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://addyosmani.com/blog/agentic-code-review/,AddyOsmani.com - Agentic Code Review,"Coding agents are extraordinarily good now, and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code...",Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0224,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts,https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/,external,simonwillison.net,"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.",Gives readers a concrete source in Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,713,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L713,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,evaluator,practitioner-analysis,ok,https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/,Research: Using DSPy to evaluate and improve Datasette Agent's SQL system prompts,"Leveraging the DSPy framework, this project evaluates and refines the core production system prompts used by Datasette Agent’s read-only SQL question answerer. The methodology involves a harness where DSPy agents …",Simon Willison,,2026,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0225,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Agentic coding notes,https://danluu.com/ai-coding/,external,danluu.com,"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","The work turns loop quality into a measurable task or score. Resource-specific angle: Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.",Gives readers a concrete source in Agentic coding notes when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from danluu.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,714,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L714,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,evaluator,practitioner-analysis,ok,https://danluu.com/ai-coding/,"Agentic test processes, LLM benchmarks, and other notes on agentic coding from Galapagos Island",,,,,,danluu.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0226,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Understanding Is the New Bottleneck,https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html,external,www.geoffreylitt.com,"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.",Gives readers a concrete source in Understanding Is the New Bottleneck when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.geoffreylitt.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,715,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L715,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,evaluator,practitioner-analysis,ok,https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html,Understanding is the new bottleneck,"Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.",,,2026,,geoffreylitt.com,,,,url-date,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0227,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Verifying Agentic Development at Scale,https://cognition.com/blog/testing-development,external,cognition.com,"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.",Gives readers a concrete source in Verifying Agentic Development at Scale when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,716,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L716,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://cognition.com/blog/testing-development,Verifying Agentic Development at Scale | Cognition,What we’ve learned building end-to-end testing capabilities in Devin’s virtual machine,,2026-05-29,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0228,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Loop Engineering Without Verification Is Just Automation,https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/,external,www.sonarsource.com,"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.",Gives readers a concrete source in Loop Engineering Without Verification Is Just Automation when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.sonarsource.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,717,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L717,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/,Loop engineering without verification is just automation | Sonar,Explore how LLM reviewers and deterministic checks work together to keep coding agent loops from shipping unfinished code.,,,,,sonarsource.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0229,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Closing the Verification Loop: Observability-Driven Harnesses,https://www.datadoghq.com/blog/ai/harness-first-agents/,external,www.datadoghq.com,"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.",Gives readers a concrete source in Closing the Verification Loop: Observability-Driven Harnesses when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.datadoghq.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,718,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L718,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.datadoghq.com/blog/ai/harness-first-agents/,Closing the verification loop: Observability-driven harnesses for building with agents | Datadog security-platform rum ci dashboard host-map apm security-platform rum ci dashboard host-map apm security-platform rum ci dashboard Icon/world,"Learn how Datadog verifies AI-generated systems at scale using deterministic testing, formal methods, and observability-driven feedback loops.","Alp Keles, Jai Menon, Sesh Nalla, Vyom Shah",2026-03-09,2026,,Datadog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0230,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,How to build a better agent harness with traces and evals,https://arize.com/blog/improve-ai-agents-traces-evals-harness/,external,arize.com,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Gives readers a concrete source in How to build a better agent harness with traces and evals when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from arize.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,723,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L723,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://arize.com/blog/improve-ai-agents-traces-evals-harness/,How to build a better agent harness with traces and evals - Arize AI,"Agents are easy to prototype and hard to improve. A repeatable loop of traces, evals, failed-span inspection, and targeted harness changes makes agent behavior easier to debug and improve.",Aaron Winston,2026-05-29,2026,,Arize AI,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0231,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Better Harness: A Recipe for Harness Hill-Climbing with Evals,https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals,external,www.langchain.com,LangChain's recipe for using evals as the learning signal for harness improvement.,LangChain's recipe for using evals as the learning signal for harness improvement.,LangChain's recipe for using evals as the learning signal for harness improvement.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: LangChain's recipe for using evals as the learning signal for harness improvement.,Gives readers a concrete source in Better Harness: A Recipe for Harness Hill-Climbing with Evals when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,724,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L724,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals,Better Harness: A Recipe for Harness Hill-Climbing with Evals,"We can build better agents by building better harnesses. But to autonomously build a “better” harness, we need a strong learning signal to “hill-climb” on. We share how we use evals as that signal, plus design decisions that help our agent generalize instead of overfit. Better-Harness is a system for iteratively sourcing and improving your harness with evals.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0232,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Improving Deep Agents with harness engineering,https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering,external,www.langchain.com,"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.",Gives readers a concrete source in Improving Deep Agents with harness engineering when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,725,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L725,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering,Improving Deep Agents with harness engineering,"Harness engineering improved LangChain's coding agent from Top 30 to Top 5 on Terminal Bench using self-verification, tracing, and context optimization.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0233,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,https://arxiv.org/abs/2604.25850,external,arxiv.org,"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.",Gives readers a concrete source in Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.25850; inspect its method and evaluation before treating results as production evidence.,medium,README.md,726,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L726,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.25850,[2604.25850] Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,"Harnesses are now central to coding-agent performance, mediating how models interact with tools and execution environments. Yet harness engineering remains a manual craft, because automating it faces a heterogeneous action space across editable components, voluminous trajectories that bury actionable signal, and edits whose effect is hard to attribute. We introduce Agentic Harness Engineering (AHE), a closed loop that addresses these challenges through three matched observability pillars: (1) component observability gives every editable harness component a file-level representation so the action space is explicit and revertible; (2) experience observability distills millions of raw trajectory tokens into a layered, drill-down evidence corpus that an evolving agent can actually consume; and (3) decision observability pairs every edit with a self-declared prediction, later verified against the next round's task-level outcomes. Together, these pillars turn every edit into a falsifiable contract, so harness evolution proceeds autonomously without collapsing into trial-and-error. Empirically, ten AHE iterations lift pass@1 on Terminal-Bench 2 from 69.7% to 77.0%, surpassing the human-designed harness Codex-CLI (71.9%) and the self-evolving baselines ACE and TF-GRPO. The frozen harness transfers without re-evolution: on SWE-bench-verified it tops aggregate success at 12% fewer tokens than the seed, and on Terminal-Bench 2 it yields +5.1 to +10.1pp cross-family gains across three alternate model families, indicating the evolved components encode general engineering experience rather than benchmark-specific tuning. Ablations localize the gain to tools, middleware, and long-term memory rather than the system prompt, suggesting factual harness structure transfers while prose-level strategy does not.",Jiahang Lin; Shichun Liu; Chengjun Pan; Lizhi Lin; Shihan Dou; Zhiheng Xi; Xuanjing Huang; Hang Yan; Zhenhua Han; Tao Gui; Yu-Gang Jiang,2026-04-28,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2604.25850,2026-07-17T05:43:07+00:00
-ale-0234,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Meta-Harness: End-to-End Optimization of Model Harnesses,https://arxiv.org/abs/2603.28052,external,arxiv.org,"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.",Gives readers a concrete source in Meta-Harness: End-to-End Optimization of Model Harnesses when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2603.28052; inspect its method and evaluation before treating results as production evidence.,medium,README.md,727,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L727,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.28052,[2603.28052] Meta-Harness: End-to-End Optimization of Model Harnesses,"The performance of large language model (LLM) systems depends not only on model weights, but also on their harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing text optimizers are poorly matched to this setting because they compress feedback too aggressively. We introduce Meta-Harness, an outer-loop system that searches over harness code for LLM applications. It uses an agentic proposer that accesses the source code, scores, and execution traces of all prior candidates through a filesystem. On online text classification, Meta-Harness improves over a state-of-the-art context management system by 7.7 points while using 4x fewer context tokens. On retrieval-augmented math reasoning, a single discovered harness improves accuracy on 200 IMO-level problems by 4.7 points on average across five held-out models. On agentic coding, discovered harnesses surpass the best hand-engineered baselines on TerminalBench-2. Together, these results show that richer access to prior experience can enable automated harness engineering.",Yoonho Lee; Roshen Nair; Qizheng Zhang; Kangwook Lee; Omar Khattab; Chelsea Finn,2026-03-30,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.28052,2026-07-17T05:43:07+00:00
-ale-0235,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,HALO (Hierarchical Agent Loop Optimizer),https://github.com/context-labs/halo,external,github.com,"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.",Gives readers a concrete source in HALO (Hierarchical Agent Loop Optimizer) when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (1,103 stars; 80 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,728,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L728,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/context-labs/halo,GitHub - context-labs/HALO: Hierarchal Agent Loop Optimizer · GitHub,Hierarchal Agent Loop Optimizer. Contribute to context-labs/HALO development by creating an account on GitHub.,,2026-04-21,2026,context-labs/halo,GitHub,,,,github-api,context-labs/halo,1103,80,,2026-04-21T18:20:46Z,2026-07-17T03:36:53Z,,2026-07-17T05:43:07+00:00
-ale-0236,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions",https://arxiv.org/abs/2607.03935,external,arxiv.org,"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Gives readers a concrete source in Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2607.03935; inspect its method and evaluation before treating results as production evidence.,medium,README.md,729,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L729,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03935,"[2607.03935] Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions","Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-learning framework in which a single model can generate task solutions or edit selected harness components in a multi-turn action space. HASE enables a single Qwen3-8B model to match the text-classification performance of a GPT-OSS-120B model that uses Claude Code as the harness proposer. In alpha factor mining, HASE outperforms the reported GPT-OSS-120B baseline. HASE also repairs imperfect evaluation components and converges to state-of-the-art performance in circle-packing algorithm discovery. These results show that HASE improves the harness and the solution through one unified agentic process.",Haochen Luo; Yi Huang; Sichun Luo; Fengyuan Liu; Lei Li; Zefa Hu; Junlan Feng; Qi Liu,2026-07-04,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.03935,2026-07-17T05:43:07+00:00
-ale-0237,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,auto-harness,https://github.com/neosigmaai/auto-harness,external,github.com,"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.",Gives readers a concrete source in auto-harness when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (525 stars; 59 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,730,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L730,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/neosigmaai/auto-harness,"GitHub - neosigmaai/auto-harness: Bring your own agent and build a self-improving agentic system. Automatically mine failures, optimize the agent harness, and gate against regressions. · GitHub","Bring your own agent and build a self-improving agentic system. Automatically mine failures, optimize the agent harness, and gate against regressions. - neosigmaai/auto-harness",,2026-04-03,2026,neosigmaai/auto-harness,GitHub,,,,github-api,neosigmaai/auto-harness,525,59,MIT,2026-04-03T21:18:14Z,2026-07-16T14:02:38Z,,2026-07-17T05:43:07+00:00
-ale-0238,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,OpenAI agent evals,https://developers.openai.com/api/docs/guides/agent-evals,external,developers.openai.com,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Gives readers a concrete source in OpenAI agent evals when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,735,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L735,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,official-documentation,ok,https://developers.openai.com/api/docs/guides/agent-evals,Evaluate agent workflows | OpenAI API,"Learn how to evaluate agent workflows with traces, graders, datasets, and evaluation runs on the OpenAI platform.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0239,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Promptfoo OpenAI Agents provider,https://www.promptfoo.dev/docs/providers/openai-agents/,external,www.promptfoo.dev,"Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.",Gives readers a concrete source in Promptfoo OpenAI Agents provider when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,736,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L736,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;delegation;verification;state,builder;evaluator,implementation,ok,https://www.promptfoo.dev/docs/providers/openai-agents/,OpenAI Agents | Promptfoo,"Test OpenAI Agents with tools, handoffs, sessions, sandbox workflows, and tracing in promptfoo.",,,,,promptfoo.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0240,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Inspect AI,https://github.com/UKGovernmentBEIS/inspect_ai,external,github.com,"UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.",Gives readers a concrete source in Inspect AI when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (2,365 stars; 608 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,737,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L737,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,builder;evaluator,source-implementation,ok,https://github.com/UKGovernmentBEIS/inspect_ai,GitHub - UKGovernmentBEIS/inspect_ai: Inspect: A framework for large language model evaluations · GitHub,Inspect: A framework for large language model evaluations - UKGovernmentBEIS/inspect_ai,,2023-11-14,2023,UKGovernmentBEIS/inspect_ai,GitHub,,,,github-api,UKGovernmentBEIS/inspect_ai,2365,608,MIT,2023-11-14T14:53:11Z,2026-07-17T03:58:31Z,,2026-07-17T05:43:07+00:00
-ale-0241,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,OpenTelemetry Semantic Conventions for Generative AI Systems,https://opentelemetry.io/docs/specs/semconv/gen-ai/,external,opentelemetry.io,"Portable tracing conventions for model calls, tool calls, and agent workflows.","Portable tracing conventions for model calls, tool calls, and agent workflows.","Portable tracing conventions for model calls, tool calls, and agent workflows.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Portable tracing conventions for model calls, tool calls, and agent workflows.",Gives readers a concrete source in OpenTelemetry Semantic Conventions for Generative AI Systems when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from opentelemetry.io; use it for current product or standard behavior.,high,README.md,738,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L738,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,builder;evaluator,official-documentation,ok,https://opentelemetry.io/docs/specs/semconv/gen-ai/,Moved: Generative AI semantic conventions | OpenTelemetry The OpenTelemetry Logo,Important GenAI semantic conventions have moved to the OpenTelemetry GenAI semantic conventions repository. This page has moved and is no longer maintained in this repository.,,,,,OpenTelemetry,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0242,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,AgentOps,https://github.com/AgentOps-AI/agentops,external,github.com,"Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.",Gives readers a concrete source in AgentOps when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (5,716 stars; 609 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,739,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L739,,Verify,verify,"Gate progress with tests, evals, and evidence.",state;budget,builder;evaluator,source-implementation,ok,https://github.com/AgentOps-AI/agentops,"GitHub - AgentOps-AI/agentops: Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI · GitHub","Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI - AgentOps-AI/agentops",,2023-08-15,2023,AgentOps-AI/agentops,GitHub,,,,github-api,AgentOps-AI/agentops,5716,609,MIT,2023-08-15T23:26:23Z,2026-07-16T18:46:29Z,,2026-07-17T05:43:07+00:00
-ale-0243,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Langfuse,https://github.com/langfuse/langfuse,external,github.com,"Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.",Gives readers a concrete source in Langfuse when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (31,302 stars; 3,299 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,740,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L740,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/langfuse/langfuse,"GitHub - langfuse/langfuse: 🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 · GitHub","🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 - GitHub - langfuse/langfuse: 🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23",,2023-05-18,2023,langfuse/langfuse,GitHub,,,,github-api,langfuse/langfuse,31302,3299,NOASSERTION,2023-05-18T17:47:09Z,2026-07-17T05:18:44Z,,2026-07-17T05:43:07+00:00
-ale-0244,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,LangSmith,https://www.langchain.com/langsmith,external,www.langchain.com,"Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.",Gives readers a concrete source in LangSmith when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,741,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L741,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://www.langchain.com/langsmith/observability,LangSmith: Agent & LLM Observability Platform,"Complete AI agent and LLM observability platform with tracing and real-time monitoring. Debug agents, find failures fast, and track costs and latency.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0245,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Arize Phoenix,https://github.com/Arize-ai/phoenix,external,github.com,"Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.",Gives readers a concrete source in Arize Phoenix when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (10,595 stars; 988 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,742,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L742,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/Arize-ai/phoenix,GitHub - Arize-ai/phoenix: AI Observability & Evaluation · GitHub,AI Observability & Evaluation. Contribute to Arize-ai/phoenix development by creating an account on GitHub.,,2022-11-09,2022,Arize-ai/phoenix,GitHub,,,,github-api,Arize-ai/phoenix,10595,988,NOASSERTION,2022-11-09T23:44:35Z,2026-07-17T05:13:05Z,,2026-07-17T05:43:07+00:00
-ale-0246,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Braintrust,https://www.braintrust.dev/,external,www.braintrust.dev,"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.",Gives readers a concrete source in Braintrust when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,743,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L743,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://www.braintrust.dev/,Braintrust - The AI observability platform for building quality AI products,"Ship quality AI at scale. Braintrust is the AI observability platform for tracing production, running evals, and catching regressions before they reach users.",,,,,Braintrust,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0247,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Weave,https://docs.wandb.ai/weave,external,docs.wandb.ai,"Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.",Gives readers a concrete source in Weave when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,744,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L744,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://docs.wandb.ai/weave,W&B Weave - Weights & Biases Documentation,"Track, test, and improve language model apps with W&B Weave",,,,,Weights & Biases Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0248,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,agentops (boshu2),https://github.com/boshu2/agentops,external,github.com,"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.",Gives readers a concrete source in agentops (boshu2) when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (411 stars; 40 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,745,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L745,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,source-implementation,ok,https://github.com/boshu2/agentops,GitHub - boshu2/agentops: The operating loop a coding agent follows — and skills to orchestrate multi-agent systems. · GitHub,The operating loop a coding agent follows — and skills to orchestrate multi-agent systems. - boshu2/agentops,,2025-11-05,2025,boshu2/agentops,GitHub,,,,github-api,boshu2/agentops,411,40,Apache-2.0,2025-11-05T19:18:56Z,2026-07-17T02:15:43Z,,2026-07-17T05:43:07+00:00
-ale-0249,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,SkillSpec,https://github.com/modiqo/skillspec,external,github.com,"CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.",Gives readers a concrete source in SkillSpec when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (975 stars; 60 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,746,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L746,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/modiqo/skillspec,"GitHub - modiqo/skillspec: SkillSpec makes agent skills followable, testable, and provable with Doctor risk reports, guided imports, structured contracts, and alignment proof. · GitHub","SkillSpec makes agent skills followable, testable, and provable with Doctor risk reports, guided imports, structured contracts, and alignment proof. - modiqo/skillspec",,2026-06-19,2026,modiqo/skillspec,GitHub,,,,github-api,modiqo/skillspec,975,60,Apache-2.0,2026-06-19T23:42:55Z,2026-07-17T05:08:46Z,,2026-07-17T05:43:07+00:00
-ale-0250,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Shepherd,https://github.com/shepherd-agents/shepherd,external,github.com,"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.",Gives readers a concrete source in Shepherd when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (1,434 stars; 104 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,747,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L747,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;state,builder;evaluator,source-implementation,ok,https://github.com/shepherd-agents/shepherd,"GitHub - shepherd-agents/shepherd: A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents · GitHub","A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents - shepherd-agents/shepherd",,2026-06-24,2026,shepherd-agents/shepherd,GitHub,,,,github-api,shepherd-agents/shepherd,1434,104,MIT,2026-06-24T17:26:46Z,2026-07-16T21:43:18Z,,2026-07-17T05:43:07+00:00
-ale-0251,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,grill-for-unknowns,https://github.com/nicobailon/grill-for-unknowns,external,github.com,"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.",Gives readers a concrete source in grill-for-unknowns when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (178 stars; 6 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,748,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L748,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,builder;evaluator,source-implementation,ok,https://github.com/nicobailon/grill-for-unknowns,"GitHub - nicobailon/grill-for-unknowns: Agent skill for finding unknowns, grilling plans, and reaching shared understanding before implementation · GitHub","Agent skill for finding unknowns, grilling plans, and reaching shared understanding before implementation - nicobailon/grill-for-unknowns",,2026-07-09,2026,nicobailon/grill-for-unknowns,GitHub,,,,github-api,nicobailon/grill-for-unknowns,178,6,MIT,2026-07-09T18:55:30Z,2026-07-16T16:09:21Z,,2026-07-17T05:43:07+00:00
-ale-0252,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Fable Harness,https://github.com/Miguok/fable-harness,external,github.com,"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.",Gives readers a concrete source in Fable Harness when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (189 stars; 33 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,749,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L749,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,source-implementation,ok,https://github.com/Miguok/fable-harness,"GitHub - Miguok/fable-harness: Make Claude Code work like a disciplined engineer: OODA, multi-party adversarial review, tiered model routing, fail-then-pass — token-efficient by design (route heavy work to smaller models, isolate sub-agent context). Distilled from Fable to reinforce the Opus harness. · GitHub","Make Claude Code work like a disciplined engineer: OODA, multi-party adversarial review, tiered model routing, fail-then-pass — token-efficient by design (route heavy work to smaller models, isolate sub-agent context). Distilled from Fable to reinforce the Opus harness. - Miguok/fable-harness",,2026-07-05,2026,Miguok/fable-harness,GitHub,,,,github-api,Miguok/fable-harness,189,33,MIT,2026-07-05T05:57:40Z,2026-07-16T03:21:33Z,,2026-07-17T05:43:07+00:00
-ale-0253,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Mindwalk,https://github.com/cosmtrek/mindwalk,external,github.com,"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.",Gives readers a concrete source in Mindwalk when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (761 stars; 45 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,750,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L750,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,builder;evaluator,source-implementation,ok,https://github.com/cosmtrek/mindwalk,GitHub - cosmtrek/mindwalk: A visualization tool that replays coding-agent sessions on a 3D map of your codebase. · GitHub,A visualization tool that replays coding-agent sessions on a 3D map of your codebase. - cosmtrek/mindwalk,,2026-07-09,2026,cosmtrek/mindwalk,GitHub,,,,github-api,cosmtrek/mindwalk,761,45,MIT,2026-07-09T11:41:46Z,2026-07-17T04:55:08Z,,2026-07-17T05:43:07+00:00
-ale-0254,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Waggle,https://github.com/modiqo/waggle,external,github.com,"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.",Gives readers a concrete source in Waggle when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (791 stars; 141 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,751,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L751,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",context;delegation;budget,builder;evaluator,source-implementation,ok,https://github.com/modiqo/waggle,"GitHub - modiqo/waggle: Attributed, resolvable artifact references for agent handoffs — a ~30-byte token instead of pasted context. MCP-native; the reference layer for the agent-harness world. · GitHub","Attributed, resolvable artifact references for agent handoffs — a ~30-byte token instead of pasted context. MCP-native; the reference layer for the agent-harness world. - modiqo/waggle",,2026-07-08,2026,modiqo/waggle,GitHub,,,,github-api,modiqo/waggle,791,141,Apache-2.0,2026-07-08T04:33:09Z,2026-07-17T05:16:51Z,,2026-07-17T05:43:07+00:00
-ale-0255,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Jacquard,https://github.com/jbwinters/jacquard-lang,external,github.com,"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.",Gives readers a concrete source in Jacquard when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (93 stars; 2 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,752,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L752,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,builder;evaluator,source-implementation,ok,https://github.com/jbwinters/jacquard-lang,GitHub - jbwinters/jacquard-lang: Jacquard is a small programming language designed for a regime in which most code is written by machine-learning models and reviewed by people. · GitHub,Jacquard is a small programming language designed for a regime in which most code is written by machine-learning models and reviewed by people. - jbwinters/jacquard-lang,,2026-07-06,2026,jbwinters/jacquard-lang,GitHub,,,,github-api,jbwinters/jacquard-lang,93,2,Apache-2.0,2026-07-06T23:14:47Z,2026-07-17T05:07:22Z,,2026-07-17T05:43:07+00:00
-ale-0256,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Verification of Software Systems,https://arxiv.org/abs/2511.17330,external,arxiv.org,"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.",Gives readers a concrete source in Agentic Verification of Software Systems when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2511.17330; inspect its method and evaluation before treating results as production evidence.,medium,README.md,757,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L757,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2511.17330,[2511.17330] Agentic Verification of Software Systems,"Automatically generated code is gaining traction recently, owing to the prevalence of Large Language Models (LLMs). Further, the AlphaProof initiative has demonstrated the possibility of using AI for general mathematical reasoning. Reasoning about computer programs (software) can be accomplished via general mathematical reasoning; however, it tends to be more structured and richer in contexts. This forms an attractive proposition, since then AI agents can be used to reason about voluminous code that gets generated by AI. In this work, we present a first LLM agent, AutoRocq, for conducting program verification. Unlike past works, which rely on extensive training of LLMs on proof examples, our agent learns on-the-fly and improves the proof via an iterative refinement loop. The iterative improvement of the proof is achieved by the proof agent communicating with the Rocq (formerly Coq) theorem prover to get additional context and feedback. The final result of the iteration is a proof derivation checked by the Rocq theorem prover. In this way, our proof construction involves autonomous collaboration between the proof agent and the theorem prover. This autonomy facilitates the search for proofs and decision-making in deciding on the structure of the proof tree. Experimental evaluation on SV-COMP benchmarks and on Linux kernel modules shows promising efficacy in achieving automated program verification. As automation in code generation becomes more widespread, we posit that our proof agent can be potentially integrated with AI coding agents to achieve a generate and validate loop, thus moving closer to the vision of trusted automatic programming.",Haoxin Tu; Huan Zhao; Yahui Song; Mehtab Zafar; Ruijie Meng; Abhik Roychoudhury,2025-11-21,2025,arXiv,arXiv,10.1145/3808164,Camera-ready version appeared in the Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2026),cs.SE,arxiv-api,,,,,,,2511.17330,2026-07-17T05:43:07+00:00
-ale-0257,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance",https://arxiv.org/abs/2603.18096,external,arxiv.org,"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Gives readers a concrete source in A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2603.18096; inspect its method and evaluation before treating results as production evidence.,medium,README.md,758,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L758,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.18096,"[2603.18096] A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance","In Agentic AI, Large Language Models (LLMs) are increasingly used in the orchestration layer to coordinate multiple agents and to interact with external services, retrieval components, and shared memory. In this setting, failures are not limited to incorrect final outputs. They also arise from long-horizon interaction, stochastic decisions, and external side effects (such as API calls, database writes, and message sends). Common failures include non-termination, role drift, propagation of unsupported claims, and attacks via untrusted context or external channels. This paper presents an assurance framework for such Agentic AI systems. Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts. Contracts provide machine-checkable verdicts, localize the first violating step, and support deterministic replay. The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations. It also supports structured fault injection at service, retrieval, and memory boundaries to assess containment under realistic operational faults and degraded conditions. Finally, governance is treated as a runtime component, enforcing per-agent capability limits and action mediation (allow, rewrite, block) at the language-to-action boundary. To support comparative evaluations across stochastic seeds, models, and orchestration configurations, the paper defines trace-based metrics for task success, termination reliability, contract compliance, factuality indicators, containment rate, and governance outcome distributions. More broadly, the framework is intended as a common abstraction to support testing and evaluation of multi-agent LLM systems, and to facilitate reproducible comparison across orchestration designs and configurations.",Ciprian Paduraru; Petru-Liviu Bouruc; Alin Stefanescu,2026-03-18,2026,arXiv,arXiv,,,cs.MA,arxiv-api,,,,,,,2603.18096,2026-07-17T05:43:07+00:00
-ale-0258,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Self-Evolving Agents with Anytime-Valid Certificates,https://arxiv.org/abs/2607.00871,external,arxiv.org,"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.",Gives readers a concrete source in Self-Evolving Agents with Anytime-Valid Certificates when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.00871; inspect its method and evaluation before treating results as production evidence.,medium,README.md,759,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L759,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00871,[2607.00871] Self-Evolving Agents with Anytime-Valid Certificates,"Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present \textbf{SEA}, an architecture that confines self-modification to a small steering adapter and a versioned harness around a \emph{frozen} base model and admits each modification only through an anytime-valid gate that emits an auditable certificate against a fixed error budget. Five loop controllers compose published guarantees; because such gates can only \emph{select} among behaviors the frozen base already produces, five verifier-in-the-loop mechanisms -- best-of-$N$, micro-step search, self-authored reproduction oracles, search-layer control, and self-repair -- supply the dense, grader-free signal the gates require, computed from the issue text alone. On a $52$-instance SWE-bench Verified subset across four base models, base capability is the dominant, confound-free effect, and on two strong base models a deliberate no-op-composite control isolates the suite's contribution at $+4$ and $+5$ (\textsc{Glm}~5.2 $24\to28$; \textsc{Gpt} $29\to34$, the $65\%$ best), with event logs confirming that its mechanisms fire and prevent regressions. Results are single-run on expensive evaluations; confirming run-to-run variance and adapting the per-task algorithm mix are future work.",Biswa Sengupta,2026-07-01,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.00871,2026-07-17T05:43:07+00:00
-ale-0259,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Delayed Verification Destabilizes Multi-Agent LLM Belief,https://arxiv.org/abs/2606.27409,external,arxiv.org,"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.",Gives readers a concrete source in Delayed Verification Destabilizes Multi-Agent LLM Belief when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2606.27409; inspect its method and evaluation before treating results as production evidence.,medium,README.md,760,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L760,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.27409,[2606.27409] Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement,"Multi-agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can propagate through the agent network. We model this process as delayed consensus on a graph with grounded corrector nodes. Spectral decomposition by the grounded Laplacian yields a closed-form stability threshold for the verification dose: correction that is too strong or too delayed can turn consensus into oscillation. The most unstable regime occurs when the communication and verification delays coincide; for delay two, the threshold is the inverse golden ratio. The same framework gives a supermodular placement objective and a greedy (1-1/e)-approximation rule for assigning a limited corrector budget to influential nodes. Experiments across five open models confirm the predicted dose-delay oscillations. By contrast, grounded factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed-belief tasks while grounded verification remains stabilizing",Igor Itkin,2026-06-25,2026,arXiv,arXiv,,"20 pages, 5 figures, 1 table. Code and data: https://github.com/YehudaItkin/delayed-verification-llm",cs.MA,arxiv-api,,,,,,,2606.27409,2026-07-17T05:43:07+00:00
-ale-0260,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory,https://arxiv.org/abs/2606.06523,external,arxiv.org,"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.",Gives readers a concrete source in Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2606.06523; inspect its method and evaluation before treating results as production evidence.,medium,README.md,761,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L761,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.06523,[2606.06523] Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory,"Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic capabilities, most agent systems still lack formal methods for specifying, verifying, and debugging their workflow and execution trajectories. This challenge mirrors a long-standing problem in mathematics, where the ambiguity of natural languages (NLs) motivates the development of formal languages (FLs). Inspired by this paradigm, we propose **Lean4Agent**, to the best of our knowledge, the first framework that uses Lean4, a dependent-type FL to model and verify agent behavior. **Lean4Agent** launches **FormalAgentLib**, an extensible Lean4 library for formally modeling and verifying agent workflows' semantic consistency under explicit assumptions, and enabling localization of execution-time failures revealed by trajectories. Building on **FormalAgentLib**, we further develop **LeanEvolve**, which applies results in **FormalAgentLib** to revise workflows to enhance its capability. Extensive experiments on a hard problem subset of SWE-Bench-Verified and a subset of ELAIP-Bench across 5 leading LLMs indicate that the verification-passing workflows outperform the failing ones by an average of **11.94%**, and **LeanEvolve** further improves SWE performance by **7.47%** on average. Furthermore, **Lean4Agent** establishes a foundation for a new field of using expressive dependent-type FL to formally model and verify agent behavior.",Ruida Wang; Jerry Huang; Pengcheng Wang; Xuanqing Liu; Luyang Kong; Tong Zhang,2026-06-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.06523,2026-07-17T05:43:07+00:00
-ale-0261,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Regimes: An Auditable, Held-Out-Gated Improvement Loop",https://arxiv.org/abs/2606.10241,external,arxiv.org,"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Gives readers a concrete source in Regimes: An Auditable, Held-Out-Gated Improvement Loop when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2606.10241; inspect its method and evaluation before treating results as production evidence.,medium,README.md,762,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L762,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.10241,"[2606.10241] Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph","Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history. We show that an event-sourced agent runtime removes that friction and turns controlled improvement into a first-class workflow. When the agent's state is a deterministic projection of an append-only event log, failures are recorded, a run replays exactly from its log, candidate patches scope to typed pipeline seams, gates are auditable, and every promotion or discard is itself an event. We demonstrate this with Regimes, a loop on the ActiveGraph runtime that diagnoses failed evaluations, proposes a repair at a pipeline point, and promotes it only after static checks, sandbox execution, in-sample evaluation, and held-out validation. The loop is target-agnostic: the same control flow runs against different tasks through a common interface. On LongMemEval-S the dominant failure is not retrieval but reconciliation: the evidence is already in the assembled context, yet the reader answers incorrectly. Across five seeded held-out splits, Regimes discovers reader-prompt repairs that improve final held-out accuracy by +0.05 to +0.10 in four splits and +0.01 in one over-promotion split; two splits are individually significant (seed 5 unadjusted for its sequential promotion structure), and the pooled count is descriptive only, since the splits share one 500-question pool. The durable contributions are ActiveGraph as an auditable substrate that makes controlled improvement loops tractable, the held-out-gated loop it supports, the failure-regime taxonomy routing each failure to a pipeline location (whose marginal value over an unrouted baseline is the primary open question), and the prompt-as-discovery-probe hypothesis.",Yohei Nakajima,2026-06-08,2026,arXiv,arXiv,,"30 pages, 5 figures. Code and committed runs: https://github.com/yoheinakajima/regimes",cs.AI,arxiv-api,,,,,,,2606.10241,2026-07-17T05:43:07+00:00
-ale-0262,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents,https://arxiv.org/abs/2605.22608,external,arxiv.org,"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.",Gives readers a concrete source in Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2605.22608; inspect its method and evaluation before treating results as production evidence.,medium,README.md,763,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L763,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.22608,[2605.22608] Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents,"Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-driven, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.",Asaf Yehudai; Lilach Eden; Michal Shmueli-Scheuer,2026-05-21,2026,arXiv,arXiv,,ACL,cs.CL,arxiv-api,,,,,,,2605.22608,2026-07-17T05:43:07+00:00
-ale-0263,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference,https://arxiv.org/abs/2607.02882,external,arxiv.org,"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.",Gives readers a concrete source in Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.02882; inspect its method and evaluation before treating results as production evidence.,medium,README.md,764,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L764,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.02882,[2607.02882] Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference,"Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncertainty of LLM outputs, complex inter-node dependencies, and heterogeneous tool interactions. Existing agentic workflow optimization and agent enhancement methods primarily rely on trajectory-level feedback. Without explicitly identifying the underlying failure root causes, their resulting repair plans are often insufficiently targeted. We propose FlowFixer, a diagnosis-driven automated repair framework for agentic workflows. FlowFixer first transforms workflow executions into unified symbolic traces and performs symbolic inference to derive executable behavioral specifications that capture node correctness, temporal dependencies, and causal relationships. Based on specification verification, it conducts failure attribution and root cause analysis, and then generates targeted repair patches. To reduce verification costs, FlowFixer further employs a multi-dimensional pre-execution assessment to filter infeasible repairs before dynamic verification. We evaluate FlowFixer on workflow failures collected from three popular development platforms: Dify, Coze and n8n. Results show that FlowFixer achieves a repair success rate of 71.3%, outperforming state-of-the-art baselines by 11.9% to 27.6%. It also improves failure attribution accuracy by 4.8% to 33.1% and root cause analysis accuracy by 15.3% to 38.8%. This work offers a new perspective on reliable diagnosis and repair of agentic workflows through symbolic modeling and inference.",Xuyan Ma; Yawen Wang; Junjie Wang; Xiaofei Xie; Boyu Wu; Mingyang Li; Dandan Wang; Qing Wang,2026-07-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.02882,2026-07-17T05:43:07+00:00
-ale-0264,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use,https://arxiv.org/abs/2607.01874,external,arxiv.org,"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.",Gives readers a concrete source in SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.01874; inspect its method and evaluation before treating results as production evidence.,medium,README.md,765,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L765,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.01874,[2607.01874] SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use,"Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both evaluation and training, since an agent may pass through trial and error while selecting distractor skills, skipping required steps, composing workflows incorrectly or omitting final checks. We introduce SkillCoach, a self-evolving rubric framework for evaluating and enhancing agentic skill-use. SkillCoach derives skill-grounded process rubrics from real rollouts and evaluates trajectories along four dimensions: skill selection, skill following, skill composition, and skill-grounded reflection. It keeps the external verifier as a separate outcome signal, allowing process quality to be distinguished from accidental task success. The evolved rubrics further serve as process supervision for selecting high-quality training trajectories. Experiments show that evolved rubrics substantially improve evaluation quality, expose failures hidden by final accuracy, and provide stronger supervision signals than outcome-only filtering for enhancing agentic skill-use.",Jiayin Zhu; Kelong Mao; Yudong Guo; Dengbo He; Sulong Xu; Simiu Gu; Yutao Yue,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.01874,2026-07-17T05:43:07+00:00
-ale-0265,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests,https://arxiv.org/abs/2607.00990,external,arxiv.org,"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.",Gives readers a concrete source in SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.00990; inspect its method and evaluation before treating results as production evidence.,medium,README.md,766,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L766,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00990,[2607.00990] SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests,"Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block for such agents and have been shown useful for patch validation. However, it remains unclear whether BRTs can also help the more central stage of patch generation. We first conduct a preliminary study and find that directly using advanced BRT generators to guide patch generation is not beneficial: fail-to-fail BRTs can mislead agents, while even fail-to-pass BRTs bring limited or negative gains. Our analysis reveals two reasons: fail-to-pass BRTs may cover only one manifestation of the reported issue, leading to partial patches, whereas fail-to-fail BRTs are unreliable as direct patch-generation targets. Motivated by these insights, we propose SWE-Doctor, a software issue resolution agent that guides patch generation with runtime diagnoses derived from multi-faceted BRT executions. SWE-Doctor first generates multi-faceted BRTs for different behavioral requirements stated in the issue, then executes and debugs these BRTs to construct runtime-grounded diagnosis records, and finally uses the diagnoses together with localization information inferred during BRT generation to guide patch generation and reduce partial patches. We evaluate SWE-Doctor on Python bug-fixing issues from the widely adopted SWE-bench Verified and SWE-bench Pro across five LLM backends. SWE-Doctor consistently outperforms existing agents across all 10 LLM-benchmark combinations, achieving average resolution rates of 75.7% on SWE-bench Verified and 59.4% on SWE-bench Pro. In particular, on the more challenging SWE-bench Pro, SWE-Doctor improves the average resolution rate by 8.0-8.9 percentage points over the baseline agents.",Yaoqi Guo; Yang Liu; Jie M. Zhang; Yun Ma; Yiling Lou; Zhenpeng Chen,2026-07-01,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.00990,2026-07-17T05:43:07+00:00
-ale-0266,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation,https://arxiv.org/abs/2607.06273,external,arxiv.org,"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.",Gives readers a concrete source in AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06273; inspect its method and evaluation before treating results as production evidence.,medium,README.md,767,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L767,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06273,[2607.06273] AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation,"Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent repair must recover dynamic trajectories whose early decisions can propagate into later errors and external state changes. Existing automatic remedies address only part of this problem: blind retry adds no diagnosis, outcome feedback says whether a run failed but not where or why, and self-reflection often lacks grounded evidence to prevent the same failure from recurring. We present AgentTether, a run-time repair framework that automates post-run diagnosis and guided recovery without modifying the underlying agent or environment. AgentTether abstracts each run into Transition Units, links them through a dependency-aware Critical Transition Graph, and localizes failure-critical subtrajectories by combining an offline normal-behavior model with a run-local graph detector. It then converts the localized cause into behavior-scoped guidance backed by cross-iteration Repair Memory, and can optionally apply guarded run-time intervention to keep the correction active during re-execution. The same design can be deployed as an offline diagnostic-and-guidance tool or as an online repair layer. We evaluate AgentTether on 261 tau-bench tasks across three domains with Qwen3.7-max, and test cross-model transfer on Banking with GPT-5.4. On the hardest Banking domain, AgentTether repairs 59.04% (49/83) of initially failed Qwen3.7-max tasks and 65.12% (56/86) of initially failed GPT-5.4 tasks. Overall, AgentTether improves repair effectiveness while reducing agent turns and end-to-end approach tokens, suggesting a practical reliability layer that can wrap existing agent deployments, reduce wasted re-execution, and improve recovery without retraining the agent.",Chenyu Zhao; Shenglin Zhang; Wenwei Gu; Yongqian Sun; Dan Pei; Chetan Bansal; Saravan Rajmohan; Minghua Ma,2026-07-07,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.06273,2026-07-17T05:43:07+00:00
-ale-0267,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review,https://arxiv.org/abs/2607.06065,external,arxiv.org,"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.",Gives readers a concrete source in SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06065; inspect its method and evaluation before treating results as production evidence.,medium,README.md,768,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L768,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06065,[2607.06065] SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review,"Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce \textbf{SWE-Review}, a framework for closing this loop with agentic code review. Given an issue and an AI-generated PR, a reviewer agent explores the repository, decides whether the PR should be accepted, and provides structured feedback for revision. We evaluate this setting with our proposed \textbf{SWE-Review-Bench} to measure both review correctness and downstream revision usefulness. We further curate \textbf{SWE-Review-Traj} dataset to study broader applications of agentic review and fill the data-scarcity gap for open reviewer training. Experiments show that agentic review continuously improves PRs through a generate-review-revise loop, outperforms single-turn fixed-context review in both decision accuracy and resolve rate after revision, transfers beyond review to improve issue-resolution models, and enables effective and efficient test-time scaling. These results position agentic code review as a practical mechanism for moving AI coding agents from one-shot PR generation toward closed-loop issue resolution.",Ruoyu Wang; Jierun Chen; Shaowei Wang; Chaofan Tao; Sidi Yang; Yuxin Jiang; Kim-Hui Yap; Lifeng Shang; Xiaohui Li; Haoli Bai,2026-07-07,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.06065,2026-07-17T05:43:07+00:00
-ale-0268,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode",https://arxiv.org/abs/2607.07405,external,arxiv.org,"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Gives readers a concrete source in Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2607.07405; inspect its method and evaluation before treating results as production evidence.,medium,README.md,769,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L769,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07405,"[2607.07405] Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents","Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive environments, a tool may execute any well-formed call even when the corresponding state transition is forbidden by domain policy. The result is a silent wrong state (a booking cancelled, a passenger count changed, a claim acted on without verification) that neither the tool nor the agent's self-report exposes. We study this failure mode in the $\tau^2$-bench airline domain. On a budget agent, 78% of observed failures are silent wrong-state failures with no tool error, and the aggregate failure rate is reproducible across disjoint seeds, not sampling noise. We then evaluate a lightweight intervention: deterministic, read-only pre-execution gates that inspect the proposed call and current state before allowing a write. A four-gate suite raises full-benchmark success from 29.6% to 42.0% on gpt-4o-mini (+12.4pp; paired task-level bootstrap P=0.0012), and the lift reproduces on a disjoint 15-seed set (+12.3pp; P=0.0008). The effect is concentrated where the gates fire: on the 26/50 firing tasks, success rises by +19.2pp, while movement on the 24 non-firing tasks does not exclude zero. Two negative controls (a self-enforcing retail domain and BFCL) bound the mechanism: gates help when tools are policy-permissive and add little where tools already self-enforce. As suggestive evidence, not a central claim, the same failure mode persists at the frontier: gpt-5.2 at default reasoning still attempts policy-violating writes, and the same suite improves success from 61.2% to 71.6% (+10.4pp; P=0.020; n=5, no replication). The contribution is a bounded evaluation and reliability result: deterministic gates do not guarantee task success, but they can deterministically prevent a known class of silent policy-violating writes at the action boundary.",Vikas Reddy; Sumanth Reddy Challaram; Abhishek Basu,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07405,2026-07-17T05:43:07+00:00
-ale-0269,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Harnessing Code Agents for Automatic Software Verification,https://arxiv.org/abs/2607.06341,external,arxiv.org,"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.",Gives readers a concrete source in Harnessing Code Agents for Automatic Software Verification when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06341; inspect its method and evaluation before treating results as production evidence.,medium,README.md,770,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L770,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06341,[2607.06341] Harnessing Code Agents for Automatic Software Verification,"Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large language models (LLMs) promise to generate these proofs automatically, yet existing approaches wire a fixed, human-designed proof strategy into the system and constrain the model to follow it (retrieving premises and predicting tactics one step at a time, or splitting goals by divide-and-conquer), and still prove only a fraction of their target theorems. We show that imposing such a strategy is unnecessary and limiting. Handing the whole lemma to a general LLM code agent (for example, Claude Code), free to choose its own approach, and wrapping it in a verification harness is both simpler and more effective, achieving full coverage: every targeted lemma proved, with no failures and no Coq expert intervention. The agent writes the proofs under feedback and hard constraints from the harness that keep each one sound (accepted only when the prover's kernel closes it), complete (no obligation left unproved or silently dropped), and terminating (no divergent tactics). We evaluate this harness plus code agent along three dimensions. (1) Core logic: on Iris, the state-of-the-art separation logic for concurrent and memory-manipulating programs, Aria proves all 4,257 lemmas of the four core modules and the 217 lemmas verifying Rust's standard libraries built on it, fully automatically. (2) Comparison with prior LLM provers: on reglang, where prior provers manage barely one in eight, Aria proves all 318. (3) Generality: on iris-lean, the unfinished Lean 4 port of Iris, it proves 72 not-yet-ported lemmas, showing the approach is not specific to Coq. A state-of-the-art model (Claude Opus 4.7) can write proofs for verified software development fully and automatically.",Shuangxiang Kan; Shuanglong Kan; Sebastian Ertel,2026-07-07,2026,arXiv,arXiv,,,cs.FL,arxiv-api,,,,,,,2607.06341,2026-07-17T05:43:07+00:00
-ale-0270,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,LLM-as-a-Verifier: A General-Purpose Verification Framework,https://arxiv.org/abs/2607.05391,external,arxiv.org,"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.",Gives readers a concrete source in LLM-as-a-Verifier: A General-Purpose Verification Framework when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05391; inspect its method and evaluation before treating results as production evidence.,medium,README.md,771,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L771,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05391,[2607.05391] LLM-as-a-Verifier: A General-Purpose Verification Framework,"Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training. Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores. This probabilistic formulation enables verification to scale along multiple dimensions: (1) score granularity, (2) repeated evaluation, and (3) criteria decomposition. In particular, we show that scaling the scoring granularity leads to better separation between positive and negative solutions, resulting in more calibrated comparisons. Moreover, scaling repeated evaluation and criteria decomposition consistently lead to additional gains in verification accuracy through variance and complexity reduction. We further introduce a cost-efficient ranking algorithm for selecting the best solution among candidates using the verifier's continuous scores. LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%). Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress. We build an extension for Claude Code, enabling developers to monitor and improve their own agentic systems. Finally, we show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.",Jacky Kwok; Shulu Li; Pranav Atreya; Yuejiang Liu; Yixing Jiang; Chelsea Finn; Marco Pavone; Ion Stoica; Azalia Mirhoseini,2026-07-06,2026,arXiv,arXiv,,Code: https://github.com/llm-as-a-verifier/llm-as-a-verifier Website: https://llm-as-a-verifier.com,cs.AI,arxiv-api,,,,,,,2607.05391,2026-07-17T05:43:07+00:00
-ale-0271,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents,https://arxiv.org/abs/2607.08028,external,arxiv.org,"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.",Gives readers a concrete source in From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.08028; inspect its method and evaluation before treating results as production evidence.,medium,README.md,772,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L772,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08028,[2607.08028] From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents,"Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context. Productization adds requirements for source boundaries, entity routing, answer contracts, and reproducible traces. We present a harness-engineering approach that reconstructs this pattern into a traceable, auditable LLM-agent architecture: deterministic behavior moves into code, manifests, schemas, and validation artifacts around a replaceable composition boundary, while source-backed claims remain the authority for runtime answers. We instantiate it on a public-data slice of five Korean corporate groups (25 listed companies) and evaluate three research questions. (1) The harness preserves its source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts across the fixed validation scenarios; a fault-injection control confirms the validators flag deliberately broken contracts. (2) The checks the harness enforces held under model substitution: across three hosted models, they passed on all 270 composition-boundary runs; failures were confined to the model-composed side and were caught and recorded. (3) The code-owned guarantees are load-bearing, not reproducible by prompting alone: holding the model fixed and varying only the enforcement layer, prompt instructions alone let recommendation-language and internal-trace-leakage violations reach the reader, which the harness blocks entirely. A bolt-on external guardrail prevents such violations too but over-refuses, dropping utility to 88/120 where the harness preserves full utility (120/120); in this ablation, only code-owned enforcement preserves both safety and utility. The result is a reusable engineering pattern for turning exploratory prototypes into auditable applications with versioned source, control, and validation artifacts.",Joongho Ahn; Moonsoo Kim,2026-07-09,2026,arXiv,arXiv,,"32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: https://github.com/hammerbaki/enterprise-llm-agent-harness (archived at https://doi.org/10.5281/zenodo.21269426)",cs.AI,arxiv-api,,,,,,,2607.08028,2026-07-17T05:43:07+00:00
-ale-0272,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization,https://arxiv.org/abs/2607.07702,external,arxiv.org,"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.",Gives readers a concrete source in From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07702; inspect its method and evaluation before treating results as production evidence.,medium,README.md,773,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L773,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07702,[2607.07702] From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization,"The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution traces are difficult to use directly for optimization: large trace collections are often redundant and heterogeneous, making optimization inefficient and prone to overfitting to low-value failures; meanwhile, each individual trajectory also contains many irrelevant steps, while naive context reduction methods such as truncation or sliding windows can discard causally important evidence and produce misleading optimization signals. To resolve this dilemma, we introduce STRACE (Structural TRajectory Analysis and Causal Extraction), a framework that constructs high signal-noise optimization contexts for more precise and effective optimization. At the batch level, STRACE mines failure patterns to filter redundant traces and retain representative failures; within each selected trace, it performs causal localization over a textual dependency graph to remove non-causal steps and identify the true root-cause module for optimization. Empirical results demonstrate that STRACE significantly outperforms standard context-filtering baselines. Notably, on a challenging formal verification task (VeruSAGE-Bench), it successfully optimizes human-expert designed agents, delivering $1.4\times$ success-rate improvement (42.5% to 58.5%). The code is available at https://github.com/moomight/STRACE .",Ying Chang; Jiahang Xu; Xuan Feng; Chenyuan Yang; Peng Cheng; Yuqing Yang,2026-07-08,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.07702,2026-07-17T05:43:07+00:00
-ale-0273,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems,https://arxiv.org/abs/2607.07989,external,arxiv.org,"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.",Gives readers a concrete source in Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07989; inspect its method and evaluation before treating results as production evidence.,medium,README.md,774,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L774,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07989,[2607.07989] Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems,"Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures. When an execution fails, identifying which agent is responsible and at what point the trajectory first becomes irreversibly misdirected is difficult due to long-horizon interactions and tightly coupled agent behaviors. In this paper, we study the problem of failure localization in LLM-based multi-agent systems and present AgentLocate, a framework that attributes failures to both a specific agent and the earliest decisive step. AgentLocate combines an LLM-based judging mechanism with multi-perspective verification by independent evaluators, whose assessments are aggregated using a confidence-aware strategy. The resulting feedback is further used to adapt the judge through lightweight fine-tuning, improving attribution quality. We evaluate AgentLocate on two complementary benchmarks covering diverse tasks, agent configurations, and trajectory lengths. Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.",Yufei Xia; Anjun Gao; Yueyang Quan; Zhuqing Liu; Minghong Fang,2026-07-08,2026,arXiv,arXiv,,To appear in COLM 2026,cs.CR,arxiv-api,,,,,,,2607.07989,2026-07-17T05:43:07+00:00
-ale-0274,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse,https://arxiv.org/abs/2607.07980,external,arxiv.org,"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.",Gives readers a concrete source in 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07980; inspect its method and evaluation before treating results as production evidence.,medium,README.md,775,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L775,,Verify,verify,"Gate progress with tests, evals, and evidence.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07980,[2607.07980] 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse,"Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built. Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable. A motivating observational analysis of public GITHUB activity finds that agent-authored pull requests are reviewed less often, merged several times faster, and discussed less than human-authored ones, yet the direction of these trends flips under different but equally defensible analysis choices, so the traces establish what is changing without explaining why. To recover the mechanisms, we synthesize practitioner discourse at scale into an explanatory theory: we collect 38,709 grey-literature documents (engineering blogs and Reddit threads), filter to those substantively about code review, and code a stratified random sample of 3,100 with an LLM-assisted pipeline, from which we build a causal model of 26 constructs and 67 relationships (64 directed, 3 contested). Its organizing claim is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process. The theory makes the competing positions explicit and turns ""AI is changing code review"" into falsifiable propositions with named constructs and moderators. As a secondary contribution, we offer the underlying LLM-assisted, grey-literature theory-building method as a scalable template for software-engineering research, with a public implementation.",Shyam Agarwal; Courtney Miller; Christian Kästner; Bogdan Vasilescu,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07980,2026-07-17T05:43:07+00:00
-ale-0275,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring,https://arxiv.org/abs/2607.08066,external,arxiv.org,"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.",Gives readers a concrete source in Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.08066; inspect its method and evaluation before treating results as production evidence.,medium,README.md,776,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L776,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08066,[2607.08066] Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring,"Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misaligned or deceptive behavior. While effective in standard scenarios, recent work highlights that LLMs remain vulnerable to persuasion-based jailbreaks, where natural-language arguments override model constraints. We stress-test whether this vulnerability extends to monitoring LLMs: can an adversarial agent persuade its CoT monitor to approve proposed actions that violate the monitor's policy? We design an evaluation framework with 40 tasks and analyze thousands of agent-monitor interactions, where agents are instructed to argue for policy-violating proposals. We find that in such adversarial settings, monitor access to the agent's CoT reasoning increases rather than decreases approval of harmful actions on average by 9.5%, as the scratchpad provides an additional persuasion channel. To address this, we introduce a fact-checking monitoring framework. We find that a fact-checker and monitor pairing from different model families, for example a Claude 3.7 Sonnet monitor paired with a GPT-4.1 fact-checker, reduces approval of policy-violating actions by up to 45%, compared to only 6%, when using the same model for both fact-checking and monitoring roles. Our results demonstrate that CoT monitoring alone may be insufficient against adversarial persuasion, and that model-diverse fact-checking provides a robust mitigation.",Jennifer Za; Julija Bainiaksina; Nikita Ostrovsky; Tanush Chopra; Victoria Krakovna,2026-07-09,2026,arXiv,arXiv,,"25 pages, 10 figures",cs.AI,arxiv-api,,,,,,,2607.08066,2026-07-17T05:43:07+00:00
-ale-0276,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Physics-Audited Agentic Discovery in Scientific Machine Learning,https://arxiv.org/abs/2607.07379,external,arxiv.org,"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.",Gives readers a concrete source in Physics-Audited Agentic Discovery in Scientific Machine Learning when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07379; inspect its method and evaluation before treating results as production evidence.,medium,README.md,777,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L777,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07379,[2607.07379] Physics-Audited Agentic Discovery in Scientific Machine Learning,"In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A low error, however, does not establish that the predicted fields satisfy the physics that matter for mechanics, such as boundary conditions, superposition, stiffness scaling, or causality. We introduce Physics-Audited Agentic SciML (PA-SciML), a verification-first workflow for agentic SciML discovery. The workflow fixes a scoring evaluator before search, derives reviewable machine-checkable physics requirements, checks each trained candidate on its outputs, and separately searches prescribed input ranges or measured load-history spans for high-violation cases without reference solution fields. A surrogate is reported as verified only under the stated checks. When enabled, the workflow also adds advisory numerical probes before training and tests one modeling change at a time to record which isolated edits are associated with score gains before reuse. In the reported computational-solid-mechanics numerical examples, the static elasticity run selects a surrogate with lower validation error than the error-only baseline while both selected models pass the common linear-elastic checks. In the transient elastodynamics run, an error-only baseline with similar mean error fails a stricter causality check by responding to future parts of the loading history, while the selected surrogate passes the stated checks. The main distinction is per-candidate physics evidence on predicted fields, not a richer aggregate score.",Diab W. Abueidda; Bilal Ahmed; Panos Pantidis; Mostafa E. Mobasher,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07379,2026-07-17T05:43:07+00:00
-ale-0277,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair,https://arxiv.org/abs/2607.07882,external,arxiv.org,"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","The resource is directly reusable as a starting artifact. Resource-specific angle: TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.",Gives readers a concrete source in Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07882; inspect its method and evaluation before treating results as production evidence.,medium,README.md,778,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L778,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07882,[2607.07882] Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair,"Bug reports serve as task specifications for repository-level automated program repair (APR) agents, but they often describe only the observed failure and omit repair-relevant information such as the failure-inducing behavior, behavioral requirement, and implementation scope. As a result, a repair agent may inspect irrelevant code, infer an incorrect requirement, or generate a patch that addresses the reported symptom without restoring the intended repository behavior. We present TrajSpec, a trajectory-guided approach for repository-supported bug report specification refinement. Given an original report and a pre-fix repository, TrajSpec runs a trajectory-collection agent and uses the resulting unverified trajectory as a source of trajectory-derived specification evidence. It organizes this evidence into a three-level representation consisting of a high-level interpretation of the issue, diagnostic findings supporting that interpretation, and concrete repository observations. TrajSpec then generates a draft refined report and applies repository-based review to remove unsupported claims, revise uncertain claims, and add repository-supported details. We evaluate TrajSpec on all 300 SWE-Bench Lite instances using Mini-SWE-Agent V2. TrajSpec's refined reports improve Pass@1 from 41.00% to 59.67% with GPT-5-mini and from 54.67% to 64.33% with MiniMax M2.5. On a stratified sample of 100 instances, TrajSpec's refined reports also improve Pass@1 from 41.00% to 71.00% with Agentless and from 47.00% to 72.00% with AutoCodeRover. Ablation results show that removing repository-based review or the hierarchical evidence representation reduces Pass@1 from 59.67% to 48.00% and 47.67%, respectively. Overall, TrajSpec provides actionable repository-supported context that consistently improves repair performance.",S M Farah Al Fahim; Md Nakhla Rafi; Md Ahasanuzzaman; Zeyang Ma; Dong Jae Kim; Shaowei Wang; Tse-Hsun; Chen,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07882,2026-07-17T05:43:07+00:00
-ale-0278,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Failure as a Process: An Anatomy of CLI Coding Agent Trajectories,https://arxiv.org/abs/2607.09510,external,arxiv.org,"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.",Gives readers a concrete source in Failure as a Process: An Anatomy of CLI Coding Agent Trajectories when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.09510; inspect its method and evaluation before treating results as production evidence.,medium,README.md,779,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L779,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09510,[2607.09510] Failure as a Process: An Anatomy of CLI Coding Agent Trajectories,"Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern. Existing empirical studies investigate why coding agents fail, yet they largely treat failure as a final outcome rather than a temporal process, providing limited insight into how failures emerge, evolve, and become unrecoverable. We present the first large-scale empirical study of CLI coding-agent failure trajectories, introducing a process-oriented framework that analyzes failure through its onset, evolution, and recovery across execution trajectories. We first collect 3,843 execution trajectories generated by seven frontier models across three coding-agent scaffolds (OpenHands, MiniSWE, and Terminus2) on Terminal-Bench, then carefully filter them to obtain 1,794 complete and valid trajectories for manual annotation (over 63,000 execution steps), from which we derive 14 findings spanning failure occurrence, root causes, recovery, and cross-system consistency. Our findings show that coding-agent failures are predominantly driven by epistemic errors, typically begin within the first few execution steps, and often remain hidden until recovery is no longer possible, suggesting that improving coding-agent reliability requires earlier validation and intervention rather than relying solely on final-outcome evaluation.",Xiangxin Zhao; Han Li; Shuaiting Li; Tianyi Zhao; Earl T. Barr; Federica Sarro; He Ye,2026-07-10,2026,arXiv,arXiv,,"12 pages, 6 figures",cs.SE,arxiv-api,,,,,,,2607.09510,2026-07-17T05:43:07+00:00
-ale-0279,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Proof and Property-Based Testing via Property-Templates,https://arxiv.org/abs/2607.09072,external,arxiv.org,"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.",Gives readers a concrete source in Agentic Proof and Property-Based Testing via Property-Templates when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.09072; inspect its method and evaluation before treating results as production evidence.,medium,README.md,780,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L780,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09072,[2607.09072] Agentic Proof and Property-Based Testing via Property-Templates in Data-Intensive Computing,"As the cost of code generation becomes cheaper with AI, the new bottleneck in software engineering has shifted to intent specification and validation. Overcoming this durability crisis of AI-driven coding requires more than traditional fuzzing: each candidate property must be proven correct over a model and shown to hold on the real implementation, making formal proof and systematic property-based testing (PBT) complementary. However, validating properties this way at scale requires solving two subproblems: verifying candidate properties and operationalizing PBT without AI hallucination. We hypothesize that recurring property patterns, cast as property templates--abstract, parameterized forms with holes--address both at once. This paper investigates recurring property patterns in Apache Spark. In data-intensive scalable computing systems, correctness properties arise from the principles of data partition, computation decomposition, and dataflow computation. For instance, aggregation decomposition relates a global function executed on the entire dataset to a local function followed by a recombiner. We design an agentic, dual-track validation framework that uses property templates to formally verify correctness in the Lean 4 theorem prover and instantiate PBT templates as executable PySpark tests. Our evaluation shows that property templates increase agentic proof engineering success by up to 2.6x (1.6x on average) and reduce proof hallucinations by 59%. Template-guided PBT synthesis reduces intent misalignments from 22 to 1 and cuts synthesis cost by up to 5.7x (3.8x on average). Template-guided synthesis further exceeds a state-of-the-art Spark fuzzer and approaches unguided LLM-based PBT on code coverage. Finally, comparing the two tracks is informative: when a proof succeeds yet a PBT finds a counterexample, the mismatch identifies a gap between the formal model and implementation.",Seongmin Lee; Yaoxuan Wu; Miryung Kim,2026-07-10,2026,arXiv,arXiv,,"12 pages, 7 figures, 4 tables; supplementary material included as ancillary file",cs.SE,arxiv-api,,,,,,,2607.09072,2026-07-17T05:43:07+00:00
-ale-0280,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP,https://arxiv.org/abs/2607.11098,external,arxiv.org,"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.",Gives readers a concrete source in AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.11098; inspect its method and evaluation before treating results as production evidence.,medium,README.md,781,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L781,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11098,[2607.11098] AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP,"Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deployment. We present AgentCheck, an open-source web workbench that turns an MCP server into an intervention surface. AgentCheck runs an agent against its real tools and records every tool response, then re-runs the agent with the response perturbed by a fault (12 types) injector. Matching tool calls are replayed from cache, and later tool calls go live after the agent diverges. This yields a reproduce-intervene-confirm loop: the developer toggles a mitigation, re-runs against the identical fault, and sees if the failure goes away. Scoring has two parts: deterministic pass/fail rules, plus an LLM judge for interpretive labels, validated against human annotations. Across five agents, the best passes 105/120 scenarios and the weakest only 77. The failures are usually silent, confident use of incorrect tool outputs rather than crashes. On the weakest agent, a retry mitigation raises success on timeout error faults from as few as 30% of cases to 100%, whereas stale-data faults remain near 3-4 of 10 regardless of the mitigation. AgentCheck makes these failure modes reproducible, comparable, and verifiable before deployment.",Aritra Mazumder; Nusrat jahan Lia,2026-07-13,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.11098,2026-07-17T05:43:07+00:00
-ale-0281,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Latent Programming Horizons in Coding Agents,https://arxiv.org/abs/2607.05188,external,arxiv.org,"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.",Gives readers a concrete source in Latent Programming Horizons in Coding Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05188; inspect its method and evaluation before treating results as production evidence.,medium,README.md,782,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L782,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05188,[2607.05188] Latent Programming Horizons in Coding Agents,"A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally represents about the program it is working on. We show that the residual streams of language models under coding agents linearly encode properties of the evolving program: a logistic-regression probe on hidden states is able to decode whether the current code parses, passes its test suite, reduces the number of failing tests, and introduces regressions, reaching AUC up to 0.83 for correctness across two models and two benchmarks. Our second finding is more surprising: these representations run ahead of the agent's own edits. Probes trained to predict the outcome of future edits (before they are materialized and written on disk) achieve performance above chance up to roughly 25 steps in advance. We call this the agent's latent programming horizon. As a proof of external validity, we show that the probes transfer across benchmarks without retraining. Our positive results open calls for more research in mechanistic interpretability of coding agents.",André Silva; Han Tu; Martin Monperrus,2026-07-06,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.05188,2026-07-17T05:43:07+00:00
-ale-0282,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,Why evaluate agents,https://adk.dev/evaluate/,external,adk.dev,"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.",Gives readers a concrete source in Why evaluate agents when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,783,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L783,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,official-documentation,ok,https://adk.dev/evaluate/,Why evaluate agents - Agent Development Kit (ADK) Agent Development Kit (ADK),Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0283,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Structured Feedback Improves Repair in an LLM Agent Loop,https://arxiv.org/abs/2607.14167,external,arxiv.org,"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","The contribution is machine-readable and validation-friendly. Resource-specific angle: In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.",Gives readers a concrete source in Structured Feedback Improves Repair in an LLM Agent Loop when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14167; inspect its method and evaluation before treating results as production evidence.,medium,README.md,784,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L784,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14167,[2607.14167] Structured Feedback Improves Repair in an LLM Agent Loop,"LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified. We introduce VeriHarness, a code-controlled agent loop in which models generate candidates while external validators control acceptance, budgets, and traces. We use it to compare raw diagnostics with feedback that identifies the failure location, observed value, and admissible alternatives. Across 50 paired TextWorld games under a four-call cap, feedback containing all three fields raises terminal success from 14/50 to 36/50 for Qwen2.5-Coder-14B (+44 percentage points) and from 8/50 to 29/50 for Llama-3.1-8B (+42 points). Ablations locate most of the gain in the admissible alternatives: feedback containing only the location and observed value remains near the raw diagnostic baseline. Presenting the complete repair information in prose instead of a keyed JSON record yields nearly the same success, providing no evidence that JSON syntax itself improves repair. The ordering persists across the tested call budgets and one sampled-decoding setting.",Jaideep Ray; Ankit Goyal,2026-07-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.14167,2026-07-17T05:43:07+00:00
-ale-0284,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Copy-on-Write Scoring: Application-Specific Agent Evaluations,https://arxiv.org/abs/2607.14336,external,arxiv.org,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Gives readers a concrete source in Copy-on-Write Scoring: Application-Specific Agent Evaluations when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14336; inspect its method and evaluation before treating results as production evidence.,medium,README.md,785,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L785,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14336,[2607.14336] Copy-on-Write Scoring: Application-Specific Agent Evaluations,"Trustworthy deployment of LLM-based agents in software systems requires evaluating how they perform on application-specific workflows, with enough granularity to localize where they succeed and fail. Yet existing agent evaluation mechanisms are limited: benchmarks have low construct validity for application-specific workflows and environments, and replica evaluation environments are expensive and prone to drift. We propose Copy-on-Write (CoW) Scoring, a framework that evaluates agent operations directly within application environments using a PostgreSQL-level Copy-on-Write mechanism to isolate agent writes. CoW Scoring produces session- and operation-level scores that highlight where agents' database write operations succeed and fail in a given application environment, enabling inexpensive evaluation and iteration on agent harnesses and tool surfaces. We demonstrate the framework on Plane, an open-source project-management platform, where analysis surfaced specific issues in the tool surface, and corresponding fixes produced measurable improvements on affected models. Python library: https://github.com/trail-ml/agent-cow-python",Joanna Roy; Sven Hoelzel,2026-07-15,2026,arXiv,arXiv,,"15 pages, 11 figures, accepted at ICML 2026 Second Workshop on Agents in the Wild: Safety, Security, and Beyond",cs.SE,arxiv-api,,,,,,,2607.14336,2026-07-17T05:43:07+00:00
-ale-0285,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,https://arxiv.org/abs/2607.14340,external,arxiv.org,"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.",Gives readers a concrete source in The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14340; inspect its method and evaluation before treating results as production evidence.,medium,README.md,786,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L786,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14340,[2607.14340] The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,"AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3, IKEv2, X.509, and a Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an agent can be trusted to establish is bounded by the strength of its feedback.",Tobias Philipp,2026-07-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.14340,2026-07-17T05:43:07+00:00
-ale-0286,Securing Unattended Loops,securing-unattended-loops,Critique,⚠️,The lethal trifecta for AI agents,https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/,external,simonwillison.net,"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.",Gives readers a concrete source in The lethal trifecta for AI agents when they need to bound risk before recurring or unattended execution.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,794,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L794,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,operator;security,risk-analysis,ok,https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/,"The lethal trifecta for AI agents: private data, untrusted content, and external communication",If you are a user of LLM systems that use tools (you can call them “AI agents” if you like) it is critically important that you understand the risk of …,Simon Willison,,2025,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0287,Securing Unattended Loops,securing-unattended-loops,Critique,⚠️,Prompt injection series,https://simonwillison.net/series/prompt-injection/,external,simonwillison.net,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Gives readers a concrete source in Prompt injection series when they need to bound risk before recurring or unattended execution.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,795,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L795,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake,operator;security,risk-analysis,ok,https://simonwillison.net/series/prompt-injection/,Simon Willison: Prompt injection,,Simon Willison,,,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0288,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Agentic AI - Threats and Mitigations,https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/,external,genai.owasp.org,"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.",Gives readers a concrete source in Agentic AI - Threats and Mitigations when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,796,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L796,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;workspace;context;delegation,builder;operator;security,technical-documentation,ok,https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/,Agentic AI - OWASP Lists Threats and Mitigations,"Explore key threats and mitigation strategies for agentic AI, focusing on security measures to address vulnerabilities in AI applications and their potential risks.",OWASPGenAIProject Editor,,,,OWASP Gen AI Security Project,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0289,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Designing AI agents to resist prompt injection,https://openai.com/index/designing-agents-to-resist-prompt-injection/,external,openai.com,"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","Primary-source operational guidance rather than commentary. Resource-specific angle: OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.",Gives readers a concrete source in Designing AI agents to resist prompt injection when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,797,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L797,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,builder;operator;security,technical-documentation,restricted,https://openai.com/index/designing-agents-to-resist-prompt-injection/,,,,,,,OpenAI,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0290,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,sandbox-runtime,https://github.com/anthropic-experimental/sandbox-runtime,external,github.com,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Gives readers a concrete source in sandbox-runtime when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (4,685 stars; 363 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,798,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L798,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/anthropic-experimental/sandbox-runtime,"GitHub - anthropic-experimental/sandbox-runtime: A lightweight sandboxing tool for enforcing filesystem and network restrictions on arbitrary processes at the OS level, without requiring a container. · GitHub","A lightweight sandboxing tool for enforcing filesystem and network restrictions on arbitrary processes at the OS level, without requiring a container. - anthropic-experimental/sandbox-runtime",,2025-10-20,2025,anthropic-experimental/sandbox-runtime,GitHub,,,,github-api,anthropic-experimental/sandbox-runtime,4685,363,Apache-2.0,2025-10-20T02:52:10Z,2026-07-17T05:03:55Z,,2026-07-17T05:43:07+00:00
-ale-0291,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,E2B,https://github.com/e2b-dev/E2B,external,github.com,"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.",Gives readers a concrete source in E2B when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (13,014 stars; 967 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,799,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L799,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/e2b-dev/E2B,"GitHub - e2b-dev/E2B: Open-source, secure environment with real-world tools for enterprise-grade agents. · GitHub","Open-source, secure environment with real-world tools for enterprise-grade agents. - e2b-dev/E2B",,2023-03-04,2023,e2b-dev/E2B,GitHub,,,,github-api,e2b-dev/E2B,13014,967,Apache-2.0,2023-03-04T13:41:18Z,2026-07-17T04:55:28Z,,2026-07-17T05:43:07+00:00
-ale-0292,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Modal Sandboxes,https://modal.com/docs/guide/sandboxes,external,modal.com,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Secure sandboxed execution for agent-driven code with resource limits and network controls.,Gives readers a concrete source in Modal Sandboxes when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,800,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L800,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,technical-documentation,ok,https://modal.com/docs/guide/sandboxes,Sandboxes | Modal Docs,"This page is a high-level guide to Sandboxes, secure containers for executing untrusted user or agent code on Modal.",,,,,Modal,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0293,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Daytona,https://www.daytona.io/,external,www.daytona.io,"Infrastructure for running AI-generated code in fast, isolated sandboxes.","Infrastructure for running AI-generated code in fast, isolated sandboxes.","Infrastructure for running AI-generated code in fast, isolated sandboxes.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Infrastructure for running AI-generated code in fast, isolated sandboxes.",Gives readers a concrete source in Daytona when they need to bound risk before recurring or unattended execution.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,801,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L801,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,implementation,ok,https://www.daytona.io/,Daytona - Secure Infrastructure for Running AI-Generated Code,"Deploy Al code with confidence using Daytona's lightning-fast infrastructure. 90ms environment creation, stateful operations, and enterprise-grade security.",,,,,daytona.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0294,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,peerd,https://github.com/NotASithLord/peerd,external,github.com,"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.",Gives readers a concrete source in peerd when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (359 stars; 35 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,802,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L802,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;delegation,builder;operator;security,source-implementation,ok,https://github.com/NotASithLord/peerd,"GitHub - NotASithLord/peerd: The first AI agent harness native to the browser. A browser extension that runs a full agent loop where you already work: it drives your tabs, spins up sandboxed compute (JS notebooks, WASM Linux VMs, client-side apps), and shares what it builds peer-to-peer. BYOK, no backend, no telemetry. · GitHub","The first AI agent harness native to the browser. A browser extension that runs a full agent loop where you already work: it drives your tabs, spins up sandboxed compute (JS notebooks, WASM Linux VMs, client-side apps), and shares what it builds peer-to-peer. BYOK, no backend, no telemetry. - NotASithLord/peerd",,2026-06-22,2026,NotASithLord/peerd,GitHub,,,,github-api,NotASithLord/peerd,359,35,Apache-2.0,2026-06-22T05:55:13Z,2026-07-16T23:48:05Z,,2026-07-17T05:43:07+00:00
-ale-0295,Securing Unattended Loops,securing-unattended-loops,Paper,📄,When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,https://arxiv.org/abs/2607.05189,external,arxiv.org,"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.",Gives readers a concrete source in When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05189; inspect its method and evaluation before treating results as production evidence.,medium,README.md,803,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L803,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05189,[2607.05189] When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,"Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution. This integration also creates a new path to compromise: untrusted external content can be silently written into persistent memory and later reused as trusted state. We study this threat as stealth memory injection, in which a remote black-box adversary delivers a single email payload that must induce the agent to write poisoned memory, stay hidden in the agent's response to the user, and affect future behavior. We introduce WhisperBench, a 108-case benchmark spanning five risk categories and both fact and preference poisoning. Built on a real IMAP/SMTP workflow and an authentic email agent skill, it enables full-cycle evaluation of stealth memory injection attacks. To enable this black-box attack under single-email delivery and without runtime feedback, we propose MemGhost, a one-shot payload generation framework. MemGhost uses an environment proxy to emulate persistent-agent execution and an objective proxy to convert memory adoption and conversational stealth into dense rubric-based rewards, then trains the attacker policy with supervised fine-tuning and reinforcement learning. Across 56 held-out test cases, MemGhost achieves 87.5% end-to-end success on OpenClaw with GPT-5.4 and 71.4% on Claude Code SDK with Sonnet 4.6. It also transfers across personal-agent architectures (NanoClaw and Hermes Agent) and memory backends (filesystem and vector-based Mem0), and remains effective against input-level, model-level, and system-level defenses. These results suggest that persistent memory can turn ordinary external processing into a practical pathway for long-term agent compromise.",Yechao Zhang; Shiqian Zhao; Jiawen Zhang; Jie Zhang; Gelei Deng; Xiaogeng Liu; Chaowei Xiao; Tianwei Zhang,2026-07-06,2026,arXiv,arXiv,,"25 pages, 8 figures. Preprint",cs.CR,arxiv-api,,,,,,,2607.05189,2026-07-17T05:43:07+00:00
-ale-0296,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,https://arxiv.org/abs/2607.05029,external,arxiv.org,"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.",Gives readers a concrete source in Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05029; inspect its method and evaluation before treating results as production evidence.,medium,README.md,804,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L804,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05029,[2607.05029] Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,"Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context. While this has improved the agent's functionality and continuity across tasks, it has also introduced a new attack surface: the agent's own reasoning history. In this paper, we introduce the Forged Amplifying Rationale Memory Attack (FARMA), which poisons an agent's remembered reasoning rather than its factual knowledge. It inserts forged reasoning traces using evasive language that bypasses keyword-based defenses, then amplifies them through self-referential reinforcement that defeats consensus-based defenses. To address FARMA, we introduce SENTINEL, a layered defense pipeline to detect forged reasoning entries. Its central component is the Reasoning Guard that structurally analyzes candidate entries for forgery using five weighted signals. We evaluate FARMA and SENTINEL across multiple agents and different LLM models with 50 trials and show that FARMA achieves an attack success rate of up to 100% under baseline conditions and is capable of defeating defense mechanisms like keyword filter and A-MemGuard. Our evaluation also shows that SENTINEL reduces FARMA's attack success rate to as low as 0% with no false positives observed across 326 benign agent traces. Our work demonstrates the need to protect not only an agent's retrieved content but also the integrity of its reasoning history.",Neeraj Karamchandani; Piyush Nagasubramaniam; Sencun Zhu; Dinghao Wu,2026-07-06,2026,arXiv,arXiv,,"Preprint. 10 pages, 2 figures, 4 tables",cs.CR,arxiv-api,,,,,,,2607.05029,2026-07-17T05:43:07+00:00
-ale-0297,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Distributed Attacks in Persistent-State AI Control,https://arxiv.org/abs/2607.02514,external,arxiv.org,"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.",Gives readers a concrete source in Distributed Attacks in Persistent-State AI Control when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.02514; inspect its method and evaluation before treating results as production evidence.,medium,README.md,805,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L805,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.02514,[2607.02514] Distributed Attacks in Persistent-State AI Control,"As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence creates a new attack surface: a misaligned or prompt-injected agent can distribute attacks across pull requests (PRs) and time its payload for the PR with the best natural cover. To study the resulting dynamics, we introduce Iterative VibeCoding, a setting for AI control, the study of safely deploying capable but potentially untrusted AI. In Iterative VibeCoding, a coding agent builds software over a sequence of PRs in a persistent codebase while pursuing a covert side task. Our benchmark includes two task families: CLI tools and Flask web services, across 20 total task variations. We use Claude Sonnet 4.5 as the attack agent and GPT-4o as the monitor. We compare gradual attacks, which distribute the side task across PRs, against non-gradual attacks concentrated in a single PR. No single monitor is robust to both: which strategy evades best (success while evading the monitor) depends on the monitor type, so a defender cannot close off both gradual and non-gradual attacks with any one monitor. High evasion (>= 65%) generalizes across model attack agent backends (Sonnet 4.5, Gemini 3.1 Pro, Kimi K2.5), confirming this is a property of the persistent-state attack surface rather than a single model's capability. Evasion also remains high across state-of-the-art monitor models and the gap between gradual and non-gradual evasion widens for more capable models. We introduce a stateful link-tracker monitor that tracks suspicious buildup across PRs. On both task families, it detects gradual attacks substantially better than diff monitors that merely see more accumulated history. Combining this stronger monitor with trajectory monitors in a four-monitor ensemble reduces gradual-attack evasion from 93% under the weakest standard diff monitor to 47%.",Josh Hills; Ida Caspary; Asa Cooper Stickland,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02514,2026-07-17T05:43:07+00:00
-ale-0298,Securing Unattended Loops,securing-unattended-loops,Paper,📄,ElephantAgent: Contextual State Continuity in Agentic Systems,https://arxiv.org/abs/2607.01919,external,arxiv.org,"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.",Gives readers a concrete source in ElephantAgent: Contextual State Continuity in Agentic Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.01919; inspect its method and evaluation before treating results as production evidence.,medium,README.md,806,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L806,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.01919,[2607.01919] ElephantAgent: Contextual State Continuity in Agentic Systems,"Agentic systems enhance their capabilities by invoking external tools and maintaining persistent memory. However, these external dependencies introduce novel attack surfaces. Recent tool and memory poisoning attacks show that maliciously crafted tool descriptors and poisoned memory can covertly bias agent behavior. These threats reflect a deeper issue: the lack of verifiable continuity in the agent's contextual state for planning and execution. We present ElephantAgent, a protocol that enforces Contextual State Continuity to defend against contextual state poisoning. Inspired by prior state-continuity mechanisms (e.g., Nimble), ElephantAgent extends this protection to the evolving contextual state of agentic systems. We define the contextual state as the bounded, security-critical subset of the agent's entire context (e.g., tool state and memory). Before processing each query, ElephantAgent recomputes the digest of the local contextual state and verifies it against the latest authorized digest. Using replicated trusted hardware, ElephantAgent maintains a linearizable ledger of authorized contextual state transitions and detects out-of-band state tampering. To handle in-band semantic abuse, ElephantAgent additionally provides Historical Traceability, enabling conditional post-hoc audit and recovery to a known-good prior state.",Jiankai Jin; Xiangzheng Zhang; Zhao Liu; Wenzhuo Xu; Dongdong Yang; Deyue Zhang; Quanchen Zou,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.01919,2026-07-17T05:43:07+00:00
-ale-0299,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Cloudflare security-audit-skill,https://github.com/cloudflare/security-audit-skill,external,github.com,"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","The contribution is machine-readable and validation-friendly. Resource-specific angle: Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.",Gives readers a concrete source in Cloudflare security-audit-skill when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (2,547 stars; 190 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,807,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L807,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,builder;operator;security,source-implementation,ok,https://github.com/cloudflare/security-audit-skill,"GitHub - cloudflare/security-audit-skill: A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings · GitHub","A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings - cloudflare/security-audit-skill",,2026-06-18,2026,cloudflare/security-audit-skill,GitHub,,,,github-api,cloudflare/security-audit-skill,2547,190,MIT,2026-06-18T14:08:44Z,2026-07-17T05:00:47Z,,2026-07-17T05:43:07+00:00
-ale-0300,Securing Unattended Loops,securing-unattended-loops,Paper,📄,The Balkanization of Execution-Security Research for AI Coding Agents,https://arxiv.org/abs/2607.05743,external,arxiv.org,"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.",Gives readers a concrete source in The Balkanization of Execution-Security Research for AI Coding Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05743; inspect its method and evaluation before treating results as production evidence.,medium,README.md,808,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L808,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05743,"[2607.05743] The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities","AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe. That literature is scattered. Papers on sandbox isolation, capability and access control, policy enforcement, time-of-check-to-time-of-use (TOCTOU) races, Model Context Protocol (MCP) threats, identity delegation, execution provenance, network egress control, and static analysis of agent-generated code are published independently and rarely cite one another. We systematize 39 papers published between 2023 and 2026 into 17 categories, each verified directly against its source. The same verification protocol also confirms four disclosed, patched CVEs directly affecting production agent harnesses. Reading across categories surfaces five cross-cutting gaps that no single paper addresses. (1) Isolation architectures and capability models are almost never evaluated against one another on a shared benchmark. (2) Policy-enforcement studies report failure rates from 69% to 98% of real denylists, yet no isolation paper re-evaluates its own defense under that adversarial setting. (3) TOCTOU and MCP threats are analyzed as separate literatures despite both being instances of the same state-validation problem. (4) Every enforcement mechanism assumes an honest policy author, leaving policy-authoring error itself unaddressed. (5) Benign but out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting are addressed by no access-control or capability paper in the corpus. Existing broader surveys of agentic AI security discuss sandboxing only as one item among many defenses, leaving execution security without a dedicated systematization. This paper is written to fill that gap. We conclude with a research agenda directed at the five gaps.",Mohammadreza Rashidi,2026-07-07,2026,arXiv,arXiv,,"18 pages, 15 figures, 6 tables. Systematizes 39 execution-security papers (2023-2026) into 17 verified categories. Machine-readable corpus and verification script released as a supplementary artifact",cs.CR,arxiv-api,,,,,,,2607.05743,2026-07-17T05:43:07+00:00
-ale-0301,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Context-to-Execution Integrity for LLM Agents,https://arxiv.org/abs/2607.06000,external,arxiv.org,"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.",Gives readers a concrete source in Context-to-Execution Integrity for LLM Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.06000; inspect its method and evaluation before treating results as production evidence.,medium,README.md,809,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L809,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.06000,[2607.06000] Context-to-Execution Integrity for LLM Agents,"Language-model agents read attacker-writable context to solve tasks. Tool execution needs a separate authority check for protected sink fields, sink-interpreted payloads, and the invocation event. Context-to-Execution Integrity (CXI) is an execution-boundary system for this setting. Policies mark protected sink fields, typed releases carry narrow validated values from writable context to specific destinations, opaque data slots keep evidence as data, and a deterministic gate admits a call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest. We evaluate CXI on open-weight field-projection runs, AgentDojo live episodes, a code-agent exact-effect benchmark, manifest-bound ledger faults, proposal-pressure controls, and hosted/API compatibility traces. AgentDojo covers 720 live episodes and 1,739 LLM calls; the code-agent benchmark covers 400 repository episodes with exact-effect authorization and lease-bound execution, yielding 231 safe task completions and zero observed field, effect, or invocation escapes. The accounting reports parser outcomes, authorization outcomes, and task-quality outcomes together with the admission-integrity result. Across the evaluated sinks, CXI admits execution only when field, effect, and invocation authority bind to the same action manifest.",Igor Santos-Grueiro,2026-07-07,2026,arXiv,arXiv,,20 pages,cs.CR,arxiv-api,,,,,,,2607.06000,2026-07-17T05:43:07+00:00
-ale-0302,Securing Unattended Loops,securing-unattended-loops,Paper,📄,When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents,https://arxiv.org/abs/2607.06595,external,arxiv.org,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,Gives readers a concrete source in When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.06595; inspect its method and evaluation before treating results as production evidence.,medium,README.md,810,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L810,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.06595,[2607.06595] When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents,"Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight. When augmented with long-term memory, an agent can recall specific details relevant to the current task, reducing the need for large context windows. Currently, long-term memory agents tend to fall into two distinct domains: conversational and action-planning agents. Personal assistant agents sit at the convergence of these two domains and handle sensitive information while interacting with untrusted information sources, creating previously unaccounted security vulnerabilities. In this work, we introduce the novel attack vector, GhostWriter, which exploits current memory subsystems in tool-using personal agents to poison their memory store. GhostWriter operates in two phases: injection, where an adversary sends a hidden attack payload to the target agent; and activation, in which the poisoned memory is retrieved. We show that GhostWriter achieves near-universal injection rates of approximately 98% and a high average activation rate of approximately 60% against state-of-the-art agents. This attack is possible due to the lack of security-focused memory governance. In response, we propose Agentic Memory Sentry (AM-Sentry), which leverages two mitigation techniques: a memory-saving policy and a memory-retrieval screen. Our experiments show that AM-Sentry dramatically reduces GhostWriter's success rate while preserving agent utility.",George Torres; Sharad Shrestha; Satyajayant Misra,2026-07-06,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.06595,2026-07-17T05:43:07+00:00
-ale-0303,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents,https://arxiv.org/abs/2607.08395,external,arxiv.org,"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.",Gives readers a concrete source in Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08395; inspect its method and evaluation before treating results as production evidence.,medium,README.md,811,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L811,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;state;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08395,[2607.08395] Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents,"Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assistants, unsafe content in these agents can propagate through persistent state, reusable skills, and tool-mediated interactions, creating a substantially larger semantic attack surface. We observe that most security-critical interactions in such agents are transmitted through natural-language token flows, including memory updates, tool arguments, retrieved files, and inter-component communications. This observation enables a new security formulation: unsafe behavior can be intercepted as risky semantic flows before reaching privileged runtime sinks. Based on this insight, we propose TokenWall, a runtime defense framework that acts as a semantic firewall over agent token flows. TokenWall performs boundary-aware semantic auditing over these flows, constructing structured source-sink audit records, applying lightweight local inspection before execution, and selectively escalating ambiguous high-risk cases to stronger arbitration modules. Unlike prior approaches that rely on sparse auditing or remote large-model oversight, TokenWall enables full-coverage pre-execution mediation while reducing remote arbitration and latency. Experiments on CIK-Bench show that TokenWall reduces attack success rate to 12.5% while maintaining a 97.4% benign executable pass rate without human confirmation. TokenWall further introduces only 0.69 seconds of additional latency on benign cases, demonstrating that semantic runtime containment can achieve a practical security-utility trade-off for persistent AI agents.",Puji Wang; Yingchen Zhang; Ruqing Zhang; Jiafeng Guo; Xueqi Cheng,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08395,2026-07-17T05:43:07+00:00
-ale-0304,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Prismata: Confining Cross-Site Prompt Injection in Web Agents,https://arxiv.org/abs/2607.08147,external,arxiv.org,"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.",Gives readers a concrete source in Prismata: Confining Cross-Site Prompt Injection in Web Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08147; inspect its method and evaluation before treating results as production evidence.,medium,README.md,812,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L812,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08147,[2607.08147] Prismata: Confining Cross-Site Prompt Injection in Web Agents,"Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untrusted content is dangerous, even on benign pages. Agents resurface this risk by interpreting natural language as instructions, allowing third-party and user-generated content to hijack the agent via prompt injection. The core challenge is that deriving a task-specific security policy requires reasoning over page structure that is entangled with the attacker's content. We present Prismata, a defense enforcing contextual least privilege for web agents, constraining both what the agent sees and what it can do. Prismata's dynamic trust derivation produces permission labels for page content, with structural confinement guarantees, inspired by classical integrity models, that bound any labeling errors so that labels can only decrease in privilege and mislabelings are bounded. Prismata's mechanical confinement enforces these labels by redacting content and restricting agent capabilities. Importantly, these mechanisms require no developer annotations, so Prismata supports the long tail of websites. Across recent published web agent attacks, including adaptive variants, Prismata substantially reduces attack success while preserving benign task utility.",Corban Villa; Alp Eren Ozdarendeli; Sijun Tan; Raluca Ada Popa,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08147,2026-07-17T05:43:07+00:00
-ale-0305,Securing Unattended Loops,securing-unattended-loops,Paper,📄,TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories,https://arxiv.org/abs/2607.08400,external,arxiv.org,"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.",Gives readers a concrete source in TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08400; inspect its method and evaluation before treating results as production evidence.,medium,README.md,813,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L813,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08400,[2607.08400] TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories,"LLM agents reach users through resellers, who may rebrand a developer's agent or substitute a cheaper model. When provenance is disputed, attribution rests on the trajectory log (the record of tool calls, observations, and executed actions, not the model's reasoning), which the reseller stores and processes to meter usage. A watermark must therefore survive an adversary with full read/write access to the very evidence it is detected from; existing agent watermarks do not, as their attribution is read straight off that log. We present TRACE, to our knowledge the first agent watermark that is distortion-free in its action choices, self-synchronizing under deletion, and unconditionally invariant under rewriting. Deletion desynchronizes a position-derived key and rewriting alters content, so a deletion-robust key must come from content and a rewrite-robust key from position, and no single key serves both. A trajectory, however, has room for two watermarks. TRACE superposes a selection channel that sets which action is chosen, keyed on local content with a distortion-free sampler, so the agent's distribution is provably unchanged and detection resynchronizes after deletions, and a tally channel that sets how many records each decision group holds, keyed on the log's skeleton alone, which no rewriting can touch. We prove this behavioral watermark's signal is bought with decision entropy, each decision paying at least half its entropy and deterministic decisions nothing, and that erasing both channels forces the reseller to corrupt the trajectories it resells. On ToolBench and ALFWorld, TRACE matches the unwatermarked agent's success rate while its selection channel reaches detection scores near z = 100 on long-horizon trajectories, stays detectable under 70% step deletion, and keeps a tally channel exactly unchanged under LLM rewriting of any strength.",Zheng Gao; Xiaoyu Li; Xiaoyan Feng; Jiaojiao Jiang; Yang Song; Yulei Sui; Zhenchang Xing; Liming Zhu,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08400,2026-07-17T05:43:07+00:00
-ale-0306,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents,https://arxiv.org/abs/2607.07474,external,arxiv.org,"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.",Gives readers a concrete source in Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07474; inspect its method and evaluation before treating results as production evidence.,medium,README.md,814,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L814,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07474,[2607.07474] Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents,"Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not. We argue that this binary attack-success rate discards the information a defender most needs, namely how harmful the resulting action was. We introduce an action-graded harm rubric that scores an agent's tool-call trajectory on a seven-level ordinal scale (L0 to L6) according to whether the executed action was reversible, whether it crossed scope to reach another party, and whether it expanded privilege. We compute the scale two ways: a deterministic oracle that reads the trajectory and the attacker's stated goal, and a panel of three frontier language-model judges that read a tag-free account of the same trajectory. Across four victim models and two defenses on the AgentDojo workspace suite, severity grading exposes three cases the binary metric hides, including a defense that reports a zero attack-success rate while still permitting an externally visible cross-scope leak through an unfiltered tool. The judge panel reproduces the oracle with high ordinal agreement (Krippendorff's alpha = 0.91) but shares systematic blind spots that we characterize, most notably a failure to recognize escalation chains. Unlike prior work that provides harm taxonomies, harmful-task completion tests, execution-level safety benchmarks, or severity-aware simulation, our contribution is a reusable, trace-grounded severity instrument applied to the actual actions recorded in existing red-team logs. All code, prompts, and per-episode logs are released.",Harry Owiredu-Ashley,2026-07-08,2026,arXiv,arXiv,,"8 pages, 6 figures. Code and artifacts: https://github.com/Harry-Ashley/action-graded-severity",cs.CR,arxiv-api,,,,,,,2607.07474,2026-07-17T05:43:07+00:00
-ale-0307,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting,https://arxiv.org/abs/2607.07433,external,arxiv.org,"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.",Gives readers a concrete source in Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07433; inspect its method and evaluation before treating results as production evidence.,medium,README.md,815,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L815,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07433,[2607.07433] Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting,"The growing adoption of agentic LLM applications has introduced a new threat previously named as promptware. While prior work has established that adversaries can exploit direct channels to LLM applications to apply promptware under weak threat models, many applications do not provide any direct channels that could be exploited for prompt injection beyond the Internet. This raises a question: can attackers exploit LLM applications at scale without any direct channels in practical threat models? In this work, we show that the inherent tendency of LLMs to hallucinate resource identifiers can be exploited to amplify untargeted promptware attacks that pull adversarial prompts at scale and could be exploited to establish a botnet. We introduce adversarial hallucination squatting, a technique in which attackers identify trending resources (e.g., popular repositories, popular skills, etc.), compute the LLM distribution of hallucinations on the trending resource names, and preemptively register them to host adversarial prompts. By leveraging the predictability and transferability of hallucinations across foundational LLMs and to application layers, adversaries can significantly amplify the reach of untargeted promptware under weak threat models and establish a botnet by exploiting LLM applications to install a bot on the device that pulled the compromised hallucinated resource from the Inter. We empirically demonstrate that hallucinated resource generation occurs at high rates, up to 85% in repository cloning scenarios and up to 100% in skill installation, and that these hallucinations transfer between foundational models and different prompts. We demonstrate the practicality of adversarial hallucination squatting against various production LLM applications with integrated terminals in their set of tools, achieving remote tool execution and remote code execution.",Aya Spira; Stav Cohen; Elad Feldman; Ron Bitton; Avishai Wool; Ben Nassi,2026-07-08,2026,arXiv,arXiv,,Website: https://sites.google.com/view/agentic-botnets/home,cs.CR,arxiv-api,,,,,,,2607.07433,2026-07-17T05:43:07+00:00
-ale-0308,Securing Unattended Loops,securing-unattended-loops,Blog,📝,GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos,https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/,external,noma.security,"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.",Gives readers a concrete source in GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos when they need to bound risk before recurring or unattended execution.,"Contextual source from noma.security; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,816,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L816,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake,operator;security,practitioner-analysis,ok,https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/,GitLost: How We Tricked GitHub’s AI Agent into Leaking Private Repos - Noma Security,"TL;DR: Noma Labs discovered a critical prompt injection vulnerability within GitHub’s new Agentic Workflows, allowing an unauthenticated attacker to silently pull data from private repositories by posting a crafted GitHub Issue in a public repository belonging to the same organization as the private repositories. Noma Labs named the vulnerability GitLost. Introduction GitHub recently launched […]",,,,,noma.security,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0309,Securing Unattended Loops,securing-unattended-loops,Benchmark,🧪,ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents,https://arxiv.org/abs/2607.07774,external,arxiv.org,"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.",Gives readers a concrete source in ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents when they need to bound risk before recurring or unattended execution.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,817,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L817,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;budget,researcher;evaluator;operator;security,benchmark,ok,https://arxiv.org/abs/2607.07774,[2607.07774] ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents,"As LLM agents take on offensive security work, a single out-of-scope tool call can breach a client's engagement boundary, disrupt production, or void a bug-bounty finding. Unlike a fixed safety policy, the boundary that matters is declared in the user's request and must be inferred from intent. That challenge is sharpened by the adversarial nature of offensive security: the same tool call is in or out of scope depending not on the action itself but on the target it touches and the context in which it runs, which no fixed policy can enumerate in advance. We study pre-execution gating: a cheap, trusted LLM judge inspects each call proposed by a strong, swappable agent, and accepts or rejects it before it runs. We introduce ScopeJudge, a benchmark of 4,897 tool calls (7.7% scope violations) from agent trajectories on tasks engineered to tempt agents out of scope and labeled at the call level by professional penetration testers, with substantial inter-grader agreement (Fleiss kappa = 0.64) that sets an expert agreement reference point of F1 = 0.78. We evaluate eight judge models under five transcript strategies, varying how much context the judge sees, from the static policy alone to the full raw transcript, and chart the resulting cost-accuracy Pareto frontier. We find that a static policy is structurally insufficient for scope enforcement: blind to the user's request, judge recall collapses to near zero, confirming that scope lives in the request and that request-conditioned monitoring is necessary. Because a missed violation costs more than a spurious rejection, we report precision, recall, and F1 separately and recommend two operating points: a cost-sensitive configuration and a recall-first one for high-stakes deployments. We release the ScopeJudge dataset to support real-time monitoring and scalable oversight of autonomous security agents.",Shane Caldwell; Max Harley; Ads Dawson; Michael Kouremetis; Vincent Abruzzo; Will Pearce,2026-07-08,2026,arXiv,arXiv,,"22 pages, 4 figures, 4 tables",cs.CR,arxiv-api,,,,,,,2607.07774,2026-07-17T05:43:07+00:00
-ale-0310,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors,https://arxiv.org/abs/2607.07368,external,arxiv.org,"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.",Gives readers a concrete source in Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07368; inspect its method and evaluation before treating results as production evidence.,medium,README.md,818,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L818,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;delegation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07368,[2607.07368] Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors,"AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent. AI Control usually studies a single agent in one trajectory, but real deployments run many agents over shared infrastructure, and the most severe risks (model-weight exfiltration, training-run poisoning) plausibly need several agents acting in concert. We initiate the empirical study of multi-agent AI control, formalising distributed attacks in which several agents jointly aim for a malicious goal. We develop FakeLab: a synthetic AI-lab codebase (9 services, 86 benign tasks, 4 attack objectives). We evaluate single agent monitoring against distributed attacks, varying the number of agents, their coordination, model capabilities and precise monitoring configuration. Our central finding is the fragmentation effect: as more agents coordinate to attack, per-agent monitoring becomes less likely to catch any of the attackers. Secondly, this is not driven by the ratio of benign to malicious code -- we argue this is likely dependent on model capability. Third, an explicit planner amplifies the fragmentation effect, raises the rate of completing the attack up to sevenfold, and mildly sharpens executors' poor monitor-theory-of-mind. Finally: a weak ""trusted"" monitor misses most attacks, while a stronger one cuts undetected success by more than an order of magnitude but remains imperfect. We are happy to share FakeLab, our control setting, with safety researchers on request.",Oliver Makins; Orazio Angelini; Zohreh Shams; Mary Phuong,2026-07-08,2026,arXiv,arXiv,,Submitted to NeurIPS; 81 pages; 32 figures and 24 tables,cs.LG,arxiv-api,,,,,,,2607.07368,2026-07-17T05:43:07+00:00
-ale-0311,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions,https://arxiv.org/abs/2607.07461,external,arxiv.org,"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.",Gives readers a concrete source in Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07461; inspect its method and evaluation before treating results as production evidence.,medium,README.md,819,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L819,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07461,[2607.07461] Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions,"Large language models (LLMs) are increasingly deployed as autonomous agents that interact with external tools and services via the Model Context Protocol (MCP), a standardized interface for dynamic tool invocation. While MCP simplifies integration, it also expands the attack surface and enables generic exploits across multiple servers. Despite prior work on malicious MCP servers, the vulnerability landscape of MCP servers remains underexplored. In this work, we systematically analyze MCP server vulnerabilities, focusing on metadata characteristics, vulnerable code patterns, and community responses. Our study reveals that taint-style vulnerabilities constitute a substantial fraction of MCP server vulnerabilities, require significant code modifications to remediate, and are met with slow community responses. Motivated by these findings, we propose SPELLSMITH, presenting a novel textbased avenue for shielding taint-style vulnerabilities in MCP servers. In particular, SPELLSMITH analyzes the high-risk capabilities exposed by an MCP server and combines them with tool descriptions and parameter semantics to identify potential taint-style vulnerability risks, thereby constructing a tool-level risk profile. Then, SPELLSMITH leverages the Description property of the protocol to embed behavioral guidance (Description Enhancement Module) and exploits LLMs' self-reflection capabilities (Self-Reflection Module) to iteratively evaluate and refine outputs. By strengthening LLM internal decision-making, SPELLSMITH provides an active and unified mitigation strategy that generalizes across multiple vulnerabilities, reducing reliance on context-specific code-level fixes. Our experiments demonstrate that SPELLSMITH effectively mitigates taint-style vulnerability exploitation in MCP servers, highlighting its practical applicability and advantages over traditional code-level mitigations.",Yang Shi; Jiaheng Fu; Yihe Huang; Ruixiang Wu; Chengyao Sun; Kaifeng Huang,2026-07-08,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.07461,2026-07-17T05:43:07+00:00
-ale-0312,Securing Unattended Loops,securing-unattended-loops,Blog,📝,Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits,https://factory.ai/news/droid-shield-2-0,external,factory.ai,"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.",Gives readers a concrete source in Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits when they need to bound risk before recurring or unattended execution.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,820,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L820,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,operator;security,practitioner-analysis,ok,https://factory.ai/news/droid-shield-2-0,Droid Shield 2.0: learned secret detection | Factory.ai Factory.ai Logo Arrow Right Icon,"Droid Shield 2.0: learned secret detection Factory's Droids write, refactor, and commit code autonomously at a volume th...",Factory,2026-07-01,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0313,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,destructive_command_guard,https://github.com/Dicklesworthstone/destructive_command_guard,external,github.com,"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.",Gives readers a concrete source in destructive_command_guard when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (5,033 stars; 189 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,821,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L821,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,builder;operator;security,source-implementation,ok,https://github.com/Dicklesworthstone/destructive_command_guard,GitHub - Dicklesworthstone/destructive_command_guard: The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents. · GitHub,The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents. - Dicklesworthstone/destructive_command_guard,,2026-01-07,2026,Dicklesworthstone/destructive_command_guard,GitHub,,,,github-api,Dicklesworthstone/destructive_command_guard,5033,189,NOASSERTION,2026-01-07T22:27:34Z,2026-07-17T05:40:21Z,,2026-07-17T05:43:07+00:00
-ale-0314,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution,https://ainowinstitute.org/publications/friendly-fire-exploit-brief,external,ainowinstitute.org,"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.",Gives readers a concrete source in Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution when they need to bound risk before recurring or unattended execution.,Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,822,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L822,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-paper,ok,https://ainowinstitute.org/publications/friendly-fire-exploit-brief,Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution - AI Now Institute,"AI Now’s latest research demonstrates a critical attack vector on popular AI agents, built by Anthropic and OpenAI, when used for defensive purposes that actually turn the agent against its user.",Boyan Milanov,2026-07-08,2026,,AI Now Institute,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0315,Securing Unattended Loops,securing-unattended-loops,Blog,📝,How We Contain Claude Across Products,https://www.anthropic.com/engineering/how-we-contain-claude,external,www.anthropic.com,"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.",Gives readers a concrete source in How We Contain Claude Across Products when they need to bound risk before recurring or unattended execution.,"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,823,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L823,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,operator;security,practitioner-analysis,ok,https://www.anthropic.com/engineering/how-we-contain-claude,How we contain Claude across products \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0316,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability,https://arxiv.org/abs/2607.11086,external,arxiv.org,"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.",Gives readers a concrete source in Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.11086; inspect its method and evaluation before treating results as production evidence.,medium,README.md,824,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L824,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.11086,[2607.11086] Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability,"The Model Context Protocol (MCP) has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical. In practice, due to the absence of large-scale runtime MCP servers, such understanding largely relies on security scanners applied to a small number of cases, yet the reliability of these assessments remains unclear. In this study, we revisit how MCP security is measured. We present MCPZoo, the largest collection of MCP servers for dynamic analysis to date. MCPZoo is constructed through a multi-agent framework for transforming in-the-wild static repositories into dynamic services. The framework emulates how human experts build, diagnose, and iteratively repair deployment and runtime defects by combining environment inference with feedback-driven refinement. To ensure practical interactivity at runtime, the servers are validated via real protocol interactions. As a result, MCPZoo contains 64,611 unique MCP servers (113,927 in total), with more than 37,288 supporting dynamic analysis. Leveraging MCPZoo, we conduct the first ecosystem-scale measurement of MCP servers and the scanners that analyze them. While existing scanners report that 96.89% of servers are risky, we find that these signals are unreliable. In particular, manual validation shows that less than 50% of sampled alerts are true positives, and scanner outputs exhibit clear inconsistency across scanners. Overall, MCPZoo enables large-scale, reproducible measurement of MCP server security and exposes limitations of current scanning practices. We further release a public query interface to support practical risk assessment of MCP servers.",Pei Chen; Baichao An; Mengying Wu; Binwang Wan; Geng Hong; Jinsong Chen; Xudong Pan; Jiarun Dai; Min Yang,2026-07-13,2026,arXiv,arXiv,,"18 pages, 11 figures, and 10 tables. This article substantially extends the preliminary 3-page MCPZoo dataset release arXiv:2512.15144. Includes appendices",cs.CR,arxiv-api,,,,,,,2607.11086,2026-07-17T05:43:07+00:00
-ale-0317,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming,https://arxiv.org/abs/2607.11698,external,arxiv.org,"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.",Gives readers a concrete source in Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.11698; inspect its method and evaluation before treating results as production evidence.,medium,README.md,825,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L825,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.11698,[2607.11698] Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming,"Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pace with evolving models and tools. Existing approaches mainly optimize attack success and preserve artifacts such as benchmarks, payloads, or attack programs, which record where attacks succeed but not the enabling conditions behind unsafe agent behavior. We study automated red-teaming for production LLM agents using one agentic research environment to discover reusable vulnerability knowledge about another. We present AHA, a falsifiable discovery loop that proposes a vulnerability hypothesis, constructs a falsifier, instantiates a valid attack, executes it in a sandboxed harness, reflects on the trajectory, and promotes confirmed findings into a Vulnerability Concept Graph (VCG). Each concept links an attacker-facing surface to an unsafe trajectory through a claim, enabling condition, falsifier, transfer prediction, and supporting evidence. Across Claude Code and Codex on three scenarios covering direct and indirect attacks, the discovered concepts reveal a reusable vulnerability core across models and agents. A frozen VCG requires no further search and outperforms the strongest frozen discovery baseline by 14.2 percentage points under the same single-shot protocol, while transferring across scenarios and attack channels. The resulting VCG provides an auditable artifact for production safety teams to inspect vulnerabilities, validate patches, and accumulate reusable safety knowledge. Our code is available at https://github.com/henrymao2004/Auto-research-red-teaming-in-sleep.",Xutao Mao; Xiang Zheng; Cong Wang,2026-07-13,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.11698,2026-07-17T05:43:07+00:00
-ale-0318,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents",https://arxiv.org/abs/2607.10487,external,arxiv.org,"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Gives readers a concrete source in Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.10487; inspect its method and evaluation before treating results as production evidence.,medium,README.md,826,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L826,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.10487,"[2607.10487] Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents","LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result. We study the commit boundary at which earlier authority evidence no longer authorizes a durable effect. We call this property commit-time authorization: a durable effect is authorized only if the witness that licensed its derived state remains fresh, causally prior, bound to the same effect, and eligible at commit time. We build a controlled-invalidation suite spanning browser, tool/API, and multi-agent workflows. The suite preserves the user goal and payload shape while invalidating the authority relation before durability. In the primary 54-task matrix, endpoint success remains high: 262/270 runs reach the visible result. Only 55/270 are authorized completions; among the 216 invalidating rows, 207 commit after the authorizing path has failed. All 54 clean controls remain authorized, and a separate 54-run authority-preserving check produces no unauthorized commits. We then evaluate mitigation families. Prompt caution and single-condition checks are insufficient because different hazards break different boundary conditions. Defenses work when they refresh, rebind, replan, or refuse at the durability boundary. CommitGuard, a fail-closed boundary monitor, blocks stale durable-effect attempts on protected commit surfaces when runtimes emit witness, dependency, binding, and eligibility signals. The result is a reporting and runtime-design lesson: endpoint success is a utility metric; authorized commit is a security property.",Igor Santos-Grueiro,2026-07-11,2026,arXiv,arXiv,,20 pages,cs.CR,arxiv-api,,,,,,,2607.10487,2026-07-17T05:43:07+00:00
-ale-0319,Securing Unattended Loops,securing-unattended-loops,Paper,📄,ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm,https://arxiv.org/abs/2607.10455,external,arxiv.org,"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.",Gives readers a concrete source in ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.10455; inspect its method and evaluation before treating results as production evidence.,medium,README.md,827,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L827,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.10455,[2607.10455] ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm,"Autonomous CLI agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight. Does greater autonomy invite greater risk? We introduce ANCHOR, an automated auditing framework that stress-tests CLI agents on illegal tasks grounded in public US court cases. ANCHOR deploys an auditor agent fine-tuned on dark personality data using supervised and reinforcement fine tuning. This auditor roleplays persistent malicious users who decompose tasks, reframe requests upon refusal, and adapt strategies across multi-turn interactions. Evaluating frontier CLI agents, we find that while they often refuse illegal tasks when prompted directly, compliance reaches 100\% under persistent malicious interaction. When agents comply, they frequently exceed user requests, autonomously building infrastructure for large-scale harm, including catastrophic risk scenarios such as large-scale financial fraud and bioweapon development. These findings demonstrate that current alignment techniques are insufficient for autonomous agents and underscore the need for safety evaluations against persistent, adaptive malicious users. We release ANCHOR at https://github.com/garified/anchor",Kefan Song; Yanjun Qi,2026-07-11,2026,"International Conference on Machine Learning, 2026",arXiv,,"Accepted at ICML 2026. 19 pages, 14 figures, 5 tables",cs.AI,arxiv-api,,,,,,,2607.10455,2026-07-17T05:43:07+00:00
-ale-0320,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Clawk,https://github.com/clawkwork/clawk,external,github.com,"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.",Gives readers a concrete source in Clawk when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (662 stars; 16 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,828,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L828,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/clawkwork/clawk,"GitHub - clawkwork/clawk: Give coding agents a disposable Linux VM, not your laptop · GitHub","Give coding agents a disposable Linux VM, not your laptop - clawkwork/clawk",,2026-07-06,2026,clawkwork/clawk,GitHub,,,,github-api,clawkwork/clawk,662,16,Apache-2.0,2026-07-06T21:30:38Z,2026-07-17T05:17:27Z,,2026-07-17T05:43:07+00:00
-ale-0321,Securing Unattended Loops,securing-unattended-loops,Blog,📝,Auto-Review of Agent Actions Without Synchronous Human Oversight,https://alignment.openai.com/auto-review/,external,alignment.openai.com,"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.",Gives readers a concrete source in Auto-Review of Agent Actions Without Synchronous Human Oversight when they need to bound risk before recurring or unattended execution.,"Contextual source from alignment.openai.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,829,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L829,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation,operator;security,practitioner-analysis,ok,https://alignment.openai.com/auto-review/,"Auto-review of agent actions without synchronous human oversight Illustrative deployment snapshot showing 10,000 actions, most inside the sandbox, and out-of-sandbox actions reviewed by Auto-review.","Auto-review offers a safer default for deploying coding agents, using a separate agent to approve or deny boundary-crossing actions.",Maja Trębacz; Sam Arnesen; Ollie Matthews; Dylan Hurd; Won Park; Owen Lin; Joe Gershenson,2026-04-30,2026,OpenAI,OpenAI,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0322,Securing Unattended Loops,securing-unattended-loops,Paper,📄,SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing,https://arxiv.org/abs/2607.13594,external,arxiv.org,"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.",Gives readers a concrete source in SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13594; inspect its method and evaluation before treating results as production evidence.,medium,README.md,830,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L830,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13594,[2607.13594] SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing,"LLM agents act on real-world environments through tool calls, and a single misjudged action can cause irreversible harm. The standard safeguard is a guard model that labels each proposed action as safe or unsafe, but this binary view conflates two distinct decisions: whether the action is harmful in itself, and whether it is appropriate given the user's context. It also operates at the granularity of action categories rather than individual instances, producing routine interruptions that erode autonomy and train users to wave through the most consequential alerts. We reframe the problem as a per-instance three-way routing decision over {EXECUTE, ASK, REFUSE} and instantiate it with Safety Sentry, a lightweight guard model whose inference reduces to a single decoding call. A single decoding-time threshold lets one fixed checkpoint be re-positioned across deployments of differing risk tolerance without retraining. Safety Sentry outperforms a broad set of open-weight and frontier closed-source baselines on overall accuracy and safety-related recall, while controlling both directional error rates simultaneously.",Tianyu Chen; Chujia Hu; Wenjie Wang,2026-07-15,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.13594,2026-07-17T05:43:07+00:00
-ale-0323,Securing Unattended Loops,securing-unattended-loops,Paper,📄,CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems,https://arxiv.org/abs/2607.13716,external,arxiv.org,"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.",Gives readers a concrete source in CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13716; inspect its method and evaluation before treating results as production evidence.,medium,README.md,831,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L831,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13716,[2607.13716] CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems,"Agentic AI systems increasingly act through heterogeneous runtimes: local coding hooks, SDK tools, browser automation, managed-agent traces, API gateways, and workflow engines. A single operational act such as publishing code, changing identity state, moving money, or exporting data may therefore be represented by many incompatible runtime records. This makes a basic governance question difficult to answer: what action was actually approved, what evidence binds the approval to execution, and can an independent verifier reproduce the same action identity later? This paper presents Canonical Action Verification and Attestation (CAVA), a runtime-semantics layer for converting heterogeneous agent activity into canonical runtime action objects. CAVA is positioned below Proof-Carrying Agent Actions (PCAA): PCAA defines the deployer-owned route-review-prove governance process, while CAVA defines the stable action object that process governs. The paper formalizes canonical action identity, semantic pattern detection, approval binding, receipt integrity, runtime-portable projection, and optional attestation substrates. We study a reference implementation through a 96-seed, 384-variant benchmark covering semantic equivalence, semantic separation, wrapper bypass, false-positive control, approval binding, receipt reproducibility, attestation tamper detection, runtime portability, semantic pattern detection, policy degradation, and Azure deployment drills. The contribution is a systems formulation of action-level canonicalization and policy-addressable semantic patterns as a necessary substrate for deployer-side AI governance.",Zexun Wang,2026-07-15,2026,arXiv,arXiv,,"35 pages. Working paper on canonical action verification, runtime governance, semantic pattern detection, and approval-bound action receipts",cs.AI,arxiv-api,,,,,,,2607.13716,2026-07-17T05:43:07+00:00
-ale-0324,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement",https://arxiv.org/abs/2607.13718,external,arxiv.org,"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Gives readers a concrete source in How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.13718; inspect its method and evaluation before treating results as production evidence.,medium,README.md,832,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L832,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13718,"[2607.13718] How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement","As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can cause agents to leak private information to third parties. As autonomous systems, agents also present the more active danger of performing sensitive tasks, such as bank transactions, without the user's intent or authorization. Recognizing this challenge, the agentic security community has developed numerous proposals for secure agentic systems. Much of this work has focused on product-level approaches, where agentic system developers determine and apply the same security policies and permissions to all users. Yet different users have different needs and preferences, necessitating support for user-level permissions policies in agentic AI systems. To understand how user-level permissions are handled in AI agent systems, we survey 21 proposals for agent permissions systems. From this review, we construct a taxonomy of how different systems specify user-level permissions policies, both at the user interface and internally; derive internal policies from user input; and enforce those policies at run-time. We then analyze five prominent commercial agents and compare their permissions handling to agentic permissions systems in the literature. We identify several high-level themes across the literature and commerical agents, as well as multiple gaps where future work is needed.",Alexandra E. Michael; Franziska Roesner,2026-07-15,2026,arXiv,arXiv,,"15 pages, 4 figures",cs.CR,arxiv-api,,,,,,,2607.13718,2026-07-17T05:43:07+00:00
-ale-0325,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,https://arxiv.org/abs/2607.14166,external,arxiv.org,"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.",Gives readers a concrete source in Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14166; inspect its method and evaluation before treating results as production evidence.,medium,README.md,833,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L833,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14166,[2607.14166] Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,"Production LLM-agent frameworks expose control primitives -- human-in-the-loop approval gates, run cancellation, and execution timeouts -- whose names and documentation imply barrier semantics: while a run is paused, cancelled, or timed out, no gated side effect executes. We show this implied contract holds on none of the six widely used open-source frameworks we test. Model-free differential probes isolate a recurring sibling leak -- an approval gate suspends its own branch while a sibling branch's effect executes during the pause, so a later rejection cannot prevent it -- in every framework shipping a pre-execution gate (five of six), plus three further gaps: replay double-execution, cancellation orphans, and timeout zombies. The hazard is reachable, not merely constructible: under an a-priori-fixed protocol, frontier models emit the leak-triggering plan shape at pooled rates up to 14%, and when live models drive the unmodified frameworks under an approval pause, 215 of 1,200 runs execute their effect during the pause, across three schedulers and two language runtimes. To repair the measured gaps we present SOUNDGATE, an environment-external effect gate in Rust through which every side effect must be admitted, enforcing hold-until-decided, reject-cancels, dedup-on-replay, and fence-on-cancel -- one property per violation class -- under a stated complete-mediation contract discharged for network egress by kernel-enforced routes. We verify the properties over a model of the admission core (Verus; TLA+/TLC, exhaustive to 7.5e7 states; TLAPS), model-check the deployed Rust with Loom, and bridge model to code by differential conformance over 1.2e7 operations -- refinement evidence, not a mechanized proof. SOUNDGATE blocks every measured violation end-to-end on all six frameworks while releasing legitimate effects, at about 1 ms admission per write and 12k-26k durable admissions per second.",Sajjad Khan,2026-07-15,2026,arXiv,arXiv,,"31 pages, 3 figures, 11 tables",cs.SE,arxiv-api,,,,,,,2607.14166,2026-07-17T05:43:07+00:00
-ale-0326,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,https://arxiv.org/abs/2607.14611,external,arxiv.org,"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.",Gives readers a concrete source in Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14611; inspect its method and evaluation before treating results as production evidence.,medium,README.md,834,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L834,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14611,[2607.14611] Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,"A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases. While this makes agents more useful and self-improving, it also creates a new attack surface for prompt injections in which malicious instructions can be embedded within persistent files and influence future behavior. In this work, we study prompt injection attacks in memory-based agentic systems using a sandboxed synthetic workspace. We evaluate two agentic systems, Anthropic Claude Code and OpenAI Codex, across four models: Claude Haiku 4.5, Claude Opus 4.7, GPT-5.2, and GPT-5.5. Our results show that although it is difficult to make an agent overwrite its own memory files using untrusted external content, payloads already planted in those files can successfully attack current and future sessions. Attack success and payload persistence vary substantially across systems, models, adversarial goals, and multi-session attack sequences. These findings show that persistent memory changes the threat model for prompt injection and motivate defenses that protect memory updates without removing useful agent adaptation.",Soham Gadgil; David Alexander; Sai Sunku; Franziska Roesner,2026-07-16,2026,arXiv,arXiv,,Preprint,cs.CR,arxiv-api,,,,,,,2607.14611,2026-07-17T05:43:07+00:00
-ale-0327,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents",https://arxiv.org/abs/2607.15143,external,arxiv.org,"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Gives readers a concrete source in Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.15143; inspect its method and evaluation before treating results as production evidence.,medium,README.md,835,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L835,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.15143,"[2607.15143] Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents","AI coding agents set up projects by reading documentation and installing the dependencies it lists, without verifying their names, sources, or known vulnerabilities. By editing only a README, requirements file, or Makefile, an attacker can redirect the agent to an untrusted registry, a known-vulnerable version, or a wrong-but-plausible name: documentation becomes a vector for code execution. We present the first systematic evaluation of package-install-time supply-chain attacks delivered through ordinary project-setup documentation across production coding-agent harnesses, probing frontier models on twelve scenarios in five attack classes, grounded in documented incidents. The same model catches an attack through one harness and installs it through another: install-time security rests on the harness-model combination, not the model alone. Agents catch blatant typosquats reliably, but plausible separator-confusion names (azurecore for azure-core) slip through, and how often depends on the harness-model pairing. Source-based attacks like registry redirection are missed almost everywhere. The source blind spot recurs on npm and Cargo, where nearly every model installs the untrusted dependency; name detection carries over less consistently across ecosystems. Security-oriented prompts recover part of the gap but only for the dimension they name; a deterministic pre-install check that verifies names, sources, and versions before any code runs closes most of it.",Aadesh Bagmar; Pushkar Saraf,2026-07-16,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.15143,2026-07-17T05:43:07+00:00
-ale-0328,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Effective Context Engineering for AI Agents,https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents,external,www.anthropic.com,Anthropic guide to context as managed runtime state rather than a prompt dump.,Anthropic guide to context as managed runtime state rather than a prompt dump.,Anthropic guide to context as managed runtime state rather than a prompt dump.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Anthropic guide to context as managed runtime state rather than a prompt dump.,"Gives readers a concrete source in Effective Context Engineering for AI Agents when they need to carry context, state, and receipts across runs and failures.","Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,843,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L843,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,technical-documentation,ok,https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents,Effective context engineering for AI agents \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0329,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs,https://ninadpathak.com/blog/agent-harnesses/,external,ninadpathak.com,"Covers execution loops, state, checkpointing, observers, and replayability.","Covers execution loops, state, checkpointing, observers, and replayability.","Covers execution loops, state, checkpointing, observers, and replayability.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Covers execution loops, state, checkpointing, observers, and replayability.","Gives readers a concrete source in Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs when they need to carry context, state, and receipts across runs and failures.","Contextual source from ninadpathak.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,844,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L844,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,practitioner-analysis,ok,https://ninadpathak.com/blog/agent-harnesses/,Agent Harnesses: the Infrastructure Layer Your Llm Agent Actually Needs | Ninad Pathak,"Every production AI agent needs a harness. Here is what one contains, why frameworks often are not enough, and how to build the layer that actually determines reliability.",,,,,ninadpathak.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0330,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,The Agent Loop Is the New OS,https://www.harness.io/blog/agent-loop-new-os,external,www.harness.io,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,"Gives readers a concrete source in The Agent Loop Is the New OS when they need to carry context, state, and receipts across runs and failures.","Contextual source from www.harness.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,845,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L845,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context,builder,practitioner-analysis,ok,https://www.harness.io/blog/agent-loop-new-os,The Agent Loop Is the New OS | Harness Blog | Harness Share in X Share in Facebook Share in LinkedIn Search in ChatGpt Github icon LinkedIn icon Facebook icon Instagram icon Twitter icon,"The Harness MCP server treats the AI agent loop as an operating system, mapping the LLM to the CPU and the Context Window to RAM. Learn how this design uses 10 generic, composable tools to abstract complexity and keep the context window clean for higher-quality, cost-efficient AI agent reasoning. | Blog",,2026,2026,,Harness.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0331,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Harness engineering for coding agent users,https://martinfowler.com/articles/harness-engineering.html,external,martinfowler.com,"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Gives readers a concrete source in Harness engineering for coding agent users when they need to carry context, state, and receipts across runs and failures.","Contextual source from martinfowler.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,846,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L846,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://martinfowler.com/articles/harness-engineering.html,Harness engineering for coding agent users,"A mental model for building trust in coding agents through feedforward guides, feedback sensors, and iterative harness engineering.",,,,,martinfowler.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0332,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Context Engineering,https://simonwillison.net/2025/Jun/27/context-engineering/,external,simonwillison.net,"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Gives readers a concrete source in Context Engineering when they need to carry context, state, and receipts across runs and failures.","Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,847,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L847,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;state,builder,practitioner-analysis,ok,https://simonwillison.net/2025/Jun/27/context-engineering/,Context engineering,The term context engineering has recently started to gain traction as a better alternative to prompt engineering. I like it. I think this one may have sticking power. Here's an …,Simon Willison,,2025,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0333,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentic Coding in 2026,https://sourcegraph.com/blog/agentic-coding,external,sourcegraph.com,"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Gives readers a concrete source in Agentic Coding in 2026 when they need to carry context, state, and receipts across runs and failures.","Contextual source from sourcegraph.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,848,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L848,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://sourcegraph.com/blog/agentic-coding,Agentic Coding in 2026: A Practical Guide for Big Code | Sourcegraph,"Learn what agentic coding is, how AI coding agents work in real engineering orgs, and how to give them the codebase context they need to ship safely.",,,,,Sourcegraph,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0334,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentic AI State Management with ScyllaDB and LangGraph,https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/,external,www.scylladb.com,"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Gives readers a concrete source in Agentic AI State Management with ScyllaDB and LangGraph when they need to carry context, state, and receipts across runs and failures.","Contextual source from www.scylladb.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,849,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L849,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,practitioner-analysis,ok,https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/,Agentic AI State Management with ScyllaDB and LangGraph | ScyllaDB,"How to combine LangGraph and ScyllaDB for durable state management, crash recovery, and a highly available backend for your agentic AI applications.",Cynthia Dunlop,2026-04-08,2026,,ScyllaDB,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0335,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Mem0,https://github.com/mem0ai/mem0,external,github.com,"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Gives readers a concrete source in Mem0 when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (61,020 stars; 7,102 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,850,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L850,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/mem0ai/mem0,GitHub - mem0ai/mem0: Universal memory layer for AI Agents · GitHub,Universal memory layer for AI Agents. Contribute to mem0ai/mem0 development by creating an account on GitHub.,,2023-06-20,2023,mem0ai/mem0,GitHub,,,,github-api,mem0ai/mem0,61020,7102,Apache-2.0,2023-06-20T08:58:36Z,2026-07-17T05:40:12Z,,2026-07-17T05:43:07+00:00
-ale-0336,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Letta,https://github.com/letta-ai/letta,external,github.com,"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Gives readers a concrete source in Letta when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (23,825 stars; 2,527 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,851,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L851,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/letta-ai/letta,GitHub - letta-ai/letta: Platform for stateful agents: AI with advanced memory that can learn and self-improve over time. · GitHub,Platform for stateful agents: AI with advanced memory that can learn and self-improve over time. - letta-ai/letta,,2023-10-11,2023,letta-ai/letta,GitHub,,,,github-api,letta-ai/letta,23825,2527,Apache-2.0,2023-10-11T07:38:37Z,2026-07-17T05:16:36Z,,2026-07-17T05:43:07+00:00
-ale-0337,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Zep,https://github.com/getzep/zep,external,github.com,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,"Gives readers a concrete source in Zep when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (4,759 stars; 641 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,852,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L852,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/getzep/zep,"GitHub - getzep/zep: Zep | Examples, Integrations, & More · GitHub","Zep | Examples, Integrations, & More. Contribute to getzep/zep development by creating an account on GitHub.",,2023-04-29,2023,getzep/zep,GitHub,,,,github-api,getzep/zep,4759,641,Apache-2.0,2023-04-29T14:06:22Z,2026-07-17T03:47:47Z,,2026-07-17T05:43:07+00:00
-ale-0338,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,LangMem,https://github.com/langchain-ai/langmem,external,github.com,"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","Gives readers a concrete source in LangMem when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (1,565 stars; 176 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,853,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L853,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/langchain-ai/langmem,GitHub - langchain-ai/langmem · GitHub,Contribute to langchain-ai/langmem development by creating an account on GitHub.,,2025-01-21,2025,langchain-ai/langmem,GitHub,,,,github-api,langchain-ai/langmem,1565,176,MIT,2025-01-21T20:06:59Z,2026-07-17T02:32:51Z,,2026-07-17T05:43:07+00:00
-ale-0339,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Beads,https://github.com/steveyegge/beads,external,github.com,"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Gives readers a concrete source in Beads when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (25,380 stars; 1,700 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,854,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L854,,Persist,persist,"Carry context, state, and receipts across runs.",intake;context;state,builder,source-implementation,ok,https://github.com/gastownhall/beads,GitHub - gastownhall/beads: Beads - A memory upgrade for your coding agent · GitHub,Beads - A memory upgrade for your coding agent. Contribute to gastownhall/beads development by creating an account on GitHub.,,2025-10-12,2025,steveyegge/beads,GitHub,,,,github-api,steveyegge/beads,25380,1700,MIT,2025-10-12T03:09:46Z,2026-07-17T03:18:10Z,,2026-07-17T05:43:07+00:00
-ale-0340,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,ARC: Active and Reflection-driven Context Management for Long-Horizon Agents,https://arxiv.org/abs/2601.12030,external,arxiv.org,"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Gives readers a concrete source in ARC: Active and Reflection-driven Context Management for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2601.12030; inspect its method and evaluation before treating results as production evidence.,medium,README.md,855,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L855,,Persist,persist,"Carry context, state, and receipts across runs.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2601.12030,[2601.12030] ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents,"Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histories grow. This degradation, known as context rot, reflects a failure to maintain coherent and task-relevant internal states over extended reasoning horizons. Existing approaches primarily manage context through raw accumulation or passive summarization, treating it as a static artifact and allowing early errors or misplaced emphasis to persist. Motivated by this perspective, we propose ARC, which is the first framework to systematically formulate context management as an active, reflection-driven process that treats context as a dynamic internal reasoning state during execution. ARC operationalizes this view through reflection-driven monitoring and revision, allowing agents to actively reorganize their working context when misalignment or degradation is detected. Experiments on challenging long-horizon information-seeking benchmarks show that ARC consistently outperforms passive context compression methods, achieving up to an 11% absolute improvement in accuracy on BrowseComp-ZH with Qwen2.5-32B-Instruct.",Yilun Yao; Shan Huang; Elsie Dai; Zhewen Tan; Zhenyu Duan; Shousheng Jia; Yanbing Jiang; Tong Yang,2026-01-17,2026,arXiv,arXiv,,"15 pages, 5 figures",cs.AI,arxiv-api,,,,,,,2601.12030,2026-07-17T05:43:07+00:00
-ale-0341,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers",https://arxiv.org/abs/2603.07670,external,arxiv.org,"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Gives readers a concrete source in Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2603.07670; inspect its method and evaluation before treating results as production evidence.,medium,README.md,856,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L856,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.07670,"[2603.07670] Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers","Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated. Memory -- the ability to persist, organize, and selectively recall information across interactions -- is what turns a stateless text generator into a genuinely adaptive agent. This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026. We formalize agent memory as a \emph{write--manage--read} loop tightly coupled with perception and action, then introduce a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy. Five mechanism families are examined in depth: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. On the evaluation side, we trace the shift from static recall benchmarks to multi-session agentic tests that interleave memory with decision-making, analyzing four recent benchmarks that expose stubborn gaps in current systems. We also survey applications where memory is the differentiating factor -- personal assistants, coding agents, open-world games, scientific reasoning, and multi-agent teamwork -- and address the engineering realities of write-path filtering, contradiction handling, latency budgets, and privacy governance. The paper closes with open challenges: continual consolidation, causally grounded retrieval, trustworthy reflection, learned forgetting, and multimodal embodied memory.",Pengfei Du,2026-03-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.07670,2026-07-17T05:43:07+00:00
-ale-0342,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering",https://arxiv.org/abs/2604.08224,external,arxiv.org,"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Gives readers a concrete source in Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2604.08224; inspect its method and evaluation before treating results as production evidence.,medium,README.md,857,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L857,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.08224,"[2604.08224] Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable skills, interaction protocols, and the surrounding harness that makes these modules reliable in practice. This paper reviews that shift through the lens of externalization. Drawing on the idea of cognitive artifacts, we argue that agent infrastructure matters not merely because it adds auxiliary components, but because it transforms hard cognitive burdens into forms that the model can solve more reliably. Under this view, memory externalizes state across time, skills externalize procedural expertise, protocols externalize interaction structure, and harness engineering serves as the unification layer that coordinates them into governed execution. We trace a historical progression from weights to context to harness, analyze memory, skills, and protocols as three distinct but coupled forms of externalization, and examine how they interact inside a larger agent system. We further discuss the trade-off between parametric and externalized capability, identify emerging directions such as self-evolving harnesses and shared agent infrastructure, and discuss open challenges in evaluation, governance, and the long-term co-evolution of models and external infrastructure. The result is a systems-level framework for explaining why practical agent progress increasingly depends not only on stronger models, but on better external cognitive infrastructure.",Chenyu Zhou; Huacan Chai; Wenteng Chen; Zihan Guo; Rong Shan; Yuanyi Song; Tianyi Xu; Yingxuan Yang; Aofan Yu; Weiming Zhang; Congming Zheng; Jiachen Zhu; Zeyu Zheng; Zhuosheng Zhang; Xingyu Lou; Changwang Zhang; Zhihui Fu; Jun Wang; Weiwen Liu; Jianghao Lin; Weinan Zhang,2026-04-09,2026,arXiv,arXiv,,"54 pages, tech report on Externalization in LLM Agents",cs.SE,arxiv-api,,,,,,,2604.08224,2026-07-17T05:43:07+00:00
-ale-0343,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Meta Context Engineering via Agentic Skill Evolution,https://arxiv.org/abs/2601.21557,external,arxiv.org,"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","Gives readers a concrete source in Meta Context Engineering via Agentic Skill Evolution when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2601.21557; inspect its method and evaluation before treating results as production evidence.,medium,README.md,858,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L858,,Persist,persist,"Carry context, state, and receipts across runs.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2601.21557,[2601.21557] Meta Context Engineering via Agentic Skill Evolution,"The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and predefined context schemas. They impose structural biases and restrict context optimization to a narrow, intuition-bound design space. To address this, we introduce Meta Context Engineering (MCE), a bi-level framework that supersedes static CE heuristics by co-evolving CE skills and context artifacts. In MCE iterations, a meta-level agent refines engineering skills via agentic crossover, a deliberative search over the history of skills, their executions, and evaluations. A base-level agent executes these skills, learns from training rollouts, and optimizes context as flexible files and code. We evaluate MCE across five disparate domains under offline and online settings. MCE demonstrates consistent performance gains, achieving 5.6--53.8% relative improvement over state-of-the-art agentic CE methods (mean of 16.9%), while maintaining superior context adaptability, transferability, and efficiency in both context usage and training.",Haoran Ye; Xuning He; Vincent Arak; Haonan Dong; Guojie Song,2026-01-29,2026,arXiv,arXiv,,"46 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2601.21557,2026-07-17T05:43:07+00:00
-ale-0344,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Are We Ready for an Agent-Native Memory System?,https://arxiv.org/abs/2606.24775,external,arxiv.org,"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Gives readers a concrete source in Are We Ready for an Agent-Native Memory System? when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.24775; inspect its method and evaluation before treating results as production evidence.,medium,README.md,859,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L859,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.24775,[2606.24775] Are We Ready For An Agent-Native Memory System?,"Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box. As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from a data management perspective. We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets. Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck. Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability. Finally, we reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization. Based on these findings, we identify promising directions towards building truly agent-native memory systems. The code is publicly available at https://github.com/OpenDataBox/MemoryData.",Wei Zhou; Xuanhe Zhou; Shaokun Han; Hongming Xu; Guoliang Li; Zhiyu Li; Feiyu Xiong; Fan Wu,2026-06-23,2026,arXiv,arXiv,,Paper list available at: https://github.com/OpenDataBox/awesome-agent-memory. Source code available at: https://github.com/OpenDataBox/MemoryData,cs.CL,arxiv-api,,,,,,,2606.24775,2026-07-17T05:43:07+00:00
-ale-0345,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Self-Evolving World Models for LLM Agent Planning,https://arxiv.org/abs/2606.30639,external,arxiv.org,"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Gives readers a concrete source in Self-Evolving World Models for LLM Agent Planning when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.30639; inspect its method and evaluation before treating results as production evidence.,medium,README.md,860,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L860,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.30639,[2606.30639] Self-Evolving World Models for LLM Agent Planning,"World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignored, misused, or even degrade downstream decision-making. In this paper, we introduce WorldEvolver, a self-evolving world model framework that revises its deployment-time context while keeping the downstream agent and all model parameters frozen. WorldEvolver integrates three modules: (i) Episodic Memory, which exploits real action transitions through retrieval-based simulation; (ii) Semantic Memory, which extracts persistent heuristic rules from prediction-observation mismatches; and (iii) Selective Foresight, which filters low-confidence predictions before integrating them into agent reasoning context. We evaluate WorldEvolver on ALFWorld and ScienceWorld, measuring world model prediction accuracy on Word2World and downstream agent success rate on AgentBoard. Extensive experiments show that WorldEvolver achieves the highest prediction accuracy across three backbones and leads other world model baselines on downstream agent success rate, demonstrating that test-time memory revision enhances both predictive fidelity and planning performance.",Xuan Zhang; Wenxuan Zhang; See-Kiong Ng; Yang Deng,2026-06-29,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.30639,2026-07-17T05:43:07+00:00
-ale-0346,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Rethinking Continual Experience Internalization for Self-Evolving LLM Agents,https://arxiv.org/abs/2606.04703,external,arxiv.org,"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Gives readers a concrete source in Rethinking Continual Experience Internalization for Self-Evolving LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.04703; inspect its method and evaluation before treating results as production evidence.,medium,README.md,861,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L861,,Persist,persist,"Carry context, state, and receipts across runs.",workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.04703,[2606.04703] Rethinking Continual Experience Internalization for Self-Evolving LLM Agents,"Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital dimensions of experience internalization: (1) Experience Granularity: We find that principle-level experience is more durable than instance-level experience, as it effectively abstracts transferable strategies away from trajectory-specific details. (2) Experience Injection Pattern: Our analysis reveals that step-wise injection significantly outperforms global injection by aligning experience with intermediate decision states, a property that is critical for long-horizon tool use. (3) Internalization Regime: We demonstrate that off-policy context-distillation on high-quality teacher trajectories provides a substantially more stable training signal than on-policy context-distillation, which is inherently limited by local corrections on student-induced flawed states. Together, these insights yield a simple yet robust recipe for stable and sustainable experience internalization, providing concrete guidance for engineering self-evolving and continually learning LLMs.",Jingwen Chen; Wenkai Yang; Shengda Fan; Wenbo Nie; Chenxing Sun; Shaodong Zheng; Yangen Hu; Lu Pan; Ke Zeng; Yankai Lin,2026-06-03,2026,arXiv,arXiv,,"10 pages, 8 figures",cs.CL,arxiv-api,,,,,,,2606.04703,2026-07-17T05:43:07+00:00
-ale-0347,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,GenericAgent,https://github.com/lsdefine/GenericAgent,external,github.com,"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Gives readers a concrete source in GenericAgent when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (13,461 stars; 1,558 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,862,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L862,,Persist,persist,"Carry context, state, and receipts across runs.",objective;context,builder,source-implementation,ok,https://github.com/lsdefine/GenericAgent,"GitHub - lsdefine/GenericAgent: Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption · GitHub","Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption - lsdefine/GenericAgent",,2026-01-16,2026,lsdefine/GenericAgent,GitHub,,,,github-api,lsdefine/GenericAgent,13461,1558,MIT,2026-01-16T15:45:24Z,2026-07-17T05:00:04Z,,2026-07-17T05:43:07+00:00
-ale-0348,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Self-GC: Self-Governing Context for Long-Horizon LLM Agents,https://arxiv.org/abs/2607.00692,external,arxiv.org,"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Gives readers a concrete source in Self-GC: Self-Governing Context for Long-Horizon LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.00692; inspect its method and evaluation before treating results as production evidence.,medium,README.md,863,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L863,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00692,[2607.00692] Self-GC: Self-Governing Context for Long-Horizon LLM Agents,"Long-horizon LLM agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix. Current systems mostly rely on in-run heuristics such as chronological pruning and tool-output masking, or on final self-summary near a context limit. Heuristics are cheap but blind to future dependencies; summaries preserve narrative state but often hide exact evidence, locators, and editable artifacts. We present Self-GC, where GC denotes self-governing context while deliberately echoing garbage collection: the system does not merely reclaim unused tokens, but governs the lifecycle of agent context objects. Self-GC turns user turns, tool spans, and skill state into indexed objects; asks a side-channel planner to propose fold, mask, and prune actions; and lets the harness enforce recoverable sidecars, safe commit boundaries, and cache-aware commit. On a 33-session Hard Set, Self-GC prunes 43.95% of prefix tokens while leaving 84.85% of future continuations unaffected, compared with no-impact rates of 54.55% to 69.70% for heuristic baselines. On a 332-session production-derived suite, three planner backbones reach no-impact rates of 91.27% to 94.58%, while baselines remain at 77.71% to 87.46%. In production, an online account-level split reduces daytime average input tokens by 10% to 15%, with peak reductions near 20%. These results point to context management as runtime lifecycle control over indexed, recoverable objects rather than post hoc text cleanup.",Xubin Hao; Hongjin Meng; Xin Yin; Jiawei Zhu; Chenpeng Cao,2026-07-01,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.00692,2026-07-17T05:43:07+00:00
-ale-0349,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents,https://arxiv.org/abs/2607.05378,external,arxiv.org,"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Gives readers a concrete source in CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.05378; inspect its method and evaluation before treating results as production evidence.,medium,README.md,864,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L864,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05378,[2607.05378] CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents,"Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed. Context compaction offers a natural solution by summarizing previous interaction states and continuing the rollout under a compressed context, but incorporating compaction into reinforcement learning remains underexplored. We propose CompactionRL, a reinforcement learning strategy to train long-horizon agentic LLMs with context compaction. Our approach jointly optimizes task execution and summary generation with token-level loss normalization and cross-trajectory generalized advantage estimation. This design enables the LLM agents to learn from compacted long-horizon trajectories. We train CompactionRL on top of open models and observe consistent performance gains on agentic coding tasks. CompactionRL enables the open GLM-4.5-Air model (106B-A30B) to achieve Pass@1 scores of 66.8% on SWE-bench Verified and 24.5% on Terminal-Bench 2.0, with absolute gains of 7.0 and 3.1 points, respectively. Built upon GLM-4.7-Flash (30B-A3B), CompactionRL improves Pass@1 by 5.5 and 6.8 points, reaching 56.0% on SWE-bench Verified and 20.2% on Terminal-Bench 2.0, respectively. CompactionRL is thus deployed in the RL pipeline for training the open GLM-5.2 model (750B-A40B).",Yujiang Li; Zhenyu Hou; Yi Jing; Jie Tang; Yuxiao Dong,2026-07-06,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.05378,2026-07-17T05:43:07+00:00
-ale-0350,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,SelfMem: Self-Optimizing Memory for AI Agents,https://arxiv.org/abs/2607.03726,external,arxiv.org,"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Primary-source operational guidance rather than commentary. Resource-specific angle: Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Gives readers a concrete source in SelfMem: Self-Optimizing Memory for AI Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.03726; inspect its method and evaluation before treating results as production evidence.,medium,README.md,865,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L865,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03726,[2607.03726] SelfMem: Self-Optimizing Memory for AI Agents,"While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory systems to effectively leverage historical experience. Existing memory frameworks typically rely on fixed storage, retrieval, and summarization mechanisms, which can be rigid across different tasks and often require manual tuning. To address this limitation, we propose SelfMem, a self-optimizing memory framework. Inspired by prior work on self-improving AI, we follow the principle of ""teaching an agent to fish rather than giving it a fish."" Instead of forcing the model to follow a predefined memory strategy or format, SelfMem provides an environment with memory tools and feedback signals that allow the agent to explore, evaluate, and refine its own memory strategy. Our results show that SelfMem consistently outperforms retrieval, compression, and agent-memory baselines on BEAM across conversation scales from 100K to 1M tokens. Compared with the strongest baseline, SelfMem improves the official score by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M, respectively. Further question-type analysis shows broad robustness across diverse memory demands, and our optimization study shows that model-guided strategy refinement further improves performance.",Shu Yang; Junchao Wu; Derek F. Wong; Di Wang,2026-07-04,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.03726,2026-07-17T05:43:07+00:00
-ale-0351,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture,https://arxiv.org/abs/2607.04391,external,arxiv.org,"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Gives readers a concrete source in Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.04391; inspect its method and evaluation before treating results as production evidence.,medium,README.md,866,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L866,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.04391,[2607.04391] Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture,"Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database. MOSS is model-agnostic, storage-agnostic, and API-agnostic: it runs on any relational engine, connects to any LLM provider (or to deterministic non-LLM processes), and deploys on any infrastructure, local or cloud. Its retrieval execution is symbolic and reproducible (once a query is formulated, no LLM participates in the retrieval loop) and every step of the system, from indexing to answer formulation, is logged and inspectable, making MOSS auditable by construction. Rather than imposing an external ontology, MOSS derives its conceptual vocabulary from the corpus itself. We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued documents, 569 inductively derived concepts, 322,662 concept annotations, and eleven metadata graphs totaling approximately five million relations--across four successive infrastructure generations. While the present case is that of a single researcher, the architecture is in no way specific to one person: it serves a team, an institution, or any entity that accumulates knowledge over time. We argue that auditable, sovereign, structurally unbounded memory is a precondition for AI agents intended to accompany a person or an organization over years rather than sessions.",Serge Lacasse; Jérémie Hatier; Alex Baker,2026-07-05,2026,arXiv,arXiv,,"22 pages, 2 figures",cs.CL,arxiv-api,,,,,,,2607.04391,2026-07-17T05:43:07+00:00
-ale-0352,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems",https://arxiv.org/abs/2605.21997,external,arxiv.org,"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","Gives readers a concrete source in The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2605.21997; inspect its method and evaluation before treating results as production evidence.,medium,README.md,867,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L867,,Persist,persist,"Carry context, state, and receipts across runs.",objective;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.21997,"[2605.21997] The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems","Most agent frameworks are built around the language model: a conversation loop comes first, then tools, then rules, and finally a logging layer bolted on for observability, with state persisted as retrievable ""memory."" We describe ActiveGraph, a runtime that inverts this arrangement. The append-only event log is the source of truth; the working graph is a deterministic projection of that log; and behaviors--ordinary functions, classes, LLM-backed routines, or logic attached to typed edges--react to changes in the graph and emit new events. No component instructs another; coordination happens entirely through the shared graph. This single design decision yields three properties that retrieval-and-summarization memory systems do not provide: deterministic replay of any run from its log, cheap forking that branches a run at any event without re-executing the shared prefix, and end-to-end lineage from a high-level goal down to the individual model call that produced each artifact. We present the architecture, a determinism contract that makes replay sound, and a worked diligence example whose full causal structure is reconstructable from the log alone. We discuss--without claiming to demonstrate--why this substrate is unusually well suited to self-improving agents, and how it extends the BabyAGI lineage and prior graph-memory research.",Yohei Nakajima,2026-05-21,2026,arXiv,arXiv,,"11 pages, 1 figure. Open-source Apache-2.0 implementation with reproducible quickstart demo, deterministic replay, fork-and-diff, and lineage tracing",cs.AI,arxiv-api,,,,,,,2605.21997,2026-07-17T05:43:07+00:00
-ale-0353,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentics: Memorizing Session Transcripts Isn't Useful,https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts,external,12gramsofcarbon.com,"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","Gives readers a concrete source in Agentics: Memorizing Session Transcripts Isn't Useful when they need to carry context, state, and receipts across runs and failures.","Contextual source from 12gramsofcarbon.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,868,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L868,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts,Agentics: Memorizing Session Transcripts Isn't Useful,"Keep track of artifacts, not scratch. Alt title: Claude, please stop trying to memorize random crap",theahura,,,,12gramsofcarbon.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0354,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Long-Running Agents,https://addyo.substack.com/p/long-running-agents,external,addyo.substack.com,"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Gives readers a concrete source in Long-Running Agents when they need to carry context, state, and receipts across runs and failures.","Contextual source from addyo.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,869,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L869,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;state,builder,practitioner-analysis,ok,https://addyo.substack.com/p/long-running-agents,Long-running Agents - by Addy Osmani - Elevate,"A long-running AI agent can keep making progress over hours, days, or weeks.",Addy Osmani,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0355,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems,https://arxiv.org/abs/2607.05844,external,arxiv.org,"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Gives readers a concrete source in StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.05844; inspect its method and evaluation before treating results as production evidence.,medium,README.md,870,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L870,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05844,[2607.05844] StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems,"Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join algebra; it defines an agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.",Sergey Volkov; Yang Li; Ye Luo,2026-07-07,2026,arXiv,arXiv,,Code and supplementary materials available at: https://github.com/nZiben/statefuse,cs.AI,arxiv-api,,,,,,,2607.05844,2026-07-17T05:43:07+00:00
-ale-0356,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents,https://arxiv.org/abs/2607.08716,external,arxiv.org,"Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Gives readers a concrete source in Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08716; inspect its method and evaluation before treating results as production evidence.,medium,README.md,871,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L871,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08716,[2607.08716] Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents,"In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode ""behavioral state decay"". We study memory as an active intervention mechanism rather than passive retrieval. A separate memory agent runs alongside an unmodified action agent, updating a structured memory bank from the recent trajectory and deciding whether to inject a memory-grounded reminder or remain silent. The module is plug-and-play with frontier action agents and existing agent harnesses. Across Terminal-Bench 2.0 and $\tau^2$-Bench, it improves pass@1 for both weaker and stronger action agents, with gains of +8.3 pp on Terminal-Bench and +6.8 pp on $\tau^2$-Bench. Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval. As an early step toward open-weight memory policies, we train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.",Yifan Wu; Lizhu Zhang; Yuhang Zhou; Mingyi Wang; Bo Peng; Serena Li; Xiangjun Fan; Zhuokai Zhao,2026-07-09,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.08716,2026-07-17T05:43:07+00:00
-ale-0357,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction",https://arxiv.org/abs/2607.08032,external,arxiv.org,"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Gives readers a concrete source in What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08032; inspect its method and evaluation before treating results as production evidence.,medium,README.md,872,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L872,,Persist,persist,"Carry context, state, and receipts across runs.",objective;context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08032,"[2607.08032] What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents","Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions. Because none of this memory is free, four largely separate research communities have each learned to compact it. They evict or quantize the KV cache, prune or distill prompts, bound architectural state, and consolidate agent memory. We argue that these are instances of one problem: a rate--distortion decision about what context-derived information to retain versus discard, at what fidelity, under a resource budget, so as to preserve downstream task utility. We make this lens precise with a single compaction objective and a layer-agnostic lower bound, use it to build a seven-axis taxonomy that classifies methods from across the stack uniformly, and use it to transfer mechanisms between layers that have never been connected, from serving-stack KV management to agent long-term memory. Two patterns hold across the survey. At every layer the signal that decides what to keep is attention magnitude or recency, and it fails in the same way everywhere, by discarding, before the query is known and with no way to undo it, information the query later needs. And while compression is measured carefully on single-turn long context, the repeated compaction that agents actually perform is almost never measured, and no benchmark holds one budget axis across all the layers at once. We turn both observations into a benchmark proposal, a small reference experiment, and a set of compaction-aware design principles, and we map the open problems.",Ashwin Gerard Colaco; Nada Lahjouji,2026-07-09,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.08032,2026-07-17T05:43:07+00:00
-ale-0358,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling,https://arxiv.org/abs/2607.07666,external,arxiv.org,"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Gives readers a concrete source in A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.07666; inspect its method and evaluation before treating results as production evidence.,medium,README.md,873,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L873,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07666,[2607.07666] A hierarchical memory architecture overcomes context limits in long-horizon multi-agent computational modeling,"Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session continuity and quantitative rigor. Here we present Ensemble QSP, a multi-agent framework featuring a three-layer hierarchical memory architecture that keeps injected context bounded and constant in project duration (mid-term project state: median 301 tokens, max 4,050, across 104 runs) by capping each state category and evicting completed work, enabling continuous autonomous operation without context degradation. The system orchestrates five specialist worker agents under domain-expert principal investigators, enforcing physical constraints through physics-based checklists and structured-domain knowledge. Comprehensive benchmarking demonstrates robust autonomous pharmacokinetic-pharmacodynamic model selection without human intervention, consistent result quality across both lower-cost and frontier LLMs, improved PK parameter recovery relative to single-agent baselines, and stable model selection across linguistically diverse prompts of the same task. Feature-level ablation across physiologically based pharmacokinetic (PBPK) models spanning a broad complexity range shows that PI-agent oversight improves debugging efficiency while preserving final accuracy across conditions. The architecture is structurally domain-agnostic, adding a new scientific domain requires only a new PI agent configuration.",Shivendra G. Tewari; Holly Kimko,2026-07-08,2026,arXiv,arXiv,,"19 pages, 4 figures, 2 tables. Preprint submitted for publication",q-bio.QM,arxiv-api,,,,,,,2607.07666,2026-07-17T05:43:07+00:00
-ale-0359,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents,https://arxiv.org/abs/2607.07676,external,arxiv.org,"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Gives readers a concrete source in SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.07676; inspect its method and evaluation before treating results as production evidence.,medium,README.md,874,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L874,,Persist,persist,"Carry context, state, and receipts across runs.",state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07676,[2607.07676] SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents,"Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable. We introduce SkillCenter, to our knowledge the largest open skill library for agents by total count: 216,938 structured skills across 24 domain bundles. A SkillGate-filtered pipeline contributes 114,565 source-grounded skills from peer-reviewed journals, ArXiv, and over 24,000 technical sources, integrated with 102,373 community skills from GitHub and the ClawHub marketplace. We present the end-to-end framework that builds the pipeline subset: multi-source acquisition, an LLM-based quality gate (SkillGate), template-driven generation, iterative source-grounding, and quality-controlled publishing. Source grounding is a traceability guarantee: each retained claim maps to an exact quotation in its source. All skills ship as offline-searchable SQLite FTS5 bundles.",Tianming Sha; Yue Zhao; Lichao Sun; Yushun Dong,2026-07-08,2026,arXiv,arXiv,,"44 pages, 5 figures. Code: https://github.com/LabRAI/SkillCenter ; Data: https://huggingface.co/datasets/Tommysha/skillcenter-bundles",cs.AI,arxiv-api,,,,,,,2607.07676,2026-07-17T05:43:07+00:00
-ale-0360,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,How version control will evolve for the agent boom,https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom,external,entire.io,"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Gives readers a concrete source in How version control will evolve for the agent boom when they need to carry context, state, and receipts across runs and failures.","Contextual source from entire.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,875,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L875,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;state;exit,builder,practitioner-analysis,ok,https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom,How Version Control Will Evolve for the Agent Boom · Entire,"To meet the demand of the agent boom, Git hosting must return to its original promise: a distributed network of many hosts.",,2026-07-06,2026,,Entire,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0361,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,self-learning-skills,https://github.com/Kulaxyz/self-learning-skills,external,github.com,"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Gives readers a concrete source in self-learning-skills when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (882 stars; 35 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,876,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L876,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,source-implementation,ok,https://github.com/Kulaxyz/self-learning-skills,"GitHub - Kulaxyz/self-learning-skills: A self-improving skill for AI coding agents (Claude Code, Cursor, AGENTS.md): recognize a hard-won golden path in a session and harvest it into a reusable skill/rule for next time. · GitHub","A self-improving skill for AI coding agents (Claude Code, Cursor, AGENTS.md): recognize a hard-won golden path in a session and harvest it into a reusable skill/rule for next time. - Kulaxyz/self-learning-skills",,2026-06-28,2026,Kulaxyz/self-learning-skills,GitHub,,,,github-api,Kulaxyz/self-learning-skills,882,35,MIT,2026-06-28T19:30:36Z,2026-07-17T03:49:26Z,,2026-07-17T05:43:07+00:00
-ale-0362,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,GitLake: Git-for-data for the agentic lakehouse,https://arxiv.org/abs/2607.08319,external,arxiv.org,"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Gives readers a concrete source in GitLake: Git-for-data for the agentic lakehouse when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08319; inspect its method and evaluation before treating results as production evidence.,medium,README.md,877,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L877,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08319,[2607.08319] GitLake: Git-for-data for the agentic lakehouse,"We present GitLake, a Git-for-data design for an agent-first lakehouse. The system lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, letting agents work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and publish through a final merge, so all outputs become visible atomically or none do. Finally, we report production lessons as well as correctness insights from a preliminary Alloy model of our core abstractions.",Weiming Sheng; Jinlang Wang; Manuel Barros; Aldrin Montana; Jacopo Tagliabue; Luca Bigon,2026-07-09,2026,arXiv,arXiv,,"Pre-print of the paper accepted at DASHSys, VLDB 2026, Boston, USA",cs.DB,arxiv-api,,,,,,,2607.08319,2026-07-17T05:43:07+00:00
-ale-0363,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Shared Selective Persistent Memory for Agentic LLM Systems,https://arxiv.org/abs/2607.09493,external,arxiv.org,"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Gives readers a concrete source in Shared Selective Persistent Memory for Agentic LLM Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.09493; inspect its method and evaluation before treating results as production evidence.,medium,README.md,878,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L878,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context;state;budget;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09493,[2607.09493] Shared Selective Persistent Memory for Agentic LLM Systems,"Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is token-inefficient and counterproductive: irrelevant context degrades generation quality. We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and output constraints) while discarding session-specific reasoning traces. Crucially, this memory is shared: workspaces encapsulating selective memory can be transferred across users with role-based access control, enabling collaborative reuse without redundant specification. We implement it in a deployed collaborative workspace platform where LLM agents produce, edit, and maintain git-versioned artifacts (dashboards, reports, and data-driven documents) from heterogeneous sources (CSV, SQL, REST APIs, and MCP servers). A complementary zero-token data refresh mechanism decouples generated programs from runtime data, enabling artifact reuse without re-invocation. Across three enterprise scenarios, shared selective persistent memory achieves 96% task completion (vs. 79% without memory and 71% with full history). Zero-token refresh eliminates LLM re-invocation for recurring updates (14x task-time reduction), while summary-driven generation cuts per-invocation token cost by 97x versus raw data injection. A replication on four public datasets confirms generalizability, with zero-token refresh succeeding in 12/12 trials. Notably, naive full-history persistence actively degrades completion by biasing the agent with stale traces, while selective memory outperforms both extremes.",Sanjana Pedada; Aditya Dhavala; Neelraj Patil,2026-07-10,2026,arXiv,arXiv,,"11 pages, 2 figures, 4 tables",cs.AI,arxiv-api,,,,,,,2607.09493,2026-07-17T05:43:07+00:00
-ale-0364,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Scoped Verification for Reliable Long-Horizon Agentic Context Evolution,https://arxiv.org/abs/2607.09175,external,arxiv.org,"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","Gives readers a concrete source in Scoped Verification for Reliable Long-Horizon Agentic Context Evolution when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.09175; inspect its method and evaluation before treating results as production evidence.,medium,README.md,879,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L879,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09175,[2607.09175] Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift,"Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is updated from operational experience while the model, tools, and harness remain fixed. Over long evolution horizons, flat-text maintenance makes verification increasingly difficult as accumulated instructions grow and interact. We propose Graph-Regularized Agentic Context Evolution (GRACE), which maintains the persistent instruction component as a typed semantic graph and validates proposed updates within the local typed neighborhoods of modified nodes. Accepted graph updates are reconstructed as incremental edits to the textual instruction checkpoint used at deployment. We evaluate GRACE within a fixed telecom agent harness derived from $\tau^2$-bench under a controlled distribution-shift protocol. Across five independent replications, GRACE improves strict reliability, measured by pass^3, from the Gemini 2.5 Flash zero-shot value of 0.091 to 0.673$\pm$0.136 at the final checkpoint. This exceeds a Gemini 3.1 Pro zero-shot reference of 0.242 on the same held-out set, while the flat-text HCE baseline finishes at 0.191$\pm$0.051. These results identify two requirements for reliable long-horizon context evolution, a structural substrate that makes verification local and a consolidation mechanism that keeps accumulated instruction content usable.",Dan C. Hsu; Luke Lu,2026-07-10,2026,arXiv,arXiv,,"18 pages, 3 figs",cs.AI,arxiv-api,,,,,,,2607.09175,2026-07-17T05:43:07+00:00
-ale-0365,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,AgentMemory,https://github.com/rohitg00/agentmemory,external,github.com,"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Gives readers a concrete source in AgentMemory when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (25,247 stars; 2,088 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,880,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L880,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context;state,builder,source-implementation,ok,https://github.com/rohitg00/agentmemory,GitHub - rohitg00/agentmemory: #1 Persistent memory for AI coding agents based on real-world benchmarks · GitHub,#1 Persistent memory for AI coding agents based on real-world benchmarks - rohitg00/agentmemory,,2026-02-25,2026,rohitg00/agentmemory,GitHub,,,,github-api,rohitg00/agentmemory,25247,2088,Apache-2.0,2026-02-25T07:32:52Z,2026-07-17T05:45:20Z,,2026-07-17T05:43:07+00:00
-ale-0366,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,TencentDB-Agent-Memory,https://github.com/TencentCloud/TencentDB-Agent-Memory,external,github.com,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,"Gives readers a concrete source in TencentDB-Agent-Memory when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (9,020 stars; 830 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,881,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L881,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/TencentCloud/TencentDB-Agent-Memory,"GitHub - TencentCloud/TencentDB-Agent-Memory: TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies. · GitHub","TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies. - TencentCloud/TencentDB-Agent-Memory",,2026-04-07,2026,TencentCloud/TencentDB-Agent-Memory,GitHub,,,,github-api,TencentCloud/TencentDB-Agent-Memory,9020,830,NOASSERTION,2026-04-07T06:34:26Z,2026-07-17T05:32:42Z,,2026-07-17T05:43:07+00:00
-ale-0367,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,agent-memory (Neo4j Labs),https://github.com/neo4j-labs/agent-memory,external,github.com,"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Gives readers a concrete source in agent-memory (Neo4j Labs) when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (373 stars; 85 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,882,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L882,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/neo4j-labs/agent-memory,"GitHub - neo4j-labs/agent-memory: A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j. · GitHub","A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j. - neo4j-labs/agent-memory",,2026-01-06,2026,neo4j-labs/agent-memory,GitHub,,,,github-api,neo4j-labs/agent-memory,373,85,Apache-2.0,2026-01-06T23:07:16Z,2026-07-16T18:06:32Z,,2026-07-17T05:43:07+00:00
-ale-0368,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,re_gent,https://github.com/regent-vcs/re_gent,external,github.com,"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Gives readers a concrete source in re_gent when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (780 stars; 57 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,883,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L883,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/regent-vcs/re_gent,GitHub - regent-vcs/re_gent: Version-Control for AI coding agents. · GitHub,Version-Control for AI coding agents. Contribute to regent-vcs/re_gent development by creating an account on GitHub.,,2026-04-30,2026,regent-vcs/re_gent,GitHub,,,,github-api,regent-vcs/re_gent,780,57,Apache-2.0,2026-04-30T09:09:41Z,2026-07-15T15:28:45Z,,2026-07-17T05:43:07+00:00
-ale-0369,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure,https://arxiv.org/abs/2607.11388,external,arxiv.org,"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","Gives readers a concrete source in StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.11388; inspect its method and evaluation before treating results as production evidence.,medium,README.md,884,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L884,2026-07-15,Persist,persist,"Carry context, state, and receipts across runs.",verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11388,[2607.11388] StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure,"Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts containing accumulated observations, intermediate edits, failed attempts, and partially completed executions. Existing agents typically operate over raw interaction history, making task progress difficult to interpret, verify, and recover, which ultimately limits reliable long-horizon execution. In this paper, we argue that addressing this challenge requires explicitly structuring both the agent's state and workflow around a unified causal representation of task progress. We present \textbf{StructAgent}, a state-centered framework that introduces a unified state for maintaining compact, verifiable task progress and a structured workflow that regulates progress through verifier-backed state transitions. Building on this design, StructAgent further enables explicit progress checkpointing, evidence-driven task completion, targeted failure recovery, and tool-supported execution, while ensuring that all progress updates remain grounded in verification. Extensive experiments demonstrate that StructAgent consistently improves a wide range of LLM and VLM backbones on long-horizon computer-use tasks. On OSWorld-Verified, it improves Qwen3.5-9B from 27.0\% to 46.9\% success rate and Qwen3.5-27B from 31.6\% to 62.2\%, while achieving a new open-source state of the art of 78.9\% with MiniMax-M3. Moreover, the same framework generalizes beyond desktop environments to Minecraft, demonstrating the generality of our design.",Wenyi Wu; Sibo Zhu; Kun Zhou; Aayush Salvi; Zixuan Song; Biwei Huang,2026-07-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.11388,2026-07-17T05:43:07+00:00
-ale-0370,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,https://arxiv.org/abs/2607.10608,external,arxiv.org,"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Gives readers a concrete source in The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.10608; inspect its method and evaluation before treating results as production evidence.,medium,README.md,885,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L885,2026-07-15,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10608,[2607.10608] The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,"Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments. Existing work mostly treats memory as a supply problem, asking what experience to write, how to store it, and which entry to retrieve for the next task. Yet we still lack a clear account of how models consume retrieved memory across a multi-step action trajectory. This consumption process matters because it determines not only what memories should be retrieved, but also what models and control policies are needed to use them safely. To diagnose this process, we propose Entry--Propagation--Recovery (E-P-R), a trajectory-level framework that asks where memory first changes an action, whether that change carries forward, and whether the agent can recover after leaving a correct path. We instantiate E-P-R on WebArena and on MemTrapBench, a controlled benchmark we build to isolate these phases. We find that the main failure often begins at entry: agents adopt conflicting memory at the first exposed decision point even when it is task-wrong. Repeated exposure then amplifies this early error, while recovery after divergence is weak. Together, these effects create a compliance trap: across models, conflicting memory induces similar compliance rates, but once agents comply, their success rates collapse to a low floor. Stronger agents therefore suffer larger absolute damage because each compliance event erases more baseline capability. These results suggest that memory-augmented agents should be evaluated not only by retrieval quality or final success rate, but by how they consume memory throughout the trajectory.",Yixiong Chen; Xinyi Bai; Alan Yuille,2026-07-12,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.10608,2026-07-17T05:43:07+00:00
-ale-0371,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,"Conversational Context: Session, State, and Memory",https://adk.dev/sessions/,external,adk.dev,"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Gives readers a concrete source in Conversational Context: Session, State, and Memory when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,886,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L886,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,official-documentation,ok,https://adk.dev/sessions/,"Conversational Context: Session, State, and Memory - Agent Development Kit (ADK) Agent Development Kit (ADK)",Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0372,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Persistence,https://docs.langchain.com/oss/python/langgraph/persistence,external,docs.langchain.com,"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Gives readers a concrete source in Persistence when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from docs.langchain.com; use it for current product or standard behavior.,high,README.md,887,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L887,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state;escalation,builder,official-documentation,ok,https://docs.langchain.com/oss/python/langgraph/persistence,Persistence - Docs by LangChain,LangGraph's persistence layer gives agents short-term memory through checkpointers and long-term memory through stores.,LangChain,,,LangGraph,LangChain,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0373,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Workflow checkpoints,https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints,external,learn.microsoft.com,"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Gives readers a concrete source in Workflow checkpoints when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from learn.microsoft.com; use it for current product or standard behavior.,high,README.md,888,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L888,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,official-documentation,ok,https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints,Microsoft Agent Framework Workflows - Checkpoints | Microsoft Learn,In-depth look at Checkpoints in Microsoft Agent Framework Workflows.,Microsoft,,,Microsoft Agent Framework,Microsoft,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0374,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Agent state,https://strandsagents.com/docs/user-guide/concepts/agents/state/,external,strandsagents.com,"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Gives readers a concrete source in Agent state when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from strandsagents.com; use it for current product or standard behavior.,high,README.md,889,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L889,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,official-documentation,ok,https://strandsagents.com/docs/user-guide/concepts/agents/state/,State Management | Strands Agents,The open source toolkit for building production agents.,Strands Agents,,,Strands Agents,Strands Agents,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0375,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,https://arxiv.org/abs/2607.13591,external,arxiv.org,"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","The work turns loop quality into a measurable task or score. Resource-specific angle: Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Gives readers a concrete source in Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.13591; inspect its method and evaluation before treating results as production evidence.,medium,README.md,890,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L890,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13591,[2607.13591] Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,"Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics. We argue that this static view of memory is a core bottleneck for agentic learning because optimal memory behavior is fundamentally context-dependent. The early stages of the tasks, benefit from minimal retrieval because memory is sparse; recurring goal types benefit from plan reuse rather than generic nearest-neighbor lookup; stuck agents benefit from re-retrieval with alternative queries; and across long task streams, the memory store itself must be consolidated and pruned to remain useful. We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process and learns an online policy that adaptively decides when, what, and how much to retrieve, when to inject a distilled plan, and when to consolidate or forget. MemCon is backend-agnostic: it wraps any existing memory implementation, learns from task-by-task binary feedback with no pretraining and no additional LLM calls, and uses a lightweight tabular contextual bandit with UCB exploration that converges within tens of tasks. Across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones, MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5--20%.",Eric Hanchen Jiang; Zhi Zhang; Yuchen Wu; Levina Li; Dong Liu; Xiao Liang; Rui Sun; Yubei Li; Edward Sun; Haozheng Luo; Zhaolu Kang; Aylin Caliskan; Kai-Wei Chang; Ying Nian Wu,2026-07-15,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.13591,2026-07-17T05:43:07+00:00
-ale-0376,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Why Git Is the Memory Solution for the Agentic Development Lifecycle,https://arxiv.org/abs/2607.14390,external,arxiv.org,"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Gives readers a concrete source in Why Git Is the Memory Solution for the Agentic Development Lifecycle when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.14390; inspect its method and evaluation before treating results as production evidence.,medium,README.md,891,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L891,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14390,[2607.14390] Why Git Is the Memory Solution for the Agentic Development Lifecycle,"Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session. Memory for this setting, the agentic development lifecycle (ADLC), is usually posed as one retrieval problem and built as machinery: tiered stores, memory graphs, compiled wikis, model-judged admission. We argue memory should instead be git-bound -- built into the repository's version control, inheriting the guarantees the machinery struggles to construct: ground truth from commits, freshness from rebuild, verification from the merge, containment from review. On this ledger we solve two problems separately, then combine them. Seed supply is closed as an eight-corpus retrieval study under a pre-registered ship discipline: five imported ranking mechanisms rejected, two kept, and a best configuration of ~0.31 pooled MRR -- ~60x the raw-transcript grep floor, ~15x an honest parsed-turn floor. Answer assembly is where ranking stops helping: single-shot retrieval scores only 0.07-0.20 answer-sufficiency on real developer questions, and ungated episode injection measurably degrades good answers. A router dispatches breadth to a git-anchored structural map, pointed lookups to confidence-gated episodes, and rationale to decision synthesis, which reconstructs why-arcs no single session contains (0.83 sufficiency on a young ~50k-LOC production system). Routed, the system answers at 382-980 tokens per question -- three orders of magnitude below the recorded history. Because ground truth is mined from commit-session links rather than annotated, every result is replicable on any user's own history at zero labeling cost. The remaining constraint is capture. Code, benchmark, and paper source: github.com/rekal-dev/rekal-cli.",Frank Guo,2026-07-15,2026,arXiv,arXiv,,8 pages,cs.SE,arxiv-api,,,,,,,2607.14390,2026-07-17T05:43:07+00:00
-ale-0377,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AutoGen,https://github.com/microsoft/autogen,external,github.com,"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.",Gives readers a concrete source in AutoGen when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (59,783 stars; 8,998 forks; CC-BY-4.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,899,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L899,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,source-implementation,ok,https://github.com/microsoft/autogen,GitHub - microsoft/autogen: A programming framework for agentic AI · GitHub,A programming framework for agentic AI. Contribute to microsoft/autogen development by creating an account on GitHub.,,2023-08-18,2023,microsoft/autogen,GitHub,,,,github-api,microsoft/autogen,59783,8998,CC-BY-4.0,2023-08-18T11:43:45Z,2026-07-17T04:18:46Z,,2026-07-17T05:43:07+00:00
-ale-0378,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Microsoft Agent Framework,https://github.com/microsoft/agent-framework,external,github.com,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.",Gives readers a concrete source in Microsoft Agent Framework when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (12,171 stars; 2,045 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,900,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L900,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/microsoft/agent-framework,"GitHub - microsoft/agent-framework: A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. · GitHub","A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. - microsoft/agent-framework",,2025-04-28,2025,microsoft/agent-framework,GitHub,,,,github-api,microsoft/agent-framework,12171,2045,MIT,2025-04-28T19:40:42Z,2026-07-17T03:58:35Z,,2026-07-17T05:43:07+00:00
-ale-0379,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,LangGraph,https://github.com/langchain-ai/langgraph,external,github.com,"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.",Gives readers a concrete source in LangGraph when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (37,472 stars; 6,279 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,901,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L901,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;escalation,builder,source-implementation,ok,https://github.com/langchain-ai/langgraph,GitHub - langchain-ai/langgraph: Build resilient agents. · GitHub,Build resilient agents. Contribute to langchain-ai/langgraph development by creating an account on GitHub.,,2023-08-09,2023,langchain-ai/langgraph,GitHub,,,,github-api,langchain-ai/langgraph,37472,6279,MIT,2023-08-09T18:33:12Z,2026-07-17T05:21:44Z,,2026-07-17T05:43:07+00:00
-ale-0380,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,CrewAI,https://github.com/crewAIInc/crewAI,external,github.com,"Framework for multi-agent workflows organized around roles, tasks, and crews.","Framework for multi-agent workflows organized around roles, tasks, and crews.","Framework for multi-agent workflows organized around roles, tasks, and crews.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Framework for multi-agent workflows organized around roles, tasks, and crews.",Gives readers a concrete source in CrewAI when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (55,668 stars; 7,854 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,902,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L902,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/crewAIInc/crewAI,"GitHub - crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. · GitHub","Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. - crewAIInc/crewAI",,2023-10-27,2023,crewAIInc/crewAI,GitHub,,,,github-api,crewAIInc/crewAI,55668,7854,MIT,2023-10-27T03:26:59Z,2026-07-17T05:04:58Z,,2026-07-17T05:43:07+00:00
-ale-0381,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,LlamaIndex Workflows,https://developers.llamaindex.ai/python/llamaagents/workflows/,external,developers.llamaindex.ai,Event-driven workflow abstraction for agentic applications.,Event-driven workflow abstraction for agentic applications.,Event-driven workflow abstraction for agentic applications.,"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Event-driven workflow abstraction for agentic applications.",Gives readers a concrete source in LlamaIndex Workflows when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,903,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L903,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger,builder,technical-documentation,ok,https://developers.llamaindex.ai/python/llamaagents/workflows/,Introduction | Developer Documentation,,,,,,Developer Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0382,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,OpenAI Agents SDK handoffs,https://openai.github.io/openai-agents-python/handoffs/,external,openai.github.io,First-class delegation between specialized agents.,First-class delegation between specialized agents.,First-class delegation between specialized agents.,"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: First-class delegation between specialized agents.",Gives readers a concrete source in OpenAI Agents SDK handoffs when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from openai.github.io; use it for current product or standard behavior.,high,README.md,904,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L904,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,official-documentation,ok,https://openai.github.io/openai-agents-python/handoffs/,Handoffs - OpenAI Agents SDK,,,,,,openai.github.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0383,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Agent Protocol,https://agentprotocol.ai/,external,agentprotocol.ai,"API protocol for agent interaction, useful for separating loop managers from agent runtimes.","API protocol for agent interaction, useful for separating loop managers from agent runtimes.","API protocol for agent interaction, useful for separating loop managers from agent runtimes.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: API protocol for agent interaction, useful for separating loop managers from agent runtimes.",Gives readers a concrete source in Agent Protocol when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,905,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L905,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,technical-documentation,ok,https://agentprotocol.ai/,AgentProtocol.ai — A practical guide to AI agent communication standards.,"AgentProtocol.ai is an independent, vendor-neutral guide to AI agent communication standards — MCP, A2A, Agent Protocol, AI agent APIs and agent interoperability.",AgentProtocol.ai,,,,AgentProtocol.ai,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0384,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AgentKit,https://github.com/inngest/agent-kit,external,github.com,"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: TypeScript toolkit for durable, event-driven agents on workflow infrastructure.",Gives readers a concrete source in AgentKit when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (913 stars; 136 forks; Apache-2.0 license; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,906,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L906,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger,builder,source-implementation,ok,https://github.com/inngest/agent-kit,GitHub - inngest/agent-kit: AgentKit: Build multi-agent networks in TypeScript with deterministic routing and rich tooling via MCP. · GitHub,AgentKit: Build multi-agent networks in TypeScript with deterministic routing and rich tooling via MCP. - inngest/agent-kit,,2024-11-18,2024,inngest/agent-kit,GitHub,,,,github-api,inngest/agent-kit,913,136,Apache-2.0,2024-11-18T05:28:42Z,2026-07-13T12:05:29Z,,2026-07-17T05:43:07+00:00
-ale-0385,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,deepagents,https://github.com/langchain-ai/deepagents,external,github.com,"LangChain project for deeper, longer-running agents with middleware and harness patterns.","LangChain project for deeper, longer-running agents with middleware and harness patterns.","LangChain project for deeper, longer-running agents with middleware and harness patterns.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: LangChain project for deeper, longer-running agents with middleware and harness patterns.",Gives readers a concrete source in deepagents when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (26,333 stars; 3,694 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,907,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L907,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,source-implementation,ok,https://github.com/langchain-ai/deepagents,GitHub - langchain-ai/deepagents: The batteries-included agent harness. · GitHub,The batteries-included agent harness. Contribute to langchain-ai/deepagents development by creating an account on GitHub.,,2025-07-27,2025,langchain-ai/deepagents,GitHub,,,,github-api,langchain-ai/deepagents,26333,3694,MIT,2025-07-27T23:07:53Z,2026-07-17T05:38:17Z,,2026-07-17T05:43:07+00:00
-ale-0386,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Temporal for AI,https://temporal.io/solutions/ai,external,temporal.io,"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.",Gives readers a concrete source in Temporal for AI when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,908,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L908,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;budget;escalation,builder,technical-documentation,ok,https://temporal.io/solutions/ai,Temporal for AI | Temporal,"Temporal is a durable workflow platform that ensures AI applications run reliably, every time. Build faster, prevent failures, and stand out from the crowd.",,,,,temporal.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0387,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Restate,https://restate.dev/,external,restate.dev,"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.",Gives readers a concrete source in Restate when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,909,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L909,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state,builder,implementation,ok,https://www.restate.dev/,Restate - Build innately resilient distributed apps,Restate is a lightweight runtime that lets developers build innately resilient distributed apps without the complexity tax.,,,,,Restate,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0388,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,DBOS,https://www.dbos.dev/,external,www.dbos.dev,"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.",Gives readers a concrete source in DBOS when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,910,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L910,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;intake,builder,implementation,ok,https://www.dbos.dev/,DBOS | Durable Workflow Orchestration,"DBOS is an open source durable execution and workflow orchestration system that radically simplifies the development and operation of reliable, observable workflows.",,,,,dbos.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0389,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Composio Agent Orchestrator,https://github.com/ComposioHQ/agent-orchestrator,external,github.com,"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.",Gives readers a concrete source in Composio Agent Orchestrator when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (8,314 stars; 1,195 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,911,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L911,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,source-implementation,ok,https://github.com/AgentWrapper/agent-orchestrator,"GitHub - AgentWrapper/agent-orchestrator: AO is an agent IDE, that helps developers manage fleets of coding agents to do your day to day tasks for parallel coding agents. It comes with an agentic orchestrator that plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews. · GitHub","AO is an agent IDE, that helps developers manage fleets of coding agents to do your day to day tasks for parallel coding agents. It comes with an agentic orchestrator that plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews. - AgentWrapper/agent-orchestrator",,2026-02-13,2026,ComposioHQ/agent-orchestrator,GitHub,,,,github-api,ComposioHQ/agent-orchestrator,8314,1195,Apache-2.0,2026-02-13T09:52:36Z,2026-07-17T04:40:02Z,,2026-07-17T05:43:07+00:00
-ale-0390,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Omnigent,https://github.com/omnigent-ai/omnigent,external,github.com,"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.",Gives readers a concrete source in Omnigent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (7,397 stars; 1,043 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,912,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L912,,Build,build,"Choose runtimes, tools, and delegation surfaces.",budget;escalation,builder,source-implementation,ok,https://github.com/omnigent-ai/omnigent,"GitHub - omnigent-ai/omnigent: Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device. · GitHub","Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device. - omnigent-ai/omnigent",,2026-06-11,2026,omnigent-ai/omnigent,GitHub,,,,github-api,omnigent-ai/omnigent,7397,1043,Apache-2.0,2026-06-11T12:18:13Z,2026-07-17T05:38:06Z,,2026-07-17T05:43:07+00:00
-ale-0391,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution,https://arxiv.org/abs/2604.11378,external,arxiv.org,"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.",Gives readers a concrete source in From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2604.11378; inspect its method and evaluation before treating results as production evidence.,medium,README.md,913,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L913,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;escalation;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.11378,[2604.11378] From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution,"The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window. This paradigm has three structural weaknesses: implicit dependencies between steps, unbounded recovery loops, and mutable execution history that complicates debugging. We characterize the Agent Loop as a single ready unit scheduler: at any moment, at most one executable unit is active, and the choice of which unit to activate comes from opaque LLM inference rather than an inspectable policy. This perspective places Agent Loops and graph based execution engines on a single semantic continuum. We propose SGH, Structured Graph Harness, which lifts control flow from implicit context into an explicit static DAG. SGH makes three commitments: execution plans are immutable within a plan version, planning execution and recovery are separated into three layers, and recovery follows a strict escalation protocol. These choices trade some expressiveness for controllability, verifiability, and implementability. Our contributions are fourfold: a scheduler unified framework that applies classical scheduling theory to LLM agent execution and identifies challenges introduced by non deterministic LLM nodes; a trade off analysis of controllability, expressiveness, and implementability across 70 surveyed systems; a formal specification including a node state machine with termination and soundness guarantees; and an attributable experimental framework with a seven group design for future validation. This is a position paper and design proposal. We provide a theoretical framework, design analysis, and experimental protocol, not a production implementation or empirical results.",Hu Wei,2026-04-13,2026,arXiv,arXiv,,"51 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2604.11378,2026-07-17T05:43:07+00:00
-ale-0392,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Eve,https://github.com/vercel/eve,external,github.com,"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.",Gives readers a concrete source in Eve when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,770 stars; 343 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,914,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L914,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;state,builder,source-implementation,ok,https://github.com/vercel/eve,GitHub - vercel/eve: The Framework for Building Agents · GitHub,The Framework for Building Agents. Contribute to vercel/eve development by creating an account on GitHub.,,2026-06-16,2026,vercel/eve,GitHub,,,,github-api,vercel/eve,3770,343,Apache-2.0,2026-06-16T10:51:20Z,2026-07-17T05:32:55Z,,2026-07-17T05:43:07+00:00
-ale-0393,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework,https://arxiv.org/abs/2603.11445,external,arxiv.org,"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.",Gives readers a concrete source in Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.11445; inspect its method and evaluation before treating results as production evidence.,medium,README.md,915,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L915,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.11445,[2603.11445] Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework for Complex Query Resolution,"We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.",Xing Zhang; Yanwei Cui; Guanghui Wang; Wei Qiu; Ziyuan Li; Fangwei Han; Yajing Huang; Hengzhi Qiu; Bing Zhu; Peiyang He,2026-03-12,2026,arXiv,arXiv,,ICLR 2026 Workshop on MALGAI,cs.AI,arxiv-api,,,,,,,2603.11445,2026-07-17T05:43:07+00:00
-ale-0394,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents,https://arxiv.org/abs/2603.22386,external,arxiv.org,"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.",Gives readers a concrete source in From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.22386; inspect its method and evaluation before treating results as production evidence.,medium,README.md,916,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L916,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.22386,[2603.22386] From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents,"Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows, which we treat as agentic computation graphs (ACGs). We organize the literature based on when workflow structure is determined, where structure refers to which components or agents are present, how they depend on each other, and how information flows between them. This lens distinguishes static methods, which fix a reusable workflow scaffold before deployment, from dynamic methods, which select, generate, or revise the workflow for a particular run before or during execution. We further organize prior work along three dimensions: when structure is determined, what part of the workflow is optimized, and which evaluation signals guide optimization (e.g., task metrics, verifier signals, preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating reusable design choices from the structures actually deployed in a given run and from realized runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. Our goal is to provide a clear vocabulary, a unified framework for positioning new methods, a more comparable view of existing body of literature, and a more reproducible evaluation standard for future work in workflow optimizations for LLM agents.",Ling Yue; Kushal Raj Bhandari; Ching-Yun Ko; Dhaval Patel; Shuxin Lin; Nianjun Zhou; Jianxi Gao; Pin-Yu Chen; Shaowu Pan,2026-03-23,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.22386,2026-07-17T05:43:07+00:00
-ale-0395,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Agent-as-a-Router,https://github.com/LanceZPF/agent-as-a-router,external,github.com,"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.",Gives readers a concrete source in Agent-as-a-Router when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (647 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,917,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L917,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation;verification,builder,source-implementation,ok,https://github.com/LanceZPF/agent-as-a-router,GitHub - LanceZPF/agent-as-a-router: The official implementations of Agent-as-a-Router: Agentic Model Routing for Coding Tasks. · GitHub,The official implementations of Agent-as-a-Router: Agentic Model Routing for Coding Tasks. - LanceZPF/agent-as-a-router,,2026-06-20,2026,LanceZPF/agent-as-a-router,GitHub,,,,github-api,LanceZPF/agent-as-a-router,647,14,MIT,2026-06-20T16:00:51Z,2026-07-17T05:13:11Z,,2026-07-17T05:43:07+00:00
-ale-0396,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Amp: Custom Agents,https://ampcode.com/news/custom-agents,external,ampcode.com,"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.",Gives readers a concrete source in Amp: Custom Agents when they need to choose an implementation surface for repeatable agent work.,"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,918,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L918,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,practitioner-analysis,ok,https://ampcode.com/news/custom-agents,Amp,"Plugins can now create agents, run them once, and keep talking to their threads.",,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0397,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AgentsMesh,https://github.com/AgentsMesh/AgentsMesh,external,github.com,"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.",Gives readers a concrete source in AgentsMesh when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,282 stars; 228 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,919,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L919,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,source-implementation,ok,https://github.com/AgentsMesh/AgentsMesh,"GitHub - AgentsMesh/AgentsMesh: The AI Agent Workforce Platform. Run a hundred AI coding agents across your own machines — schedule, isolate, and steer them all from one console. · GitHub","The AI Agent Workforce Platform. Run a hundred AI coding agents across your own machines — schedule, isolate, and steer them all from one console. - AgentsMesh/AgentsMesh",,2026-02-28,2026,AgentsMesh/AgentsMesh,GitHub,,,,github-api,AgentsMesh/AgentsMesh,2282,228,NOASSERTION,2026-02-28T07:10:42Z,2026-07-17T01:12:07Z,,2026-07-17T05:43:07+00:00
-ale-0398,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Bernstein,https://github.com/sipyourdrink-ltd/bernstein,external,github.com,"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.",Gives readers a concrete source in Bernstein when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (682 stars; 62 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,920,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L920,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace;delegation;verification,builder,source-implementation,ok,https://github.com/sipyourdrink-ltd/bernstein,"GitHub - sipyourdrink-ltd/bernstein: Deterministic, audit-grade multi-agent orchestration for CLI coding agents (Claude Code, Codex, Gemini CLI, +40 more). HMAC-chained audit log, signed agent cards, per-artefact lineage, byte-identical replay with divergence detection, air-gap deploy. https://bernstein.run · GitHub","Deterministic, audit-grade multi-agent orchestration for CLI coding agents (Claude Code, Codex, Gemini CLI, +40 more). HMAC-chained audit log, signed agent cards, per-artefact lineage, byte-identical replay with divergence detection, air-gap deploy. https://bernstein.run - sipyourdrink-ltd/bernstein",,2026-03-22,2026,sipyourdrink-ltd/bernstein,GitHub,,,,github-api,sipyourdrink-ltd/bernstein,682,62,Apache-2.0,2026-03-22T14:52:26Z,2026-07-17T05:36:58Z,,2026-07-17T05:43:07+00:00
-ale-0399,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Aeon,https://github.com/aaronjmars/aeon,external,github.com,"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.",Gives readers a concrete source in Aeon when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (577 stars; 208 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,921,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L921,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;context;state,builder,source-implementation,ok,https://github.com/aeonfun/aeon,"GitHub - aeonfun/aeon: The most autonomous agent framework. No approval loops. No babysitting. Configure once, forget forever. · GitHub","The most autonomous agent framework. No approval loops. No babysitting. Configure once, forget forever. - aeonfun/aeon",,2026-03-04,2026,aaronjmars/aeon,GitHub,,,,github-api,aaronjmars/aeon,577,208,MIT,2026-03-04T19:44:49Z,2026-07-16T21:58:55Z,,2026-07-17T05:43:07+00:00
-ale-0400,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,h5i,https://github.com/h5i-dev/h5i,external,github.com,"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.",Gives readers a concrete source in h5i when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (470 stars; 39 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,922,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L922,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification,builder,source-implementation,ok,https://github.com/h5i-dev/h5i,"GitHub - h5i-dev/h5i: Auditable workspaces for AI coding agents: sandboxed worktrees, programmable multi-agent orchestration, automated security checks, up to 95% less token waste, and persistent memory. · GitHub","Auditable workspaces for AI coding agents: sandboxed worktrees, programmable multi-agent orchestration, automated security checks, up to 95% less token waste, and persistent memory. - h5i-dev/h5i",,2026-03-11,2026,h5i-dev/h5i,GitHub,,,,github-api,h5i-dev/h5i,470,39,Apache-2.0,2026-03-11T04:30:52Z,2026-07-16T21:21:27Z,,2026-07-17T05:43:07+00:00
-ale-0401,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,https://arxiv.org/abs/2607.02807,external,arxiv.org,"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.",Gives readers a concrete source in SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.02807; inspect its method and evaluation before treating results as production evidence.,medium,README.md,923,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L923,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.02807,[2607.02807] SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,"Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, then proceed with low-level edits while missing other superior approaches to the problem. We hypothesize two harness-level design choices contribute to this behavior: accumulating context in a single long-running agent and only exposing a single program state to edit. We introduce SwarmResearch, an orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents, each operating with local context in their respective git branch. On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques on 13/15 tasks, driven by higher-level exploration. Compared with fixed scaling of serial and parallel agents, SwarmResearch's orchestrator-guided scaling discovers better-performing solutions by adapting parallelism at different search depths.",Yuvraj Virk; Zack Edds; Chunqiu Steven Xia; Lingming Zhang,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02807,2026-07-17T05:43:07+00:00
-ale-0402,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Scaling Long-Running Autonomous Coding,https://cursor.com/blog/scaling-agents,external,cursor.com,"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.",Gives readers a concrete source in Scaling Long-Running Autonomous Coding when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,924,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L924,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,practitioner-analysis,ok,https://cursor.com/blog/scaling-agents,Scaling long-running autonomous coding · Cursor,We've been experimenting with running coding agents autonomously for weeks at a time.,Wilson Lin,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0403,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,babysitter,https://github.com/a5c-ai/babysitter,external,github.com,"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.",Gives readers a concrete source in babysitter when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,554 stars; 90 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,925,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L925,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;state;escalation;exit,builder,source-implementation,ok,https://github.com/a5c-ai/babysitter,"GitHub - a5c-ai/babysitter: Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration · GitHub","Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration - a5c-ai/babysitter",,2026-01-05,2026,a5c-ai/babysitter,GitHub,,,,github-api,a5c-ai/babysitter,1554,90,MIT,2026-01-05T15:26:53Z,2026-07-17T03:58:54Z,,2026-07-17T05:43:07+00:00
-ale-0404,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,claude-code-merge-queue,https://github.com/funador/claude-code-merge-queue,external,github.com,"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.",Gives readers a concrete source in claude-code-merge-queue when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (12 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,926,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L926,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/funador/claude-code-merge-queue,GitHub - funador/claude-code-merge-queue: The local merge queue for parallel Claude Code agents · GitHub,The local merge queue for parallel Claude Code agents - funador/claude-code-merge-queue,,2026-07-10,2026,funador/claude-code-merge-queue,GitHub,,,,github-api,funador/claude-code-merge-queue,12,1,MIT,2026-07-10T22:05:05Z,2026-07-17T04:52:41Z,,2026-07-17T05:43:07+00:00
-ale-0405,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Devin can now manage Devins,https://cognition.com/blog/devin-can-now-manage-devins,external,cognition.com,"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.",Gives readers a concrete source in Devin can now manage Devins when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,927,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L927,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,practitioner-analysis,ok,https://cognition.com/blog/devin-can-now-manage-devins,Devin can now Manage Devins | Cognition,"Devin can now break down large tasks and delegate them to a team of managed Devins, with each running in its own isolated VM in parallel.",,2026-03-19,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0406,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,pilotfish,https://github.com/Nanako0129/pilotfish,external,github.com,"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.",Gives readers a concrete source in pilotfish when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (474 stars; 37 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,928,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L928,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation,builder,source-implementation,ok,https://github.com/Nanako0129/pilotfish,"GitHub - Nanako0129/pilotfish: Multi-model orchestration layer for Claude Code — the frontier model plans, cheaper models execute, verification guards quality. One-prompt install. · GitHub","Multi-model orchestration layer for Claude Code — the frontier model plans, cheaper models execute, verification guards quality. One-prompt install. - Nanako0129/pilotfish",,2026-07-08,2026,Nanako0129/pilotfish,GitHub,,,,github-api,Nanako0129/pilotfish,474,37,MIT,2026-07-08T10:53:34Z,2026-07-17T04:50:38Z,,2026-07-17T05:43:07+00:00
-ale-0407,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,fable-advisor,https://github.com/DannyMac180/fable-advisor,external,github.com,"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.",Gives readers a concrete source in fable-advisor when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (527 stars; 45 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,929,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L929,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/DannyMac180/fable-advisor,"GitHub - DannyMac180/fable-advisor: Claude Fable as an orchestrator for Opus, GPT and Grok · GitHub","Claude Fable as an orchestrator for Opus, GPT and Grok - DannyMac180/fable-advisor",,2026-07-03,2026,DannyMac180/fable-advisor,GitHub,,,,github-api,DannyMac180/fable-advisor,527,45,MIT,2026-07-03T01:53:51Z,2026-07-16T18:52:50Z,,2026-07-17T05:43:07+00:00
-ale-0408,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,agent-chief,https://github.com/SmileLikeYe/agent-chief,external,github.com,"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.",Gives readers a concrete source in agent-chief when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (780 stars; 3 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,930,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L930,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,source-implementation,ok,https://github.com/SmileLikeYe/agent-chief,"GitHub - SmileLikeYe/agent-chief: Attention is your scarcest resource. Chief is the local-first layer that guards it — turning every agent, alert, and feed into one honest call: interrupt, or not. · GitHub","Attention is your scarcest resource. Chief is the local-first layer that guards it — turning every agent, alert, and feed into one honest call: interrupt, or not. - SmileLikeYe/agent-chief",,2026-07-04,2026,SmileLikeYe/agent-chief,GitHub,,,,github-api,SmileLikeYe/agent-chief,780,3,MIT,2026-07-04T15:28:56Z,2026-07-16T02:48:32Z,,2026-07-17T05:43:07+00:00
-ale-0409,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,OpenTag,https://github.com/amplifthq/opentag,external,github.com,"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.",Gives readers a concrete source in OpenTag when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,363 stars; 73 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,931,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L931,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/amplifthq/opentag,"GitHub - amplifthq/opentag: Open-source @agent mentions for Slack and GitHub. OpenTag routes tagged requests to Codex, Claude Code, then returns results in thread. · GitHub","Open-source @agent mentions for Slack and GitHub. OpenTag routes tagged requests to Codex, Claude Code, then returns results in thread. - amplifthq/opentag",,2026-06-24,2026,amplifthq/opentag,GitHub,,,,github-api,amplifthq/opentag,1363,73,MIT,2026-06-24T08:05:12Z,2026-07-17T01:52:47Z,,2026-07-17T05:43:07+00:00
-ale-0410,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,herdr,https://github.com/ogulcancelik/herdr,external,github.com,"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.",Gives readers a concrete source in herdr when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (17,294 stars; 1,082 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,932,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L932,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state,builder,source-implementation,ok,https://github.com/ogulcancelik/herdr,GitHub - ogulcancelik/herdr: agent multiplexer that lives in your terminal. · GitHub,agent multiplexer that lives in your terminal. Contribute to ogulcancelik/herdr development by creating an account on GitHub.,,2026-03-27,2026,ogulcancelik/herdr,GitHub,,,,github-api,ogulcancelik/herdr,17294,1082,NOASSERTION,2026-03-27T17:54:33Z,2026-07-17T05:42:55Z,,2026-07-17T05:43:07+00:00
-ale-0411,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Orca,https://github.com/stablyai/orca,external,github.com,"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.",Gives readers a concrete source in Orca when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (20,709 stars; 1,498 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,933,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L933,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,source-implementation,ok,https://github.com/stablyai/orca,GitHub - stablyai/orca: Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile. · GitHub,Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile. - stablyai/orca,,2026-03-17,2026,stablyai/orca,GitHub,,,,github-api,stablyai/orca,20709,1498,MIT,2026-03-17T03:28:57Z,2026-07-17T05:43:56Z,,2026-07-17T05:43:07+00:00
-ale-0412,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,Agentic Routing: The Harness-Native Data Flywheel,https://arxiv.org/abs/2607.11399,external,arxiv.org,"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.",Gives readers a concrete source in Agentic Routing: The Harness-Native Data Flywheel when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.11399; inspect its method and evaluation before treating results as production evidence.,medium,README.md,934,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L934,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11399,[2607.11399] Agentic Routing: The Harness-Native Data Flywheel,"Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification. At the same time, frontier and open models are becoming structurally specialized: a model that is strong at code editing, long-context recovery, tool use, mathematical reasoning, or low-latency response may not dominate on the other axes. This makes model selection inside an agent a core systems problem rather than a per-query serving trick. Existing routing methods mostly optimize single-turn cost-quality trade-offs and therefore miss the execution state, intermediate failures, and feedback loops that make agents different from chat completion. We propose Harness-Native agentic routing, a step-level routing paradigm that selects either a single best-fit model for cost-effective execution or multiple complementary models for ensemble-style accuracy improvement, conditioned on the full harness state. The key insight is that every routing decision naturally produces a structured data record -- consisting of the query, harness state, model choice or model set, execution trace, outcome, and cost -- whose labels are supplied by the environment rather than by the router itself. These records form a harness-native data flywheel: execution traces train better routers and harness-native models, which improve cost-quality trade-offs and generate more traces under the same budget. We instantiate this idea in OpenSquilla with a four-layer routing stack, an open LightGBM cold-start ranker, and a staged router-model path that turns logged arena records into progressively stronger routing policies. The report studies singleton and multi-model routing on agentic benchmarks including DRACO and PinchBench, and argues that agentic routing is not merely cost control, but a data engine for agent-native training.",Xinchen Liu; Hang Zhou; Yingjie Zong; Yuchuan Tian; Liuyang Song; Shuo Zhang; Yulong Li; Wei He; Mengyu Zheng; Runke Liu; Siyang Cheng; Xiang Kuang; Hailin Hu; Kai Han; Yunhe Wang,2026-07-13,2026,arXiv,arXiv,,Code: https://github.com/opensquilla/opensquilla,cs.CL,arxiv-api,,,,,,,2607.11399,2026-07-17T05:43:07+00:00
-ale-0413,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution,https://arxiv.org/abs/2607.11138,external,arxiv.org,"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.",Gives readers a concrete source in A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.11138; inspect its method and evaluation before treating results as production evidence.,medium,README.md,935,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L935,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11138,[2607.11138] A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery,"The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously. This leads to decision-space explosion, context window saturation, and degraded routing accuracy. To address these limitations, this paper presents a hierarchical, skill-based architecture for agentic orchestration. Capabilities are organized as a rooted tree where internal nodes make routing decisions and leaf nodes execute deterministic tasks. The runtime enforces a single-step execution loop governed by a Last-In-First-Out (LIFO) stack, giving the agent a form of memory akin to a Pushdown Automaton, therefore enabling it to track nested execution contexts and resume deterministically from any depth. Capability discovery follows a manifest-driven, lazy-loading protocol: only the immediate children of the active node are loaded, so memory and prompt costs scale with the explored path rather than the global registry. By replacing global memory with localized stack frames, the architecture prevents outputs from one execution branch from leaking into another, establishing the isolation guarantees required for deployment in regulated enterprise environments. We also discuss UPI Help, an AI-powered digital payments support product, as a motivating production deployment context. We provide a mathematical formalization of the orchestration state, detailed algorithmic analysis of the execution loop, and controlled benchmarks comparing flat and hierarchical routing under increasing tool catalogs, multi-step workflow pressure, and visible schema-token exposure per LLM call.",Prashant Devadiga; Abhishek; Adithya Mishra; Alok Singh; Amisha Sinha; Asit Desai; Gaurang Dahad; Harshit Bhushan; Mandati Pramod Reddy; Prakhar Gupta; Rupesh Patil; Siddhi Behere,2026-07-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.11138,2026-07-17T05:43:07+00:00
-ale-0414,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Graph-based agent workflows,https://adk.dev/graphs/,external,adk.dev,"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.",Gives readers a concrete source in Graph-based agent workflows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,936,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L936,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;state;escalation,builder,official-documentation,ok,https://adk.dev/graphs/,Graph-based agent workflows - Agent Development Kit (ADK) Agent Development Kit (ADK),Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0415,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Flows,https://docs.crewai.com/en/concepts/flows,external,docs.crewai.com,"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.",Gives readers a concrete source in Flows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.crewai.com; use it for current product or standard behavior.,high,README.md,937,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L937,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;state,builder,official-documentation,ok,https://docs.crewai.com/v1.15.3/en/concepts/flows,Flows - CrewAI,Learn how to create and manage AI workflows using CrewAI Flows.,CrewAI,,,CrewAI,CrewAI,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0416,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Graph,https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/,external,strandsagents.com,"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.",Gives readers a concrete source in Graph when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from strandsagents.com; use it for current product or standard behavior.,high,README.md,938,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L938,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,official-documentation,ok,https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/,Graph Multi-Agent Pattern | Strands Agents,The open source toolkit for building production agents.,Strands Agents,,,Strands Agents,Strands Agents,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0417,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-bench,https://www.swebench.com/,external,www.swebench.com,Benchmark for resolving real GitHub issues through code editing and tests.,Benchmark for resolving real GitHub issues through code editing and tests.,Benchmark for resolving real GitHub issues through code editing and tests.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for resolving real GitHub issues through code editing and tests.,Gives readers a concrete source in SWE-bench when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,946,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L946,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://www.swebench.com/,SWE-bench Leaderboards,,,,,,swebench.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0418,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,https://arxiv.org/abs/2310.06770,external,arxiv.org,Original SWE-bench paper.,Original SWE-bench paper.,Original SWE-bench paper.,Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Original SWE-bench paper.,Gives readers a concrete source in SWE-bench: Can Language Models Resolve Real-World GitHub Issues? when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2310.06770; inspect its method and evaluation before treating results as production evidence.,medium,README.md,947,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L947,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2310.06770,[2310.06770] SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,"Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we introduce SWE-bench, an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. The best-performing model, Claude 2, is able to solve a mere $1.96$% of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.",Carlos E. Jimenez; John Yang; Alexander Wettig; Shunyu Yao; Kexin Pei; Ofir Press; Karthik Narasimhan,2023-10-10,2023,arXiv,arXiv,,"Data, code, and leaderboard are available at https://www.swebench.com ICLR 2024, https://openreview.net/forum?id=VTF8yNQM66",cs.CL,arxiv-api,,,,,,,2310.06770,2026-07-17T05:43:07+00:00
-ale-0419,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,SWE-bench Goes Live,https://arxiv.org/abs/2505.23419,external,arxiv.org,Dynamic benchmark designed to reduce overfitting to static issue sets.,Dynamic benchmark designed to reduce overfitting to static issue sets.,Dynamic benchmark designed to reduce overfitting to static issue sets.,The work turns loop quality into a measurable task or score. Resource-specific angle: Dynamic benchmark designed to reduce overfitting to static issue sets.,Gives readers a concrete source in SWE-bench Goes Live when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2505.23419; inspect its method and evaluation before treating results as production evidence.,medium,README.md,948,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L948,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2505.23419,[2505.23419] SWE-bench Goes Live!,"The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability and introduce risks of overfitting and data contamination. In this work, we present SWE-bench-Live, a live-updatable benchmark designed to overcome these challenges. Our initial release consists of 1,319 tasks derived from real GitHub issues created since 2024, spanning 93 repositories. Each task is accompanied by a dedicated Docker image to ensure reproducible execution. Central to our benchmark is \method, an automated curation pipeline that streamlines the entire process from instance creation to environment setup, removing manual bottlenecks and enabling scalability and continuous updates. We evaluate a range of state-of-the-art agent frameworks and LLMs on SWE-bench-Live, revealing a substantial performance gap compared to static benchmarks like SWE-bench, even under controlled evaluation conditions. To better understand this discrepancy, we perform detailed analyses across repository origin, issue recency, and task difficulty. By providing a fresh, diverse, and executable benchmark grounded in live repository activity, SWE-bench-Live facilitates rigorous, contamination-resistant evaluation of LLMs and agents in dynamic, real-world software development settings.",Linghao Zhang; Shilin He; Chaoyun Zhang; Yu Kang; Bowen Li; Chengxing Xie; Junhao Wang; Maoquan Wang; Yufan Huang; Shengyu Fu; Elsie Nallipogu; Qingwei Lin; Yingnong Dang; Saravan Rajmohan; Dongmei Zhang,2025-05-29,2025,arXiv,arXiv,,"Homepage: \url{https://swe-bench-live.github.io/}, Code: \url{https://github.com/SWE-bench-Live}, Dataset: \url{https://huggingface.co/SWE-bench-Live}",cs.SE,arxiv-api,,,,,,,2505.23419,2026-07-17T05:43:07+00:00
-ale-0420,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Terminal-Bench,https://www.tbench.ai/,external,www.tbench.ai,Benchmark for agents operating in terminal environments.,Benchmark for agents operating in terminal environments.,Benchmark for agents operating in terminal environments.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for agents operating in terminal environments.,Gives readers a concrete source in Terminal-Bench when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,949,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L949,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://www.tbench.ai/,Terminal-Bench,A benchmark for terminal agents,,,,,Terminal-Bench,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0421,Benchmarks And Evaluation,benchmarks-and-evaluation,Tool,🧰,Terminal-Bench repository,https://github.com/harbor-framework/terminal-bench,external,github.com,Open-source benchmark and harness for hard terminal tasks.,Open-source benchmark and harness for hard terminal tasks.,Open-source benchmark and harness for hard terminal tasks.,The work turns loop quality into a measurable task or score. Resource-specific angle: Open-source benchmark and harness for hard terminal tasks.,Gives readers a concrete source in Terminal-Bench repository when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (2,458 stars; 558 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,950,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L950,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/harbor-framework/terminal-bench,GitHub - harbor-framework/terminal-bench: A benchmark for LLMs on complicated tasks in the terminal · GitHub,A benchmark for LLMs on complicated tasks in the terminal - harbor-framework/terminal-bench,,2025-01-17,2025,harbor-framework/terminal-bench,GitHub,,,,github-api,harbor-framework/terminal-bench,2458,558,Apache-2.0,2025-01-17T22:34:26Z,2026-07-16T09:24:33Z,,2026-07-17T05:43:07+00:00
-ale-0422,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AgentBench,https://arxiv.org/abs/2308.03688,external,arxiv.org,Multi-environment benchmark for evaluating LLMs as agents.,Multi-environment benchmark for evaluating LLMs as agents.,Multi-environment benchmark for evaluating LLMs as agents.,The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-environment benchmark for evaluating LLMs as agents.,Gives readers a concrete source in AgentBench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2308.03688; inspect its method and evaluation before treating results as production evidence.,medium,README.md,951,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L951,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2308.03688,[2308.03688] AgentBench: Evaluating LLMs as Agents,"The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities. Our extensive test over \num API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and many OSS competitors that are no larger than 70B. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Improving instruction following and training on high quality multi-round alignment data could improve agent performance. And different from existing assumptions, training on code present ambivalent impacts on different agent tasks. Datasets, environments, and an integrated evaluation package for AgentBench are released at https://github.com/THUDM/AgentBench.",Xiao Liu; Hao Yu; Hanchen Zhang; Yifan Xu; Xuanyu Lei; Hanyu Lai; Yu Gu; Hangliang Ding; Kaiwen Men; Kejuan Yang; Shudan Zhang; Xiang Deng; Aohan Zeng; Zhengxiao Du; Chenhui Zhang; Sheng Shen; Tianjun Zhang; Yu Su; Huan Sun; Minlie Huang; Yuxiao Dong; Jie Tang,2023-08-07,2023,arXiv,arXiv,,Published in ICLR 2024,cs.AI,arxiv-api,,,,,,,2308.03688,2026-07-17T05:43:07+00:00
-ale-0423,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,WebArena,https://arxiv.org/abs/2307.13854,external,arxiv.org,Realistic web environment for autonomous agents.,Realistic web environment for autonomous agents.,Realistic web environment for autonomous agents.,Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Realistic web environment for autonomous agents.,Gives readers a concrete source in WebArena when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2307.13854; inspect its method and evaluation before treating results as production evidence.,medium,README.md,952,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L952,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2307.13854,[2307.13854] WebArena: A Realistic Web Environment for Building Autonomous Agents,"With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress.",Shuyan Zhou; Frank F. Xu; Hao Zhu; Xuhui Zhou; Robert Lo; Abishek Sridhar; Xianyi Cheng; Tianyue Ou; Yonatan Bisk; Daniel Fried; Uri Alon; Graham Neubig,2023-07-25,2023,arXiv,arXiv,,"Our code, data, environment reproduction resources, and video demonstrations are publicly available at https://webarena.dev/",cs.AI,arxiv-api,,,,,,,2307.13854,2026-07-17T05:43:07+00:00
-ale-0424,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,OSWorld,https://arxiv.org/abs/2404.07972,external,arxiv.org,Benchmark for multimodal agents operating full computer environments.,Benchmark for multimodal agents operating full computer environments.,Benchmark for multimodal agents operating full computer environments.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for multimodal agents operating full computer environments.,Gives readers a concrete source in OSWorld when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2404.07972; inspect its method and evaluation before treating results as production evidence.,medium,README.md,953,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L953,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2404.07972,[2404.07972] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments,"Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWorld, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36% of the tasks, the best model achieves only 12.24% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWorld provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.",Tianbao Xie; Danyang Zhang; Jixuan Chen; Xiaochuan Li; Siheng Zhao; Ruisheng Cao; Toh Jing Hua; Zhoujun Cheng; Dongchan Shin; Fangyu Lei; Yitao Liu; Yiheng Xu; Shuyan Zhou; Silvio Savarese; Caiming Xiong; Victor Zhong; Tao Yu,2024-04-11,2024,arXiv,arXiv,,"51 pages, 21 figures",cs.AI,arxiv-api,,,,,,,2404.07972,2026-07-17T05:43:07+00:00
-ale-0425,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,ToolBench,https://arxiv.org/abs/2307.16789,external,arxiv.org,Tool-use benchmark and dataset for tool-augmented agents.,Tool-use benchmark and dataset for tool-augmented agents.,Tool-use benchmark and dataset for tool-augmented agents.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Tool-use benchmark and dataset for tool-augmented agents.,Gives readers a concrete source in ToolBench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2307.16789; inspect its method and evaluation before treating results as production evidence.,medium,README.md,954,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L954,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2307.16789,[2307.16789] ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs,"Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excellent tool-use capabilities of state-of-the-art (SOTA) closed-source LLMs, e.g., ChatGPT. To bridge this gap, we introduce ToolLLM, a general tool-use framework encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is constructed automatically using ChatGPT. Specifically, the construction can be divided into three stages: (i) API collection: we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub; (ii) instruction generation: we prompt ChatGPT to generate diverse instructions involving these APIs, covering both single-tool and multi-tool scenarios; (iii) solution path annotation: we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To enhance the reasoning capabilities of LLMs, we develop a novel depth-first search-based decision tree algorithm. It enables LLMs to evaluate multiple reasoning traces and expand the search space. Moreover, to evaluate the tool-use capabilities of LLMs, we develop an automatic evaluator: ToolEval. Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction. Experiments show that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. Our ToolLLaMA also demonstrates strong zero-shot generalization ability in an out-of-distribution tool-use dataset: APIBench.",Yujia Qin; Shihao Liang; Yining Ye; Kunlun Zhu; Lan Yan; Yaxi Lu; Yankai Lin; Xin Cong; Xiangru Tang; Bill Qian; Sihan Zhao; Lauren Hong; Runchu Tian; Ruobing Xie; Jie Zhou; Mark Gerstein; Dahai Li; Zhiyuan Liu; Maosong Sun,2023-07-31,2023,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2307.16789,2026-07-17T05:43:07+00:00
-ale-0426,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,GAIA,https://arxiv.org/abs/2311.12983,external,arxiv.org,"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.",Gives readers a concrete source in GAIA when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2311.12983; inspect its method and evaluation before treating results as production evidence.,medium,README.md,955,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L955,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2311.12983,[2311.12983] GAIA: a benchmark for General AI Assistants,"We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark.",Grégoire Mialon; Clémentine Fourrier; Craig Swift; Thomas Wolf; Yann LeCun; Thomas Scialom,2023-11-21,2023,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2311.12983,2026-07-17T05:43:07+00:00
-ale-0427,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Tau-bench,https://arxiv.org/abs/2406.12045,external,arxiv.org,Benchmark for tool-agent-user interactions in realistic domains.,Benchmark for tool-agent-user interactions in realistic domains.,Benchmark for tool-agent-user interactions in realistic domains.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for tool-agent-user interactions in realistic domains.,Gives readers a concrete source in Tau-bench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2406.12045; inspect its method and evaluation before treating results as production evidence.,medium,README.md,956,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L956,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2406.12045,[2406.12045] $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains,"Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state. We also propose a new metric (pass^k) to evaluate the reliability of agent behavior over multiple trials. Our experiments show that even state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point to the need for methods that can improve the ability of agents to act consistently and follow rules reliably.",Shunyu Yao; Noah Shinn; Pedram Razavi; Karthik Narasimhan,2024-06-17,2024,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2406.12045,2026-07-17T05:43:07+00:00
-ale-0428,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,VisualWebArena,https://arxiv.org/abs/2401.13649,external,arxiv.org,Visually grounded web-agent benchmark extending WebArena.,Visually grounded web-agent benchmark extending WebArena.,Visually grounded web-agent benchmark extending WebArena.,The work turns loop quality into a measurable task or score. Resource-specific angle: Visually grounded web-agent benchmark extending WebArena.,Gives readers a concrete source in VisualWebArena when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2401.13649; inspect its method and evaluation before treating results as production evidence.,medium,README.md,957,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L957,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2401.13649,[2401.13649] VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks,"Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on realistic \textit{visually grounded tasks}. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web. Our code, baseline models, and data is publicly available at https://jykoh.com/vwa.",Jing Yu Koh; Robert Lo; Lawrence Jang; Vikram Duvvur; Ming Chong Lim; Po-Yu Huang; Graham Neubig; Shuyan Zhou; Ruslan Salakhutdinov; Daniel Fried,2024-01-24,2024,arXiv,arXiv,,Accepted to ACL 2024. 24 pages. Project page: https://jykoh.com/vwa,cs.LG,arxiv-api,,,,,,,2401.13649,2026-07-17T05:43:07+00:00
-ale-0429,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AppWorld,https://arxiv.org/abs/2407.18901,external,arxiv.org,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of interactive app tasks with state-based and execution-based evaluation.,Gives readers a concrete source in AppWorld when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2407.18901; inspect its method and evaluation before treating results as production evidence.,medium,README.md,958,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L958,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.18901,[2407.18901] AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents,"Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built $\textbf{AppWorld Engine}$, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created $\textbf{AppWorld Benchmark}$ (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT-4o, solves only ~49% of our 'normal' tasks and ~30% of 'challenge' tasks, while other models solve at least 16% fewer. This highlights the benchmark's difficulty and AppWorld's potential to push the frontiers of interactive coding agents. The project website is available at https://appworld.dev/.",Harsh Trivedi; Tushar Khot; Mareike Hartmann; Ruskin Manku; Vinty Dong; Edward Li; Shashank Gupta; Ashish Sabharwal; Niranjan Balasubramanian,2024-07-26,2024,arXiv,arXiv,,ACL'24 Camera Ready,cs.SE,arxiv-api,,,,,,,2407.18901,2026-07-17T05:43:07+00:00
-ale-0430,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Vending-Bench,https://arxiv.org/abs/2502.15840,external,arxiv.org,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Gives readers a concrete source in Vending-Bench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2502.15840; inspect its method and evaluation before treating results as production evidence.,medium,README.md,959,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L959,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2502.15840,[2502.15840] Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents,"While Large Language Models (LLMs) can exhibit impressive proficiency in isolated, short-term tasks, they often fail to maintain coherent performance over longer time horizons. In this paper, we present Vending-Bench, a simulated environment designed to specifically test an LLM-based agent's ability to manage a straightforward, long-running business scenario: operating a vending machine. Agents must balance inventories, place orders, set prices, and handle daily fees - tasks that are each simple but collectively, over long horizons (>20M tokens per run) stress an LLM's capacity for sustained, coherent decision-making. Our experiments reveal high variance in performance across multiple LLMs: Claude 3.5 Sonnet and o3-mini manage the machine well in most runs and turn a profit, but all models have runs that derail, either through misinterpreting delivery schedules, forgetting orders, or descending into tangential ""meltdown"" loops from which they rarely recover. We find no clear correlation between failures and the point at which the model's context window becomes full, suggesting that these breakdowns do not stem from memory limits. Apart from highlighting the high variance in performance over long time horizons, Vending-Bench also tests models' ability to acquire capital, a necessity in many hypothetical dangerous AI scenarios. We hope the benchmark can help in preparing for the advent of stronger AI systems.",Axel Backlund; Lukas Petersson,2025-02-20,2025,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2502.15840,2026-07-17T05:43:07+00:00
-ale-0431,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Vending-Bench leaderboard,https://andonlabs.com/evals/vending-bench,external,andonlabs.com,Live long-horizon coherence results from Andon Labs.,Live long-horizon coherence results from Andon Labs.,Live long-horizon coherence results from Andon Labs.,The work turns loop quality into a measurable task or score. Resource-specific angle: Live long-horizon coherence results from Andon Labs.,Gives readers a concrete source in Vending-Bench leaderboard when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,960,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L960,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://andonlabs.com/evals/vending-bench,Vending-Bench: Testing long-term coherence in agents | Andon Labs,"How do agents act over very long horizons? We answer this by letting agents manage a simulated vending machine business. The agents need to handle ordering, inventory management, and pricing over long context horizons to successfully make money.",,,,,andonlabs.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0432,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios,https://arxiv.org/abs/2512.18470,external,arxiv.org,"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","The work turns loop quality into a measurable task or score. Resource-specific angle: Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.",Gives readers a concrete source in SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,961,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L961,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2512.18470,[2512.18470] SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios,"Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-horizon endeavor: developers interpret high-level requirements, coordinate changes across many files, and evolve codebases over multiple iterations while preserving functionality. We introduce SWE-EVO, a benchmark for this long-horizon software evolution challenge. Constructed from release notes of seven mature open-source Python projects, SWE-EVO comprises 48 tasks requiring multi-step modifications spanning an average of 21 files, validated against test suites averaging 874 tests per instance. Experiments reveal a striking capability gap: GPT-5.4 with OpenHands achieves only 25% on SWE-EVO versus 72.80% achieved by GPT-5.2 on SWE-Bench Verified, showing that current agents struggle with sustained, multi-file reasoning. We also propose Fix Rate, a metric capturing partial progress on these complex, long-horizon tasks.",Tue Le; Minh V. T. Thai; Dung Nguyen Manh; Huy Phan Nhat; Nghi D. Q. Bui,2025-12-20,2025,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2512.18470,2026-07-17T05:43:07+00:00
-ale-0433,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification,https://arxiv.org/abs/2604.01687,external,arxiv.org,"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.",Gives readers a concrete source in EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.01687; inspect its method and evaluation before treating results as production evidence.,medium,README.md,962,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L962,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.01687,[2604.01687] CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification,"Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose CoEvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, CoEvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, CoEvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and also exhibits strong generalization capabilities to six additional LLMs.",Hanrong Zhang; Shicheng Fan; Henry Peng Zou; Yankai Chen; Zhenting Wang; Jiayu Zhou; Chengze Li; Wei-Chieh Huang; Yifei Yao; Kening Zheng; Xue Liu; Xiaoxiao Li; Philip S. Yu,2026-04-02,2026,arXiv,arXiv,,Code will be released,cs.AI,arxiv-api,,,,,,,2604.01687,2026-07-17T05:43:07+00:00
-ale-0434,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering,https://arxiv.org/abs/2605.17526,external,arxiv.org,"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.",Gives readers a concrete source in SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,963,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L963,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.17526,[2605.17526] SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering,"As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to capture the heterogeneous environments, full-stack orchestration, and system-level complexity of real enterprise Software as a Service (SaaS) systems, leaving a critical gap in assessing agents under realistic engineering constraints. To fill this gap, we introduce SaaSBench, the first benchmark designed to explore the boundaries of AI agents in enterprise SaaS engineering. Spanning 30 complex tasks across 6 SaaS domains with 5,370 validation nodes, it incorporates 8 programming languages, 6 databases, and 13 frameworks to meticulously mirror real-world software heterogeneity. Furthermore, we design a dependency-aware hybrid evaluation paradigm tailored for complex systems with long horizons and multi-component coupling, enabling fine-grained, reproducible assessment. Crucially, our extensive experiments reveal a striking insight: the primary bottleneck for state-of-the-art agents is not generating isolated code logic, but successfully configuring and integrating a multi-component system. Over 95\% of task failures occur before agents even reach deep business logic, with models often falling victim to overconfidence and prematurely halting during foundational system setup, or getting trapped in ineffective debugging loops. We hope SaaSBench serves as a practical and challenging testbed to drive the evolution of reliable, system-level coding agents. The code is available at \url{https://github.com/ShadeCloak/SaaSbench}.",Qingnan Ren; Shun Zou; Shiting Huang; Ziao Zhang; Kou Shi; Zhen Fang; Yiming Zhao; Yu Zeng; Qisheng Su; Lin Chen; Yong Wang; Zehui Chen; Xiangxiang Chu; Feng Zhao,2026-05-17,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2605.17526,2026-07-17T05:43:07+00:00
-ale-0435,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades,https://arxiv.org/abs/2605.15846,external,arxiv.org,"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: 115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.",Gives readers a concrete source in RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,964,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L964,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.15846,[2605.15846] RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades,"Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass/fail evaluation outcomes, and thus fail to capture long-horizon, multi-target development at real engineering scale. To address this gap, we present RoadmapBench, a benchmark of 115 long-horizon coding tasks grounded in real open-source version upgrades across 17 repositories and 5 programming languages. Each task places the agent on a source-version code snapshot and provides a multi-target roadmap instruction requiring it to implement the functionality introduced in the target version, with a median modification of 3,700 lines across 51 files. We conduct a systematic evaluation on thirteen frontier models and find that even the strongest, Claude-Opus-4.7, resolves only 39.1% of tasks, while the weakest achieves merely 5.2%, in stark contrast to existing bug-fix benchmarks, suggesting that long-horizon software development remains a largely unsolved problem.",Xinbo Xu; Ruihan Yang; Haiyang Shen; Wendong Xu; Bofei Gao; Ruoyu Wu; Kean Shi; Weichu Xie; Xuanzhong Chen; Ming Wu; Jason Zeng; Michael Heinrich; Elvis Zhang; Liang Chen; Kuan Li; Baobao Chang,2026-05-15,2026,arXiv,arXiv,,"30 pages, 15 figures",cs.SE,arxiv-api,,,,,,,2605.15846,2026-07-17T05:43:07+00:00
-ale-0436,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code,https://arxiv.org/abs/2503.07832,external,arxiv.org,"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.",Gives readers a concrete source in RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,965,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L965,,Verify,verify,"Gate progress with tests, evals, and evidence.",state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2503.07832,[2503.07832] RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code,"Recent advances in language model (LM) agents and function calling have enabled autonomous, feedback-driven systems to solve problems across various digital domains. To better understand the unique limitations of LM agents, we introduce RefactorBench, a benchmark consisting of 100 large handcrafted multi-file refactoring tasks in popular open-source repositories. Solving tasks within RefactorBench requires thorough exploration of dependencies across multiple files and strong adherence to relevant instructions. Every task is defined by 3 natural language instructions of varying specificity and is mutually exclusive, allowing for the creation of longer combined tasks on the same repository. Baselines on RefactorBench reveal that current LM agents struggle with simple compositional tasks, solving only 22% of tasks with base instructions, in contrast to a human developer with short time constraints solving 87%. Through trajectory analysis, we identify various unique failure modes of LM agents, and further explore the failure mode of tracking past actions. By adapting a baseline agent to condition on representations of state, we achieve a 43.9% improvement in solving RefactorBench tasks. We further extend our state-aware approach to encompass entire digital environments and outline potential directions for future research. RefactorBench aims to support the study of LM agents by providing a set of real-world, multi-hop tasks within the realm of code.",Dhruv Gautam; Spandan Garg; Jinu Jang; Neel Sundaresan; Roshanak Zilouchian Moghaddam,2025-03-10,2025,arXiv,arXiv,,ICLR 2025 Camera Ready,cs.AI,arxiv-api,,,,,,,2503.07832,2026-07-17T05:43:07+00:00
-ale-0437,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents,https://arxiv.org/abs/2606.22678,external,arxiv.org,"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.",Gives readers a concrete source in RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,966,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L966,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.22678,[2606.22678] RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents,"Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks. Existing benchmarks evaluate these agents almost exclusively on outcome correctness: whether generated code passes tests or resolves issues. We argue that this outcome-only lens is insufficient: an agent that arrives at a correct solution through reckless trial-and-error, without planning, verification, or graceful recovery, is fundamentally less reliable than one that follows sound engineering discipline. We introduce RigorBench, the first benchmark designed to measure process discipline in AI coding agents. RigorBench evaluates these harnesses across five pillars: Planning Fidelity, Verification Coverage, Recovery Efficiency, Abstention Quality, and Atomic Transition Integrity. A composite RigorScore aggregates these dimensions into a single metric via a weighted sum. We curate a suite of 30 tasks spanning five categories - Plan-Then-Build, Verify-Or-Die, Doom Loop Gauntlet, Know When to Fold, and Don't Break the Build-and evaluate leading harnesses in a controlled with/without experimental design against baseline coding assistants. Our results show that structured process discipline not only improves process quality scores by an average of 41% but also raises downstream outcome correctness by 17%, providing the first quantitative evidence that how agents code matters as much as what they produce. We release the full benchmark, scoring rubrics, and trajectory analysis tools as open-source artifacts.",Meher Bhaskar Madiraju; Meher Sai Preetam Madiraju,2026-06-21,2026,arXiv,arXiv,,"9 pages, 7 tables, 1 figure",cs.SE,arxiv-api,,,,,,,2606.22678,2026-07-17T05:43:07+00:00
-ale-0438,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks,https://arxiv.org/abs/2603.24755,external,arxiv.org,"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.",Gives readers a concrete source in SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,967,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L967,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state;budget,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.24755,[2603.24755] SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks,"Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmarks attempt to remedy this but heavily constrain an agent's design decision space, making it impossible to faithfully measure how their decisions shape future extensions. We introduce SlopCodeBench, a benchmark of 36 problems and 196 checkpoints where agents repeatedly extend their own solutions. Unlike prior iterative benchmarks, our evolving specifications demand architectural decisions but leave internal structure to the agent. We measure two forms of degradation: structural erosion (concentrated complexity) and verbosity (redundant code). Evaluating 15 coding agents across open and closed models, we find that no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints. Quality degrades across checkpoints, with structural erosion rising in 77% of trajectories and verbosity in 75.5%. Compared to 473 open-source Python repositories, agent code is 2.3x more verbose and 2.0x more eroded, and the human repositories degrade less often and by smaller margins across their git histories. Explicit quality guidance reduces initial verbosity and erosion by up to a third, without affecting degradation rates. SlopCodeBench provides the first measurement of code degradation under iterative extension, revealing that agents pass checkpoints while producing code that erodes and bloats with each turn.",Gabriel Orlanski; Devjeet Roy; Alexander Yun; Changho Shin; Alex Gu; Albert Ge; Dyah Adila; Nicholas Roberts; Frederic Sala; Aws Albarghouthi,2026-03-25,2026,arXiv,arXiv,"10.5281/zenodo.18405900,",Code and Leaderboards are located at https://www.scbench.ai,cs.SE,arxiv-api,,,,,,,2603.24755,2026-07-17T05:43:07+00:00
-ale-0439,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces,https://arxiv.org/abs/2602.14337,external,arxiv.org,"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","The work turns loop quality into a measurable task or score. Resource-specific angle: Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.",Gives readers a concrete source in LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,968,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L968,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2602.14337,[2602.14337] LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces,"Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics, fail to rigorously evaluate the long-horizon planning and execution capabilities essential for realistic software engineering. To address these gaps, we introduce LongCLI-Bench, a comprehensive benchmark designed to evaluate agentic capabilities across long-horizon, realistic tasks. We curated 20 high-quality, long-horizon tasks from over 1,000 computer science assignments and real-world workflows, covering four engineering categories: from scratch, feature addition, bug fixing, and refactoring. We propose a dual-set testing protocol for LongCLI-Bench, which measures requirement fulfillment (fail-to-pass) and regression avoidance (pass-to-pass), and incorporates step-level scoring to pinpoint execution failures. Extensive experiments reveal that even state-of-the-art agents achieve pass rates below 20% in LongCLI-Bench. Step-level analysis further indicates that the majority of tasks stall at less than 30% completion, highlighting that critical failures often occur in the early stages. Although self-correction offers marginal gains, human-agent collaboration through plan injection and interactive guidance yields significantly higher improvements. These results highlight that future research must emphasize the development of synergistic human-agent workflows alongside advances in agents' planning and execution capabilities to overcome key challenges in long-horizon task performance.",Yukang Feng; Jianwen Sun; Zelai Yang; Jiaxin Ai; Chuanhao Li; Zizhen Li; Fanrui Zhang; Kang He; Rui Ma; Jifan Lin; Jie Sun; Yang Xiao; Sizhuo Zhou; Wenxiao Wu; Yiming Liu; Pengfei Liu; Yu Qiao; Shenglin Zhang; Kaipeng Zhang,2026-02-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2602.14337,2026-07-17T05:43:07+00:00
-ale-0440,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?,https://arxiv.org/abs/2606.29920,external,arxiv.org,"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.",Gives readers a concrete source in Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios? when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,969,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L969,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.29920,[2606.29920] Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?,"Rubric-based scoring has become a widely used paradigm in model evaluation, typically with LLM-as-a-Judge (LaaJ) for rubric scoring. However, the reliability of LaaJ for rubric scoring remains underexplored. This concern is especially pronounced in agentic scenarios, where long, complex outputs further challenge reliable scoring. To address this, we conduct a systematic meta-evaluation of LaaJ reliability for rubric verification. We introduce RuVerBench, the first benchmark for assessing LaaJ reliability in rubric verification for agentic scenarios. RuVerBench covers two prevalent agentic domains, deep research and agentic coding, with 2,458 instances, each containing a model-generated output, a rubric, and a human-annotated label indicating whether the output satisfies the rubric. Using RuVerBench, we evaluate numerous frontier LLMs and find that even the most advanced models achieve strong performance but still exhibit substantial noise. We further analyze the impact of key LaaJ strategies, including prompt design, batching, and majority voting, on rubric verification. We find that weaker models are more sensitive to prompt variations, batched verification presents a trade-off between accuracy and efficiency, and majority voting yields effective but diminishing returns. We have released our dataset and code to facilitate future research: https://github.com/THU-KEG/RuVerBench.",Yangda Peng; Yunjia Qi; Hao Peng; Haotian Xia; Guanzhong He; Xintong Shi; Richeng Xuan; Songyuanyi Lu; Yixian Liu; Zhichao Hu; Yuhong Liu; Lei Hou; Bin Xu; Juanzi Li,2026-06-29,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2606.29920,2026-07-17T05:43:07+00:00
-ale-0441,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SentinelBench: A Benchmark for Long-Running Monitoring Agents,https://arxiv.org/abs/2606.05342,external,arxiv.org,"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","The work turns loop quality into a measurable task or score. Resource-specific angle: Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.",Gives readers a concrete source in SentinelBench: A Benchmark for Long-Running Monitoring Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,970,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L970,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.05342,[2606.05342] SentinelBench: A Benchmark for Long-Running Monitoring Agents,"AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress. This is the wrong approach for many long-running tasks, which are better served by a strategy of sustained attention. Instead, agents should monitor an environment, notice when an external event makes progress possible, then respond promptly without wasting resources while waiting. To measure progress on this class of tasks, we introduce SentinelBench, an open-source benchmark for time-evolving monitoring tasks. SentinelBench contains 100 tasks across 10 synthetic web environments, including email, calendars, finance, professional networking, and entertainment. Each environment exposes a live web interface and replays a scripted sequence of events, requiring agents to navigate and reason about web pages whose state shifts underfoot. SentinelBench measures task completion, reaction time, and resource use, exposing the tradeoff between responsiveness and cost. We report results across three models and two browser-agent harnesses, establishing performance baselines for future comparison and demonstrating how agent design choices can dramatically impact key metrics. Together, these results show that SentinelBench distinguishes meaningful differences in agent behavior.",Matheus Kunzler Maldaner; Adam Fourney; Amanda Swearngin; Hussein Mozannar; Gagan Bansal; Maya Murad; Rafah Hosn; Saleema Amershi,2026-06-03,2026,arXiv,arXiv,,"18 pages, 16 figures",cs.AI,arxiv-api,,,,,,,2606.05342,2026-07-17T05:43:07+00:00
-ale-0442,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-Together: Evaluating Coding Agents in Interactive User Sessions,https://arxiv.org/abs/2606.29957,external,arxiv.org,"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.",Gives readers a concrete source in SWE-Together: Evaluating Coding Agents in Interactive User Sessions when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,971,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L971,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.29957,[2606.29957] SWE-Together: Evaluating Coding Agents in Interactive User Sessions,"Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assistance is interactive, with users clarifying goals, adding constraints, and correcting mistakes over multiple turns. We introduce SWE-Together, a multi-turn benchmark reconstructed from real user-agent coding sessions. To make real interactions verifiable, we curate 109 repository-level tasks from 11,260 recorded sessions, selecting sessions with recoverable repository states, clear user goals, and observable outcomes. To replay these interactions across agents, we build a reactive LLM-based user simulator that preserves the original users' intents and provides feedback when the coding agent's progress requires it. To evaluate agents as collaborators, we measure both final repository correctness and the number of corrective feedback turns required during the interaction. Experiments with frontier coding agents show that stronger agents generally achieve higher final success rates while requiring fewer interventions, suggesting an improved user experience.",Yifan Wu; Zhuokai Zhao; Songlin Li; Ho Hin Lee; Jiacheng Zhu; Shirley Wu; Tianhe Yu; Serena Li; Lizhu Zhang; Xiangjun Fan; Shengzhi Li,2026-06-29,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2606.29957,2026-07-17T05:43:07+00:00
-ale-0443,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break,https://arxiv.org/abs/2604.11978,external,arxiv.org,"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","The work turns loop quality into a measurable task or score. Resource-specific angle: Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.",Gives readers a concrete source in The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,972,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L972,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2604.11978,[2604.11978] The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break,"Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross-domain diagnostic benchmark for systematically constructing tasks and analyzing long-horizon failure behaviors in LLM-based agents. Using HORIZON, we evaluate state-of-the-art (SOTA) agents from multiple model families (GPT-5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon-dependent degradation patterns. We further propose a trajectory-grounded LLM-as-a-Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter-annotator \kappa=0.61; human-judge \kappa=0.84). Our findings offer an initial methodological step toward systematic, cross-domain analysis of long-horizon agent failures and offer practical guidance for building more reliable long-horizon agents. We release our project website at \href{https://xwang2775.github.io/horizon-leaderboard/}{HORIZON Leaderboard} and welcome contributions from the community.",Xinyu Jessica Wang; Haoyue Bai; Yiyou Sun; Haorui Wang; Shuibai Zhang; Wenjie Hu; Mya Schroder; Bilge Mutlu; Dawn Song; Robert D Nowak,2026-04-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2604.11978,2026-07-17T05:43:07+00:00
-ale-0444,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents,https://arxiv.org/abs/2603.29231,external,arxiv.org,"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.",Gives readers a concrete source in Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2603.29231; inspect its method and evaluation before treating results as production evidence.,medium,README.md,973,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L973,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.29231,[2603.29231] Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents,"Existing benchmarks measure capability -- whether a model succeeds on a single attempt -- but production deployments require reliability -- consistent success across repeated attempts on tasks of varying duration. We show these properties diverge systematically as task duration grows, and that pass@1 on short tasks is structurally blind to this divergence. We introduce a reliability science framework for long-horizon LLM agents with four metrics: Reliability Decay Curve (RDC), Variance Amplification Factor (VAF), Graceful Degradation Score (GDS), and Meltdown Onset Point (MOP). We evaluate 10 models across 23,392 episodes on a 396-task benchmark spanning four duration buckets and three domains. Key findings: (1) reliability decay is domain-stratified -- SE GDS drops from 0.90 to 0.44 while document processing is nearly flat (0.74 to 0.71); (2) VAF bifurcates by capability tier -- high VAF is a capability signature, not an instability signal; (3) capability and reliability rankings diverge substantially, with multi-rank inversions at long horizons; (4) frontier models have the highest meltdown rates (up to 19%) because they attempt ambitious multi-step strategies that sometimes spiral; and (5) memory scaffolds universally hurt long-horizon performance across all 10 models. These results motivate reliability as a first-class evaluation dimension alongside capability.",Aaditya Khanal; Yangyang Tao; Junxiu Zhou,2026-03-31,2026,arXiv,arXiv,,"23 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2603.29231,2026-07-17T05:43:07+00:00
-ale-0445,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SEAGym: An Evaluation Environment for Self-Evolving LLM Agents,https://arxiv.org/abs/2606.17546,external,arxiv.org,"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.",Gives readers a concrete source in SEAGym: An Evaluation Environment for Self-Evolving LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,974,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L974,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;budget,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.17546,[2606.17546] SEAGym: An Evaluation Environment for Self-Evolving LLM Agents,"Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batches, frozen update-validation, held-out ID and OOD transfer views, replay diagnostics, and saved snapshot and metric records. Instantiating SEAGym on Terminal-Bench 2.0 and HLE, we compare ACE, TF-GRPO, and AHE under a shared epoch/batch protocol. The results show that these evaluation views provide complementary signals about the evolution process: frequent updates may fail to improve held-out performance, useful intermediate snapshots may collapse later, and source diversity and model backend can affect harness reliability.",Congjie Zheng; Chuanyi Xue; Bin Liang; Jun Yang; Changshui Zhang,2026-06-16,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.17546,2026-07-17T05:43:07+00:00
-ale-0446,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions,https://arxiv.org/abs/2605.24110,external,arxiv.org,"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.",Gives readers a concrete source in EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,975,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L975,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.24110,[2605.24110] EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions,"Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assessment. This leaves out a basic question: can an agent keep its own codebase working as requirements change? We introduce EvoCode-Bench, a benchmark of 26 stateful coding tasks and 227 evaluated rounds. Each task preserves the agent's workspace for 5-15 rounds, states requirements through observable behavior, and uses cumulative executable tests to check new requirements and still-active prior ones. We evaluate 13 coding agents with two metrics: MT@4, a four-attempt fail-stop multi-round score, and SR, a single-round score from a reference-completed prior state. For most agents, SR exceeds MT@4 by 22-40 points. The gap also changes rankings: the highest-SR agent (78.9) ranks only third in persistent execution (44.0 MT@4). Even the strongest agents achieve only about 50% success on multi-turn metrics, and aggregate pass rate drops below half of round-1 performance by round 5. Failure analysis shows tier-dependent behavior: weaker agents fail early, while stronger agents survive long enough to expose specification-tracking and regression failures. We release the benchmark data and Harbor multi-turn infrastructure.",Haiyang Shen; Xuanzhong Chen; Wendong Xu; Yun Ma; Liang Chen; Kuan Li,2026-05-22,2026,arXiv,arXiv,,"Work in Progress; 32 pages, 10 figures, preprint",cs.AI,arxiv-api,,,,,,,2605.24110,2026-07-17T05:43:07+00:00
-ale-0447,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,On the Reliability of Computer Use Agents,https://arxiv.org/abs/2604.17849,external,arxiv.org,"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.",Gives readers a concrete source in On the Reliability of Computer Use Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.17849; inspect its method and evaluation before treating results as production evidence.,medium,README.md,976,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L976,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.17849,[2604.17849] On the Reliability of Computer Use Agents,"Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task. This raises a fundamental question: if an agent can succeed at a task once, what prevents it from doing so reliably? In this work, we study the sources of unreliability in computer-use agents through three factors: stochasticity during execution, ambiguity in task specification, and variability in agent behavior. We analyze these factors on OSWorld using repeated executions of the same task together with paired statistical tests that capture task-level changes across settings. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions. These findings suggest the need to evaluate agents under repeated execution, to allow agents to resolve task ambiguity through interaction, and to favor strategies that remain stable across runs.",Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang,2026-04-20,2026,arXiv,arXiv,,"33 pages, 3 figures, 4 tables",cs.AI,arxiv-api,,,,,,,2604.17849,2026-07-17T05:43:07+00:00
-ale-0448,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation,https://arxiv.org/abs/2605.12925,external,arxiv.org,"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.",Gives readers a concrete source in AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2605.12925; inspect its method and evaluation before treating results as production evidence.,medium,README.md,977,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L977,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.12925,[2605.12925] AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation,"Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show that this equivalence is empirically false. We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks. Of these, 47 have enough passing trajectories to construct task-level process references, yielding a 1,815-trajectory evaluation subset. Among passing trajectories in this subset, 10.7% exhibit behavior we call a Lucky Pass: regression cycles, blind retries, missing verification, or temporally disordered exploration, implementation, and verification. We introduce AgentLens, a framework for process-level assessment of SWE-agent trajectories, and define AgentLens-Bench, a dataset of 1,815 trajectories annotated with quality scores, waste signals, divergence points, and 47 task-level Prefix Tree Acceptor (PTA) references. AgentLens builds PTA references by merging multiple passing solutions for the same task, and uses a context-sensitive intent labeler to assign actions to Exploration, Implementation, Verification, or Orchestration based on trajectory history rather than tool identity alone. On AgentLens-Bench, the quality score separates passing trajectories into Lucky, Solid, and Ideal tiers and further decomposes Lucky Passes into five recurring mechanisms. Across the eight model backends, Lucky rates range from 0.5% to 23.2%, and some models move by as many as five rank positions when ranked by quality score instead of pass rate. We plan to release the project repository soon, including AgentLens-Bench artifacts, the AgentLens SDK, and the analysis tooling.",Priyam Sahoo; Gaurav Mittal; Xiaomin Li; Shengjie Ma; Benjamin Steenhoek; Pingping Lin; Yu Hu,2026-05-13,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2605.12925,2026-07-17T05:43:07+00:00
-ale-0449,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction,https://arxiv.org/abs/2601.21008,external,arxiv.org,"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.",Gives readers a concrete source in ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,978,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L978,,Verify,verify,"Gate progress with tests, evals, and evidence.",trigger;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2601.21008,[2601.21008] ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research,"Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), identifying constraint conflicts, and repairing formulations until feasibility is restored. Existing LLM benchmarks mostly treat OR as one-shot translation from problem descriptions to solver code, omitting this diagnostic loop. We formalize infeasible-model repair as a solver-in-the-loop Markov Decision Process in which each action triggers solver re-execution and IIS recomputation, yielding deterministic, verifiable feedback. We introduce ORLoopBench, a benchmark suite with two components: OR-Debug-Bench releases 5,362 LP/MILP repair instances, while OR-Bias-Bench evaluates closed-form operational decision rationality across inventory settings. Solver-verified RLVR training enables an 8B model to surpass frontier APIs on LP repair (95.3% vs 92.4% RR @5), improves diagnostic behavior, and transfers to MILP repair. The same evaluation exposes semantic drift in whole-model code regeneration: feasible regenerated MILPs can solve the wrong problem. Process-level evaluation with solver oracles enables targeted training for reliable OR self-correction.",Ruicheng Ao; David Simchi-Levi; Xinshang Wang,2026-01-28,2026,arXiv,arXiv,,"58 pages, accepted by ICML 2026",cs.LG,arxiv-api,,,,,,,2601.21008,2026-07-17T05:43:07+00:00
-ale-0450,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis,https://arxiv.org/abs/2605.30434,external,arxiv.org,"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.",Gives readers a concrete source in LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,979,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L979,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.30434,[2605.30434] LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis,"Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability to track evolving analytical context over long horizons untested. We introduce LongDS, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states. LongDS comprises 68 tasks constructed from real-world Kaggle notebooks, spanning 2,225 turns across six domains including Geoscience, Business, and Education. Tasks are designed around state-evolution patterns (e.g., counterfactual perturbation, rollback, multi-state composition), with an average dependency span of 11.3 turns. Evaluating five state-of-the-art models, we find that the best model reaches only 48.45% average accuracy, performance drops nearly 47 points from early to late turns, and long-horizon errors account for 52%--69% of failures. Further analysis shows that additional agent steps do not necessarily improve performance, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget. We release LongDS to support research on reliable long-horizon agentic data analysis. Code and data will be released at https://github.com/zjunlp/DataMind.",Kewei Xu; Xiaoben Lu; Shuofei Qiao; Zihan Ding; Haoming Xu; Lei Liang; Ningyu Zhang,2026-05-28,2026,arXiv,arXiv,,Ongoing work,cs.LG,arxiv-api,,,,,,,2605.30434,2026-07-17T05:43:07+00:00
-ale-0451,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks,https://arxiv.org/abs/2602.16313,external,arxiv.org,"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.",Gives readers a concrete source in MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,980,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L980,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2602.16313,[2602.16313] MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks,"Existing evaluations of agents with memory typically assess memorization and action in isolation. One class of benchmarks evaluates memorization by testing recall of past conversations or text but fails to capture how memory is used to guide future decisions. Another class focuses on agents acting in single-session tasks without the need for long-term memory. However, in realistic settings, memorization and action are tightly coupled: agents acquire memory while interacting with the environment, and subsequently rely on that memory to solve future tasks. To capture this setting, we introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops. The benchmark consists of human-crafted agentic tasks with explicitly interdependent subtasks, where agents must learn from earlier actions and feedback by distilling experiences into memory, and subsequently use that memory to guide later actions to solve the overall task. MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning, and reveals that agents with near-saturated performance on existing long-context memory benchmarks like LoCoMo perform poorly in our agentic setting, exposing a gap in current evaluations for agents with memory.",Zexue He; Yu Wang; Churan Zhi; Yuanzhe Hu; Tzu-Ping Chen; Lang Yin; Ze Chen; Tong Arthur Wu; Siru Ouyang; Zihan Wang; Jiaxin Pei; Julian McAuley; Yejin Choi; Alex Pentland,2026-02-18,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2602.16313,2026-07-17T05:43:07+00:00
-ale-0452,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations,https://arxiv.org/abs/2606.00832,external,arxiv.org,"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.",Gives readers a concrete source in Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,981,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L981,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;state;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.00832,[2606.00832] Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations,"Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmarks evaluate agents within a single session, ignoring past actions, stated preferences, and prior decisions that agents must integrate to fulfill personalized user goals. We introduce Momento, a benchmark for persistent agentic task completion in multi-session service environments, requiring agents to take consequential, tool-mediated actions while resolving temporal dependencies and evolving user goals across sessions. Experimental results reveal that current agents fail primarily through misestimation of user state, treating prior session history as a reliable proxy for current context rather than stale information requiring re-validation, highlighting a substantial gap between current agent capabilities and realistic long-horizon human-agent interaction.",Adril Putra Merin; David Anugraha; Ayu Purwarianti; Genta Indra Winata,2026-05-30,2026,arXiv,arXiv,,Preprint,cs.CL,arxiv-api,,,,,,,2606.00832,2026-07-17T05:43:07+00:00
-ale-0453,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows,https://arxiv.org/abs/2605.14678,external,arxiv.org,"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.",Gives readers a concrete source in π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,982,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L982,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.14678,[2605.14678] $π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows,"The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act on such hidden intents before they are explicitly stated, especially in sustained multi-turn interactions where user needs emerge gradually. To address this gap, we introduce $\pi$-Bench, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas. By incorporating hidden user intents, inter-task dependencies, and cross-session continuity, $\pi$-Bench evaluates agents' ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use. Experiments show (1) proactive assistance remains challenging, (2) a clear distinction between task completion and proactivity, and (3) the value of prior interaction for proactive intent resolution in later tasks.",Haoran Zhang; Luxin Xu; Zhilin Wang; Runquan Gui; Shunkai Zhang; Haodi Lei; Zihao He; Bingsu He; Chicheng Qin; Tong Zhu; Xiaoye Qu; Yang Yang; Yu Cheng; Yafu Li,2026-05-14,2026,arXiv,arXiv,,44 pages,cs.AI,arxiv-api,,,,,,,2605.14678,2026-07-17T05:43:07+00:00
-ale-0454,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation,https://arxiv.org/abs/2603.23638,external,arxiv.org,"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.",Gives readers a concrete source in Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,983,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L983,,Verify,verify,"Gate progress with tests, evals, and evidence.",state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.23638,[2603.23638] Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation in an Uncertain Enterprise Environment,"Large language model (LLM) agents are increasingly tested on complex tasks, but their ability to allocate scarce resources over long horizons remains unclear. Unlike reactive tasks with immediate feedback, this setting requires agents to make binding commitments under partial observability, delayed consequences, hard resource budgets, and shifting dynamics. We introduce EnterpriseArena, a 132-month CFO simulator that evaluates long-horizon resource allocation under uncertainty in a FinTech lending firm. Agents must manage liquidity, close books, gather costly signals, and request equity or debt financing across changing macroeconomic regimes. The simulator is built from transformed firm-level financial data, anonymized business documents, decade-scale macroeconomic and industry signals, and expert-validated operating rules. Experiments across 23 LLMs and four agent frameworks show that current agents remain far from robust: only 15.4% of trials survive the full horizon, larger models do not reliably outperform smaller ones, and failures cascade across observation, action timing, and capital sizing. These findings establish long-horizon resource allocation under uncertainty as a distinct capability gap for LLM agents.",Yi Han; Yan Wang; Lingfei Qian; Haohang Li; Yupeng Cao; Yueru He; Xueqing Peng; Nanhan Shen; Yitao Xu; Yankai Chen; Dongji Feng; Jimin Huang; Xue Liu; Jian-Yun Nie; Sophia Ananiadou,2026-03-24,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.23638,2026-07-17T05:43:07+00:00
-ale-0455,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer,https://arxiv.org/abs/2607.05202,external,arxiv.org,"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.",Gives readers a concrete source in EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,984,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L984,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.05202,[2607.05202] EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer,"Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmarks target information retention rather than procedural reuse. We introduce EvoAgentBench, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software engineering, and knowledge work. EvoAgentBench extracts trace-grounded Abilities from agent executions, canonicalizes them into operational units, and builds domain-specific Ability Graphs linking tasks that share procedural overlap. By design, every test task is backed by verified training-side Ability support. Across a 528/267 train/test split, two scaffolds, and three backbones, curated Ability content transfers reliably across model families, but no current automatic method sustains positive gain in all settings. EvoAgentBench shifts self-evolution evaluation from aggregate accuracy comparison to fine-grained diagnosis of experience encoding, routing, and uptake. The benchmark is publicly available at https://huggingface.co/datasets/EverMind-AI/EvoAgentBench.",Xingze Gao; Chuanrui Hu; Hongda Chen; Pengfei Yao; Zhao Wang; Yi Bai; Zhengwei Wu; Yunyun Han; Xiaofeng Cong; Jie Gui; Yafeng Deng; Teng Li,2026-07-06,2026,arXiv,arXiv,,"15 pages, 2 figures, 8 tables",cs.AI,arxiv-api,,,,,,,2607.05202,2026-07-17T05:43:07+00:00
-ale-0456,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents,https://arxiv.org/abs/2607.02255,external,arxiv.org,"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.",Gives readers a concrete source in AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,985,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L985,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.02255,[2607.02255] AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents,"Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw cross-decision transcript appended. The prompt thus stays bounded across runs of any length, and any single layer can be ablated in isolation. We instantiate the contract in Slay the Spire 2, a closed-rule stochastic deck-building game whose runs require hundreds of tactical and strategic decisions. A public online benchmark of frontier LLMs on the same game reports zero wins at the lowest difficulty across five configurations, and the developer-reported human win rate at the same difficulty is 16%; the task is hard but not saturated. Within our harness, a fixed-A0 ablation shows the largest observed difference when triggered strategic skills are enabled: the no-store baseline wins 3/10 games and adding the skill layer 6/10. At this sample size the comparison is directional rather than statistically decisive (Fisher exact p\approx0.37); a cross-backbone probe and public accumulating-context baselines are reported as operational comparisons rather than controlled tests of the contract variable itself. We release a reproducible testbed: 298 completed trajectories with condition tags, frozen memory/skill snapshots, prompt records, and analysis scripts -- an agent design and a validated, reusable methodology for studying how explicit memory layers shape long-horizon LLM-agent decisions.",Xiangchen Cheng; Yunwei Jiang; Jianwen Sun; Zizhen Li; Chuanhao Li; Xiangcheng Cao; Yihao Liu; Fanrui Zhang; Li Jin; Kaipeng Zhang,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02255,2026-07-17T05:43:07+00:00
-ale-0457,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops,https://arxiv.org/abs/2607.05197,external,arxiv.org,"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.",Gives readers a concrete source in Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05197; inspect its method and evaluation before treating results as production evidence.,medium,README.md,986,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L986,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05197,[2607.05197] Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops,"Iterative repair loops have become a core design pattern in LLM-based software engineering systems. These workflows repeatedly generate, validate, and repair artifacts using feedback such as compiler errors or test failures. Despite their widespread use, the impact of repair-loop iteration limits remains poorly understood, as most prior work adopts fixed, often arbitrary, repair budgets. We study repair-loop effectiveness across multiple software engineering tasks, including code generation, test generation, and code translation. Across several representative workflows, datasets, and contemporary low-cost LLMs, we observe a consistent pattern of diminishing returns: the first three to four repair iterations account for most achievable gains, while later iterations contribute only marginal improvements. We further find that repair behavior is influenced more strongly by workflow orchestration and feedback design than by the underlying model itself. These results suggest that repair budgets should be treated as an explicit experimental variable, as they directly affect evaluation outcomes, computational cost, runtime, and reproducibility in LLM-based software engineering research.",Tobias Kiecker; Eik Reichmann; Hosung Kang; Gabin An; Lars Grunske,2026-07-06,2026,arXiv,arXiv,,"4 Pages (+1 for references), NIER Paper",cs.SE,arxiv-api,,,,,,,2607.05197,2026-07-17T05:43:07+00:00
-ale-0458,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,"DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks",https://arxiv.org/abs/2607.07946,external,arxiv.org,"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: 113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","Gives readers a concrete source in DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks when they need to measure progress and gate completion with repeatable evidence.",Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,987,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L987,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.07946,"[2607.07946] DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks","DeepSWE is a benchmark of 113 original, long-horizon software engineering tasks for evaluating coding agents. Most public agentic coding benchmarks follow SWE-bench in mining merged fixes from public GitHub repositories, which creates two problems: the fixes and their discussion were likely seen during pretraining, so a high score can reflect recall rather than problem-solving; and each task is graded by the tests that shipped with its merged fix, which were written to confirm one specific fix rather than grade an arbitrary solution, so they can fail a correct alternative or pass an incomplete one. DeepSWE avoids both. Its tasks are written from scratch across 91 active open-source repositories and five languages and are never contributed back upstream, so their reference solutions stay out of the public record that model training scrapes; and each task is graded by a hand-written verifier that checks the requested functionality and accepts any implementation that provides it. When an independent LLM judge re-reviews graded runs, it disagrees with DeepSWE's verifier about an order of magnitude less often than with SWE-Bench Pro's inherited tests (1.4% versus 32.4%). Despite being about half the length of SWE-Bench Pro's prompts, DeepSWE's prompts describe tasks whose reference solutions touch 5.5x more code, and the benchmark separates frontier agents across a wider score band than the leaderboards on which they otherwise cluster. We release the benchmark, its verifiers, and the full record of evaluation trajectories.",Wenqi Huang; Charley Lee; Leonard Tng; Serena Ge,2026-07-08,2026,arXiv,arXiv,,"32 pages, 10 figures. Code and data: https://github.com/datacurve-ai/deep-swe ; https://deepswe.datacurve.ai/",cs.SE,arxiv-api,,,,,,,2607.07946,2026-07-17T05:43:07+00:00
-ale-0459,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization,https://arxiv.org/abs/2607.07744,external,arxiv.org,"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.",Gives readers a concrete source in PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,988,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L988,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.07744,[2607.07744] PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization,"Coding-agent benchmarks have largely measured whether agents can produce functionally correct patches, but production software also demands measurable speedups on real execution targets. Performance optimization is a distinct agentic task: agents must profile executions, diagnose cross-layer bottlenecks, edit code without breaking correctness, and verify that gains are reproducible rather than measurement artifacts. We introduce PERFOPT-Bench, a benchmark for evaluating this full performance-engineering loop. Each task provides a correct but deliberately suboptimal codebase and asks the agent to improve a target performance metric; scoring requires hidden correctness tests, verified-speedup measurement, and trajectory-level audit. We evaluate 7 agent stacks with different LLMs and agent frameworks on 7 long-horizon optimization tasks. The results show that optimization performance is workload-dependent rather than determined by model identity alone: no single stack dominates, and changing the agent framework can materially change the same LLM's per-task speedup profile. We further find that raw speedup is unsafe as a benchmark score, since some large gains arise from benchmark-specific shortcut exploitation; an exploratory relay pilot suggests that restarting from an externalized optimization summary can recover additional headroom after an initial session stops. The benchmark and our evaluation are available at: https://anonymous.4open.science/r/Dataset-D3CC.",Yingyun Cui; Yi Xie; Piaohong Wang; Jiawei Ma; Bo Liu; Liangliang Cao,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07744,2026-07-17T05:43:07+00:00
-ale-0460,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Benchmarking coding agents on Databricks' multi-million line codebase,https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase,external,www.databricks.com,"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.",Gives readers a concrete source in Benchmarking coding agents on Databricks' multi-million line codebase when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,989,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L989,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification;budget,researcher;evaluator,benchmark,ok,https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase,Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase | Databricks Blog,"Databricks shares results from its internal coding benchmark, evaluating coding agents on a multi-million line codebase to optimize engineering cost and performance.",,2026,2026,,Databricks,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0461,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks,https://arxiv.org/abs/2607.08768,external,arxiv.org,"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.",Gives readers a concrete source in UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,990,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L990,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;state;escalation,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.08768,[2607.08768] UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks,"The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they often rely on sandboxed environments and single-turn evaluation paradigms. Moreover, their scenario-based task taxonomies mix multiple model capabilities within the same task category, making it difficult to identify the root causes of agent failures. To address these limitations, we introduce UniClawBench, the first capability-driven benchmark designed to evaluate proactive agents in dynamic, real-world settings. UniClawBench is built around five foundational model capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination. Based on these capabilities, we design 400 bilingual real-world tasks. Unlike previous benchmarks that rely on static, pre-recorded answers, our benchmark evaluates agents in live Docker containers using fine-grained, step-by-step completion checkpoints. Furthermore, we design a closed-loop evaluation strategy comprising an executor agent, a hidden supervisor agent, and a user agent to simulate realistic multi-turn human feedback without leaking grading criteria. To disentangle base model capabilities from framework-level design choices, we evaluate state-of-the-art models under multiple agent frameworks. Through comprehensive comparisons across both models and frameworks, we show how base model capabilities and agent framework designs jointly shape performance in real-world environments. To facilitate future research, we make our benchmark and code publicly available at https://github.com/HKU-MMLab/UniClawBench.",Zhekai Chen; Chengqi Duan; Kaiyue Sun; Bohao Li; Yuqing Wang; Manyuan Zhang; Xihui Liu,2026-07-09,2026,arXiv,arXiv,,Project Page: https://uniclawbench.github.io | GitHub Repo: https://github.com/HKU-MMLab/UniClawBench,cs.CL,arxiv-api,,,,,,,2607.08768,2026-07-17T05:43:07+00:00
-ale-0462,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills,https://arxiv.org/abs/2607.09016,external,arxiv.org,"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.",Gives readers a concrete source in SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,991,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L991,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.09016,[2607.09016] SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills,"Agent skills extend LLM agents with reusable procedures, tools, and domain-specific workflows, but their safety depends on resolving dependencies among interacting instructions. We introduce SkillLogic, a framework for analyzing logical relations in skill files and constructing executable tests from them. Our taxonomy covers eight relation types, including preconditions that gate valid actions, constraints that limit how allowed actions may be performed, and fallbacks that specify recovery behavior after failure. Using SkillLogic, we scan over 5000 public skills and find that 70% contain at least one logical relation. We then construct SLBench, an 86-case executable benchmark from high-confidence, high-impact, and locally testable relations. Evaluating Codex and Claude Code across six LLM backbones shows unsafe rates up to 70%, with violations leading to privacy leaks, unsafe configuration changes, and incomplete cleanup. The human audit attributes failures to both agent capability gaps and low-salience skill text. We further show that SLGuard, a lightweight inference-time scaffold, reduces violations by 63% on targeted cases. Our results establish logical-relation following as a distinct reliability challenge for skill-guided agents.",Xuan Chen; Chengpeng Wang; Lu Yan; Xiangyu Zhang,2026-07-10,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.09016,2026-07-17T05:43:07+00:00
-ale-0463,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution,https://arxiv.org/abs/2603.13428,external,arxiv.org,"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.",Gives readers a concrete source in SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,992,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L992,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.13428,[2603.13428] SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution,"Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-running systems, are now entrusted to drive this evolution. Yet, existing benchmarks evaluate agents on isolated, one-off coding tasks, neglecting the temporal dependencies and technical debt inherent in real-world software evolution. To bridge this gap, we introduce DeepCommit, an agentic pipeline that reconstructs verifiable Milestone DAGs from noisy commit logs, where milestones are defined as functionally cohesive development goals. These executable sequences enable SWE-Milestone, a benchmark that evaluates agents on streams of milestone-level tasks, requiring them to sustain system integrity and limit error accumulation, dimensions of long-term software evolution largely missing from current benchmarks. Our evaluation of 12 frontier models across 4 agent frameworks reveals a critical vulnerability: overall performance scores drop significantly from >80% on isolated tasks to at most 38% in continuous settings, exposing agents' profound struggle with long-term maintenance and error propagation.",Gangda Deng; Zhaoling Chen; Zhongming Yu; Haoyang Fan; Yuhong Liu; Yuxin Yang; Dhruv Parikh; Rajgopal Kannan; Le Cong; Mengdi Wang; Qian Zhang; Viktor Prasanna; Xiangru Tang; Xingyao Wang,2026-03-13,2026,arXiv,arXiv,,ICML 2026,cs.SE,arxiv-api,,,,,,,2603.13428,2026-07-17T05:43:07+00:00
-ale-0464,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,AgentAbstain: Do LLM Agents Know When Not to Act?,https://arxiv.org/abs/2607.10059,external,arxiv.org,"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.",Gives readers a concrete source in AgentAbstain: Do LLM Agents Know When Not to Act? when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,993,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L993,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.10059,[2607.10059] AgentAbstain: Do LLM Agents Know When Not to Act?,"Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain. This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions. To close this gap, we present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act. At its core, AgentAbstain is a paired-task benchmark built on an agent-native taxonomy of 8 abstention scenarios across pre-execution reasoning and runtime discovery. It contains 263 paired tasks across 42 executable sandbox environments, where each pair consists of a should-act task and a should-abstain variant produced through a controlled perturbation to the instruction, tool, or environment state. To scale this paired design and resist data contamination, we propose AbstainGen, a fully automated pipeline that synthesizes sandbox environments and generates paired tasks end-to-end, validated by deterministic replay and semantic LLM judges; fresh task instances can be regenerated on demand, and three independent annotators rate 94-98% of sampled tasks as well-designed. Across 17 frontier LLMs in 4 agent harnesses, the best agent (Gemini 3.1 Pro) achieves only 59.5% paired accuracy (correct on both the act and abstain sides of each paired task). More importantly, abstention capability is largely independent of general task-solving capability, indicating that scaling task-solving alone will not close this gap. We further identify failure modes such as post-hoc abstention, in which agents execute irreversible actions before recognizing abstention triggers. Our code and dataset are open-sourced at agentabstain.github.io.",Xun Liu; Yi Evie Zhang; Vira Kasprova; Parisa Rabbani; Pardis Sadat Zahraei; Tianyu Zhang; Ali Ebrahimpour-Boroojeny; Varun Chandrasekaran,2026-07-11,2026,arXiv,arXiv,,"56 pages, 13 figures",cs.AI,arxiv-api,,,,,,,2607.10059,2026-07-17T05:43:07+00:00
-ale-0465,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,"Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy",https://arxiv.org/abs/2607.10526,external,arxiv.org,"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Gives readers a concrete source in Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy when they need to measure progress and gate completion with repeatable evidence.",Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,994,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L994,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.10526,"[2607.10526] Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Stateful Personal Agents","Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills. This persistence turns conversational sycophancy into a state-writing failure: accepted user-centric claims can be committed as lasting preferences, background facts, or workflows and later reused after the original conversation is gone. We call this persistent sycophancy and introduce the Personal Agent Sycophancy Benchmark (PASB), a 1,600-task benchmark that traces whether a conversational claim is accepted, written into durable agent state, and reused in a later neutral query. Unlike prior benchmarks that provide pre-written memories, PASB evaluates real agents (Hermes-Agent and OpenClaw) that decide what to store. It isolates the write process by combining four scenario framings with four temporal delivery patterns and separating a five-turn persist stage from a cleared three-turn query stage, ensuring downstream effects arise only from durable state. Across twelve models, the commit boundary is the key inflection point: downstream failure increases from 45.0% in session-only episodes to 71.9% after commitment, a consistent increase of 27.0 percentage points. Committed claims exhibit three write-time patterns: status promotion, attribution removal, and scope broadening. These patterns become stronger under memory-like or procedural framing, repeated reinforcement, and even across domain boundaries. These results show that agent sycophancy is fundamentally a state-writing governance problem. Once user content is committed to durable memory, safety must govern what agents write, not only what they say. PASB identifies the write-time controls needed to gate risky commits while preserving the source, role, and scope of stored content beyond response-level mitigations.",Xutao Mao; Liangjie Zhao; Leyao Wang; Rui Qian; Qiang Huang; Wentao Wang; Bo Han; Xiang Zheng; Cong Wang,2026-07-12,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.10526,2026-07-17T05:43:07+00:00
-ale-0466,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Set-shifting Behavioral Test for Harnessed Agents,https://arxiv.org/abs/2607.13396,external,arxiv.org,"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.",Gives readers a concrete source in Set-shifting Behavioral Test for Harnessed Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,995,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L995,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.13396,[2607.13396] Set-shifting Behavioral Test for Harnessed Agents,"What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmark mounts tool-skill libraries with redundancies, where many tools solve the same task but differ in hidden reliability. In our evaluation framework, a branched schedule shifts the reliable tool group at hidden boundaries and pairs every shift with a no-shift control. We find that agents, by default, settle on a small recurring routine within a few turns of each boundary, with call shares concentrating on a few discrete values after each reliability shift. We score the set-shifting accuracy for each agent trajectory: the joint probability of routing to the target tool group in every post-shift window. We test open-weight LLMs in an open-source agentic harness and find qualitatively distinct failure modes across the same set of routines. We also find that set framing, how the toolset presents the alternatives as competing or complementary, shifts the routing dynamics.",Ziwei Ye,2026-07-15,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.13396,2026-07-17T05:43:07+00:00
-ale-0467,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,https://arxiv.org/abs/2607.14642,external,arxiv.org,"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.",Gives readers a concrete source in MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,996,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L996,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.14642,[2607.14642] MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,"As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities. However, these benchmarks overlook the continuous evolution of tool interfaces and functionalities within MCP servers, resulting in flawed assessments that fail to capture the agent's adaptability in changing tool landscapes. To bridge this gap, we introduce \textbf{MCPEvol-Bench}, a novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution. Inspired by large-scale empirical study, we propose 11 mutation operators to simulate realistic tool evolution within 123 MCP servers. We benchmark 12 state-of-the-art LLMs on multiple versions of MCP servers, revealing that even frontier models struggle to adapt to evolving tools. For instance, GPT-5.4 and Claude-Sonnet-4-6 exhibit performance declines of 13.7\% and 14.4\% in evolved MCP servers, respectively, accompanied by substantial increases in planning and reasoning errors. These findings highlight the vulnerability of LLM-driven workflows, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.",Huanxi Liu; Kun Hu; Jiaqi Liao; Qiang Wang; Pengfei Qian; YuanZhao Zhai; Dawei Feng; Bo Ding; Huaimin Wang,2026-07-16,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.14642,2026-07-17T05:43:07+00:00
-ale-0468,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,https://arxiv.org/abs/2607.15205,external,arxiv.org,"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","The work turns loop quality into a measurable task or score. Resource-specific angle: Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.",Gives readers a concrete source in MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,997,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L997,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.15205,[2607.15205] MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,"Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated mostly as a text-only task. Existing multimodal SE benchmarks evaluate end-to-end repair, entangling localization with patch synthesis and obscuring whether visual input helped, hurt, or was ignored. We introduce \textbf{MM-IssueLoc}, a controlled benchmark and evaluation protocol for repository-level localization with visual evidence. MM-IssueLoc contains 652 issue-PR instances across 23 languages, with annotations for 7 image categories and 4 relevance levels. It provides file-level and function-level gold labels, paired text-only and with-image evaluation, and VCE-based diagnostics that convert images into structured textual evidence. We evaluate LLM-based and retrieval-based systems, including MM-IssueLoc-VL-Emb as a controlled multimodal retriever. Results show that existing systems remain far from reliable multimodal repository localization: the strongest agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, while the strongest retriever reaches 33.86 function Acc@10. Cross-benchmark comparisons show that high localization scores on text-dominant SWE benchmarks do not transfer cleanly to multimodal issue localization. MM-IssueLoc turns visual evidence into an explicit evaluation variable, enabling future work to test whether systems improve by using visual evidence for localization, rather than by relying on text-only cues or downstream patch-generation effects.",Shaoxiong Zhan; Shi Hu; Boyu Feng; Hai Lin; Andrew Gong; Zhengda Zhou; Jiaying Zhou; Yunyun Hou; Hao Su; Hai-Tao Zheng,2026-07-16,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.15205,2026-07-17T05:43:07+00:00
-ale-0469,Operations Playbooks,operations-playbooks,Blog,📝,Agentic Engineering: The Agent Loop,https://junpingyi.com/books/agentic-engineering/agent-loop/,external,junpingyi.com,Minimal mental model for the loop underlying agent operation.,Minimal mental model for the loop underlying agent operation.,Minimal mental model for the loop underlying agent operation.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Minimal mental model for the loop underlying agent operation.,Gives readers a concrete source in Agentic Engineering: The Agent Loop when they need to bound risk before recurring or unattended execution.,"Contextual source from junpingyi.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1005,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1005,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://junpingyi.com/books/agentic-engineering/agent-loop/,Chapter 1: The Agent Loop — Agentic Engineering: How to Build AI Agents Like Claude Code,Chapter 1: The Agent Loop from Agentic Engineering: How to Build AI Agents Like Claude Code,,,,,junpingyi.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0470,Operations Playbooks,operations-playbooks,Blog,📝,"The agent loop: ReAct, plan-and-execute, reflection",https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection,external,www.kunwar.page,Practical walkthrough of the base loop and common variants.,Practical walkthrough of the base loop and common variants.,Practical walkthrough of the base loop and common variants.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Practical walkthrough of the base loop and common variants.,"Gives readers a concrete source in The agent loop: ReAct, plan-and-execute, reflection when they need to bound risk before recurring or unattended execution.","Contextual source from www.kunwar.page; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1006,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1006,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection,"Chapter 67: The agent loop: ReAct, plan-and-execute, reflection — The Holy Grail Basic agent loop: generate, check for tool calls, execute tools and loop back, or return final answer on no tool call. ReAct interleaves Thought, Action, and Observation triplets; each Thought improves the next Action choice by externalizing reasoning. Agent cost vs single-shot: one LLM call versus 5-12 interleaved LLM and tool calls, showing the 10x cost and latency multiplier.",An agent is a loop of `model.generate()` calls with tool calls in between. The loop is the entire pattern,,,,,kunwar.page,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0471,Operations Playbooks,operations-playbooks,Blog,📝,How to Build an Agent,https://ampcode.com/how-to-build-an-agent,external,ampcode.com,"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.",Gives readers a concrete source in How to Build an Agent when they need to bound risk before recurring or unattended execution.,"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1007,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1007,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,operator;security,practitioner-analysis,ok,https://ampcode.com/notes/how-to-build-an-agent,Amp,"Building a fully functional, code-editing agent in less than 400 lines.",,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0472,Operations Playbooks,operations-playbooks,Blog,📝,Agentic Coding Recommendations,https://lucumr.pocoo.org/2025/6/12/agentic-coding/,external,lucumr.pocoo.org,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Gives readers a concrete source in Agentic Coding Recommendations when they need to bound risk before recurring or unattended execution.,"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1008,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1008,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://lucumr.pocoo.org/2025/6/12/agentic-coding/,Agentic Coding Recommendations | Armin Ronacher's Thoughts and Writings,Current recommendations of agentic coding.,,2025-06-12,2025,,Armin Ronacher's Thoughts and Writings,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0473,Operations Playbooks,operations-playbooks,Blog,📝,Coding Agents 101: The Art of Actually Getting Things Done,https://devin.ai/agents101,external,devin.ai,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Gives readers a concrete source in Coding Agents 101: The Art of Actually Getting Things Done when they need to bound risk before recurring or unattended execution.,"Contextual source from devin.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1009,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1009,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;exit,operator;security,practitioner-analysis,ok,https://devin.ai/agents101,Coding Agents 101: The Art of Actually Getting Things Done,Coding Agents 101: The Art of Actually Getting Things Done,,,,,devin.ai,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0474,Operations Playbooks,operations-playbooks,Blog,📝,How Anthropic teams use Claude Code,https://claude.com/blog/how-anthropic-teams-use-claude-code,external,claude.com,"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Cross-team field report of real recurring agent workflows in engineering, security, and data science.",Gives readers a concrete source in How Anthropic teams use Claude Code when they need to bound risk before recurring or unattended execution.,"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1010,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1010,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://claude.com/blog/how-anthropic-teams-use-claude-code,How Anthropic teams use Claude Code | Claude by Anthropic,Teams across Anthropic use Claude Code for everything from debugging production issues and navigating unfamiliar codebases to building custom automation tools. Here's how. ‍,,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0475,Operations Playbooks,operations-playbooks,Blog,📝,How Boris Uses Claude Code,https://howborisusesclaudecode.com/,external,howborisusesclaudecode.com,"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.",Gives readers a concrete source in How Boris Uses Claude Code when they need to bound risk before recurring or unattended execution.,"Contextual source from howborisusesclaudecode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1011,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1011,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;trigger;workspace;exit,operator;security,practitioner-analysis,ok,https://howborisusesclaudecode.com/,Boris Cherny's Claude Code Tips — How He Actually Uses It (121+ Tips),"121+ tips from Boris Cherny, creator of Claude Code, on his daily workflow: CLAUDE.md, worktrees, plan mode, hooks, subagents, and more.",@CarolinaCherry,,,,How Boris Uses Claude Code,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0476,Operations Playbooks,operations-playbooks,Blog,📝,Agent of the Day: Copilot Agent PR Analysis,https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/,external,github.github.com,"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.",Gives readers a concrete source in Agent of the Day: Copilot Agent PR Analysis when they need to bound risk before recurring or unattended execution.,"Contextual source from github.github.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1012,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1012,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake,operator;security,practitioner-analysis,ok,https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/,"Agent of the Day – May 26, 2026 | GitHub Agentic Workflows",Copilot Agent PR Analysis: a daily workflow that monitors GitHub Copilot coding agent performance across pull requests,,,2026,,GitHub Agentic Workflows,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0477,Operations Playbooks,operations-playbooks,Paper,📄,"Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows",https://arxiv.org/abs/2607.07052,external,arxiv.org,"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Gives readers a concrete source in Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.07052; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1013,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1013,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07052,"[2607.07052] Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production","AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.",Arun Malik,2026-07-08,2026,arXiv,arXiv,,"Conference-style paper; 10 pages (estimated from manuscript formatting if applicable); focuses on agentic AI, AIOps, workflow automation, deterministic execution, and LLM cost optimization",cs.SE,arxiv-api,,,,,,,2607.07052,2026-07-17T05:43:07+00:00
-ale-0478,Operations Playbooks,operations-playbooks,Paper,📄,Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,https://arxiv.org/abs/2607.08010,external,arxiv.org,"Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.",Gives readers a concrete source in Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08010; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1014,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1014,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08010,[2607.08010] Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,"Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment. The tool-maker grounds synthesis in the live environment as it collects execution traces, observes backend schemas and values, generates candidate tools, and repairs them against labeled cases. At runtime, the production agent calls these tools directly and falls back to code generation only when needed. We deploy the approach in a Fulfillment Center alarm-triage system, where an agent diagnoses alarms against a 44-node SOP over heterogeneous metric backends. In production, tool calls reduce p50 latency by 42%. On 1,500 historical alarms, they reduce end-to-end error rate by up to 53% by suppressing run-to-run variance in repeated steps. Because tools return compact structured verdicts, they also enable a simpler direct-call architecture, reducing p50 latency by a further 62% in a controlled ablation. Versioned tools also improve auditability and expose specification gaps and upstream data drift. Our results show that self-evolving agents can make industrial LLM systems faster, more reliable, and easier to operate.",Kalle Kujanpää; Ning Liu; Shahnawaz Alam; Yeshwanth Reddy Sura; Tianyu Yang; Kristina Klinkner; Shervin Malmasi,2026-07-09,2026,arXiv,arXiv,,Preprint,cs.CL,arxiv-api,,,,,,,2607.08010,2026-07-17T05:43:07+00:00
-ale-0479,Operations Playbooks,operations-playbooks,Blog,📝,AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines,https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines,external,www.sabrina.dev,"Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.",Gives readers a concrete source in AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines when they need to bound risk before recurring or unattended execution.,"Contextual source from www.sabrina.dev; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1015,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1015,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;verification;state,operator;security,practitioner-analysis,ok,https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines,AI Loop Engineering: Build Autonomous Agents with Claude Code /goal + Routines,"What loop engineering means in 2026, how to use the Claude Code /goal command, and how to build your first autonomous AI agent with a routine.",Sabrina Ramonov 🍄,,,,sabrina.dev,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0480,Operations Playbooks,operations-playbooks,Paper,📄,Agent Delivery Engineering Predictive Reliability Framework,https://arxiv.org/abs/2607.07689,external,arxiv.org,"Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.",Gives readers a concrete source in Agent Delivery Engineering Predictive Reliability Framework when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07689; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1016,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1016,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07689,[2607.07689] Agent Delivery Engineering Predictive Reliability Framework,"Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory prediction from passive degradation detection. ADE-PRF aggregates 20 heterogeneous signals across five layers into a Trust Margin (TM) metric (39.2-point dynamic range). Triple-method parallel prediction enables 8-hour forecasts: the Exponential method achieves MAE=1.228, Direction Accuracy=76.8%, with 99.65% within +/-10-point tolerance. Production validation spans 380,227 predictions and 280,579 validations across six agent profiles over 15 continuous days, plus seven sandbox-controlled experiments. Key findings include detection of ""false prosperity"" -- degradation concealed by normal surface metrics -- and immediate TM coupling with ground-truth states upon ADE plugin integration, with 16/20 factors relying on ADE-collected data. Exponential consistently outperforms Kalman. ADE-PRF provides among the earliest reliability quantification with forward-looking warnings for production LLM agents.",Dexing Liu,2026-07-08,2026,arXiv,arXiv,,"117pages,83figures",cs.MA,arxiv-api,,,,,,,2607.07689,2026-07-17T05:43:07+00:00
-ale-0481,Operations Playbooks,operations-playbooks,Tool,🧰,rocketplaneIO,https://github.com/olemeyer/rocketplaneIO,external,github.com,"Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.",Gives readers a concrete source in rocketplaneIO when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (141 stars; 3 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1017,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1017,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,builder;operator;security,source-implementation,ok,https://github.com/olemeyer/rocketplaneIO,"GitHub - olemeyer/rocketplaneIO: Self-hosted AI SRE for Kubernetes — zero-instrumentation eBPF observability plus a copilot that fixes issues through guardrailed, self-verifying actions. BYO-LLM, air-gapped capable. · GitHub","Self-hosted AI SRE for Kubernetes — zero-instrumentation eBPF observability plus a copilot that fixes issues through guardrailed, self-verifying actions. BYO-LLM, air-gapped capable. - olemeyer/rocketplaneIO",,2026-07-06,2026,olemeyer/rocketplaneIO,GitHub,,,,github-api,olemeyer/rocketplaneIO,141,3,Apache-2.0,2026-07-06T11:24:01Z,2026-07-15T11:30:27Z,,2026-07-17T05:43:07+00:00
-ale-0482,Operations Playbooks,operations-playbooks,Blog,📝,Migrating a Production AI Agent to GPT-5.6,https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6,external,ploy.ai,"Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.",Gives readers a concrete source in Migrating a Production AI Agent to GPT-5.6 when they need to bound risk before recurring or unattended execution.,"Contextual source from ploy.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1018,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1018,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;state;exit,operator;security,practitioner-analysis,ok,https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6,Migrating a production AI agent to GPT-5.6 | Ploy,"For four months, no frontier model beat Claude Opus in our production evals. GPT-5.6 did. This is what we learned while migrating.",,,,,Ploy,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0483,Operations Playbooks,operations-playbooks,Paper,📄,Coding-agents can replicate scientific machine learning papers,https://arxiv.org/abs/2607.02134,external,arxiv.org,"Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.",Gives readers a concrete source in Coding-agents can replicate scientific machine learning papers when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.02134; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1019,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1019,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.02134,[2607.02134] Coding-agents can replicate scientific machine learning papers,"Scientific machine learning papers typically make computational claims, e.g., that the relative mean square error is less than 5% or that the 95% predictive credible interval covers the test data. A coding agent can be prompted to replicate those claims from paper materials alone, but the prompt does not by itself reliably preserve progress or check whether generated evidence supports the paper's claims. We introduce Paper-replication, a workflow that makes each selected paper claim a target with recorded evidence, and implement it as a coding-agent skill. The workflow makes the agent record those targets, reconstruct the paper's method, run computational experiments, link generated outputs to provenance and comparisons with the paper's claims, record where matched evidence appears in the replication report, and pass validation checks before completion. We evaluate Paper-replication on twelve independent runs across four scientific machine learning papers. All twelve workspaces pass the completion gate, and all 158 recorded targets are matched with report coverage. Even in this completed workspace state, repeated runs differ in how papers are divided into targets, in numerical fidelity to the source papers, in elapsed replication time, in the number of intermediate executions replaced before final evidence is accepted, and in the rules used to accept evidence. Paper-replication makes completion depend on workspace evidence and validation checks rather than on the agent's final message.",Atharva Hans; Ilias Bilionis,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02134,2026-07-17T05:43:07+00:00
-ale-0484,Templates And Patterns,templates-and-patterns,Template,🧾,Resource entry template,templates/resource-entry.md,local_path,,Format for adding a single resource with evidence quality and category fit.,Format for adding a single resource with evidence quality and category fit.,Format for adding a single resource with evidence quality and category fit.,The resource is directly reusable as a starting artifact. Resource-specific angle: Format for adding a single resource with evidence quality and category fit.,Gives readers a concrete source in Resource entry template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1027,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1027,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/resource-entry.md,Resource entry template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0485,Templates And Patterns,templates-and-patterns,Template,🧾,Loop pattern template,templates/loop-pattern.md,local_path,,"Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","The resource is directly reusable as a starting artifact. Resource-specific angle: Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.",Gives readers a concrete source in Loop pattern template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1028,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1028,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/loop-pattern.md,Loop pattern template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0486,Templates And Patterns,templates-and-patterns,Template,🧾,Loop contract schema,schemas/loop-contract.schema.json,local_path,,Machine-readable schema for portable loop specs.,Machine-readable schema for portable loop specs.,Machine-readable schema for portable loop specs.,The contribution is machine-readable and validation-friendly. Resource-specific angle: Machine-readable schema for portable loop specs.,Gives readers a concrete source in Loop contract schema when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1029,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1029,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/schemas/loop-contract.schema.json,Loop contract schema,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0487,Templates And Patterns,templates-and-patterns,Template,🧾,Loop contract preview script,scripts/preview_loop_contract.py,local_path,,Dependency-free demo that validates and renders a loop contract JSON file.,Dependency-free demo that validates and renders a loop contract JSON file.,Dependency-free demo that validates and renders a loop contract JSON file.,The contribution is machine-readable and validation-friendly. Resource-specific angle: Dependency-free demo that validates and renders a loop contract JSON file.,Gives readers a concrete source in Loop contract preview script when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1030,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1030,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/scripts/preview_loop_contract.py,Loop contract preview script,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0488,Templates And Patterns,templates-and-patterns,Template,🧾,Translation guide,TRANSLATIONS.md,local_path,,How to add or maintain a language translation without drifting from the canonical English list.,How to add or maintain a language translation without drifting from the canonical English list.,How to add or maintain a language translation without drifting from the canonical English list.,The resource is directly reusable as a starting artifact. Resource-specific angle: How to add or maintain a language translation without drifting from the canonical English list.,Gives readers a concrete source in Translation guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1031,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1031,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/TRANSLATIONS.md,Translation guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0489,Templates And Patterns,templates-and-patterns,Template,🧾,Pattern library index,patterns/README.md,local_path,,"Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.",Gives readers a concrete source in Pattern library index when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1032,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1032,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;verification;state;budget;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/README.md,Pattern library index,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0490,Examples And Schema,examples-and-schema,Pattern,🔁,Example loop specs,examples/README.md,local_path,,"Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.",Gives readers a concrete source in Example loop specs when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1042,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1042,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md,Example loop specs,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0491,Examples And Schema,examples-and-schema,Template,🧾,Loop contract library,examples/README.md#contract-library,local_path,,"Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","The contribution is machine-readable and validation-friendly. Resource-specific angle: Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.",Gives readers a concrete source in Loop contract library when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1043,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1043,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md#contract-library,Loop contract library,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0492,Examples And Schema,examples-and-schema,Template,🧾,Runnable test-repair loop,examples/runnable/test-repair-loop.sh,local_path,,"Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.",Gives readers a concrete source in Runnable test-repair loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1044,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1044,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",verification;budget;escalation;exit,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/runnable/test-repair-loop.sh,Runnable test-repair loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0493,Examples And Schema,examples-and-schema,Template,🧾,Runnable loop guide,examples/runnable/README.md,local_path,,"Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","The resource is directly reusable as a starting artifact. Resource-specific angle: Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.",Gives readers a concrete source in Runnable loop guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1045,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1045,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/runnable/README.md,Runnable loop guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0494,Community Gallery,community-gallery,Template,🧾,Loop gallery guide,gallery/README.md,local_path,,Quality bar for contributed loop examples with receipts and lessons learned.,Quality bar for contributed loop examples with receipts and lessons learned.,Quality bar for contributed loop examples with receipts and lessons learned.,The resource is directly reusable as a starting artifact. Resource-specific angle: Quality bar for contributed loop examples with receipts and lessons learned.,Gives readers a concrete source in Loop gallery guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1061,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1061,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",state,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/README.md,Loop gallery guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0495,Community Gallery,community-gallery,Template,🧾,Loop gallery template,gallery/template.md,local_path,,"Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.",Gives readers a concrete source in Loop gallery template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1062,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1062,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;intake;verification;state;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/template.md,Loop gallery template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0496,Community Gallery,community-gallery,Pattern,🔁,PR babysitter reference loop,gallery/pr-babysitter-reference.md,local_path,,Reference gallery entry for keeping a pull request moving.,Reference gallery entry for keeping a pull request moving.,Reference gallery entry for keeping a pull request moving.,Turns loop adoption into shareable cases with enough structure to compare lessons learned. Resource-specific angle: Reference gallery entry for keeping a pull request moving.,Gives readers a concrete source in PR babysitter reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1063,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1063,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/pr-babysitter-reference.md,PR babysitter reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0497,Community Gallery,community-gallery,Pattern,🔁,CI repair reference loop,gallery/ci-repair-reference.md,local_path,,Reference gallery entry for turning failing CI into a verified patch or escalation.,Reference gallery entry for turning failing CI into a verified patch or escalation.,Reference gallery entry for turning failing CI into a verified patch or escalation.,Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reference gallery entry for turning failing CI into a verified patch or escalation.,Gives readers a concrete source in CI repair reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1064,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1064,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",verification;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/ci-repair-reference.md,CI repair reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0498,Community Gallery,community-gallery,Pattern,🔁,Docs drift reference loop,gallery/docs-drift-reference.md,local_path,,Reference gallery entry for recurring docs/code consistency checks.,Reference gallery entry for recurring docs/code consistency checks.,Reference gallery entry for recurring docs/code consistency checks.,Turns loop adoption into shareable cases with enough structure to compare lessons learned. Resource-specific angle: Reference gallery entry for recurring docs/code consistency checks.,Gives readers a concrete source in Docs drift reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1065,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1065,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/docs-drift-reference.md,Docs drift reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0499,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Most Developers Do Not Need Agent Loops Yet,https://alphasignalai.substack.com/p/most-developers-do-not-need-agent,external,alphasignalai.substack.com,"Useful caution against adopting loops before the task, signal, and economics justify them.","Useful caution against adopting loops before the task, signal, and economics justify them.","Useful caution against adopting loops before the task, signal, and economics justify them.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Useful caution against adopting loops before the task, signal, and economics justify them.",Gives readers a concrete source in Most Developers Do Not Need Agent Loops Yet when they need to bound risk before recurring or unattended execution.,"Contextual source from alphasignalai.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1073,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1073,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,operator;security,risk-analysis,ok,https://alphasignalai.substack.com/p/most-developers-do-not-need-agent,Most Developers Do Not Need Agent Loops Yet,"The patterns were documented in 2024. Here’s who it pays off for, and the four conditions that decide.",AlphaSignal AI,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0500,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Engineering Agentic Systems for Reliability,https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/,external,pruningmypothos.com,"Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.",Gives readers a concrete source in Engineering Agentic Systems for Reliability when they need to bound risk before recurring or unattended execution.,"Contextual source from pruningmypothos.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1074,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1074,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,operator;security,risk-analysis,ok,https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/,Engineering Agentic Systems for Reliability | Sans Serif Systems,"A practical reliability model for agentic systems built around governed steps, verification, escalation, and observability.",Shailesh Rawat,,,,Sans Serif Systems,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0501,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared",https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026,external,callsphere.ai,Compares self-correction patterns and their cost/failure tradeoffs.,Compares self-correction patterns and their cost/failure tradeoffs.,Compares self-correction patterns and their cost/failure tradeoffs.,"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Compares self-correction patterns and their cost/failure tradeoffs.","Gives readers a concrete source in Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared when they need to bound risk before recurring or unattended execution.","Contextual source from callsphere.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1075,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1075,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,operator;security,risk-analysis,ok,https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026,"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared | CallSphere Blog","Three self-correction patterns dominate 2026 agent design. Side-by-side analysis of where each one wins, where each one fails, and how to combine them.",CallSphere,2026-04-24,2026,,CallSphere,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0502,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,How to Build an AI Agent Harness: A 2026 Complete Guide,https://atlan.com/know/how-to-build-ai-agent-harness/,external,atlan.com,"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.",Gives readers a concrete source in How to Build an AI Agent Harness: A 2026 Complete Guide when they need to bound risk before recurring or unattended execution.,"Contextual source from atlan.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1076,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1076,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification,operator;security,risk-analysis,ok,https://atlan.com/know/how-to-build-ai-agent-harness/,How to Build an AI Agent Harness: Step-by-Step Tutorial (2026),"Most agent harnesses fail at the data layer, not the loop. Build one the right way in 10 steps, with code and a done test for each. Start at Step 0.",,,,,atlan.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0503,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Harness Engineering vs Prompt Engineering vs Context Engineering Explained,https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d,external,medium.com,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Gives readers a concrete source in Harness Engineering vs Prompt Engineering vs Context Engineering Explained when they need to bound risk before recurring or unattended execution.,"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1077,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1077,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context,operator;security,risk-analysis,restricted,https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d,,,,,,,Medium,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0504,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,https://arxiv.org/abs/2606.17799,external,arxiv.org,"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","The work turns loop quality into a measurable task or score. Resource-specific angle: Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.",Gives readers a concrete source in Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.17799; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1078,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1078,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.17799,[2606.17799] Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,"Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration. We argue that current coding benchmarks are misaligned with agentic software engineering. A coding agent in practice is not a model: it is a system harness -- a composite of models, harnesses, contexts, environments, and feedback signals, any one of which can move the benchmark score by margins comparable to those between adjacent model generations. We discuss three symptoms: (i) benchmark scores conflate the model with the rest of the harness; (ii) grading against a single reference solution penalises equally valid alternatives; and (iii) the absence of signal at the level of individual harness components makes the end-to-end system score difficult to iterate on.",Maria I. Gorinova; Macey Baker; Amy Heineike; Maksim Shaposhnikov; Rob Willoughby; Dru Knox,2026-06-16,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2606.17799,2026-07-17T05:43:07+00:00
-ale-0505,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Understanding the Challenges in Iterative Generative Optimization with LLMs,https://arxiv.org/abs/2603.23994,external,arxiv.org,"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.",Gives readers a concrete source in Understanding the Challenges in Iterative Generative Optimization with LLMs when they need to bound risk before recurring or unattended execution.,Research source arXiv:2603.23994; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1079,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1079,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2603.23994,[2603.23994] Understanding the Challenges in Iterative Generative Optimization with LLMs,"Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the ""right"" learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Different starting artifacts determine which solutions are reachable in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches do not monotonically improve generalization on BBEH. We conclude that the lack of a simple, universal way to set up learning loops across domains is a major hurdle for productionization and adoption. We provide practical guidance for making these choices.",Allen Nie; Xavier Daull; Zhiyi Kuang; Abhinav Akkiraju; Anish Chaudhuri; Max Piasevoli; Ryan Rong; YuCheng Yuan; Prerit Choudhary; Shannon Xiao; Rasool Fakoor; Adith Swaminathan; Ching-An Cheng,2026-03-25,2026,arXiv,arXiv,,"39 pages, 17 figures",cs.LG,arxiv-api,,,,,,,2603.23994,2026-07-17T05:43:07+00:00
-ale-0506,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Illusion of Multi-Agent Advantage,https://arxiv.org/abs/2606.13003,external,arxiv.org,"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.",Gives readers a concrete source in The Illusion of Multi-Agent Advantage when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.13003; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1080,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1080,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.13003,[2606.13003] The Illusion of Multi-Agent Advantage,"Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed decision-making. However, empirical support for this claim relies primarily on comparisons with SAS baselines using benchmarks that prioritize isolated reasoning tasks, which do not adequately assess these advantages. Focusing on automatically generated MAS that are designed for enhanced generalizability over manually-designed counterparts, we perform a rigorous, systematic evaluation against SAS, specifically Chain-of-Thought with Self-Consistency (CoT-SC). Across traditional reasoning datasets and tasks with interactive multi-step workflows (e.g., BrowseComp-Plus), we demonstrate that automatic MAS consistently underperform CoT-SC despite being up to 10x more expensive. To isolate these failures from limitations inherent to task structure, we introduce a diagnostic synthetic dataset tailored for MAS featuring explicit task decomposition, context separation and parallelization potential. We show that expert-architected MAS consistently outperforms automatically generated architectures in both raw performance and cost-efficiency on this dataset, demonstrating that existing evaluation frameworks mask critical architectural gaps and inefficiencies of complex MAS by failing to account for the marginal utility of increased computational cost. Critically, systematic deconstruction of the generated MAS architectures reveals that current automated design paradigms produce architectural bloat that prioritizes superficial complexity which does not translate into functional utility, exposing a fundamental misalignment with multi-agent principles.",Prathyusha Jwalapuram; Hehai Lin; Chuyuan Li; Fangkai Jiao; Sudong Wang; Yifei Ming; Zixuan Ke; Chengwei Qin; Giuseppe Carenini; Shafiq Joty,2026-06-11,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.13003,2026-07-17T05:43:07+00:00
-ale-0507,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,The Coming Loop,https://lucumr.pocoo.org/2026/6/23/the-coming-loop/,external,lucumr.pocoo.org,"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.",Gives readers a concrete source in The Coming Loop when they need to bound risk before recurring or unattended execution.,"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1081,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1081,,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation;exit,operator;security,risk-analysis,ok,https://lucumr.pocoo.org/2026/6/23/the-coming-loop/,The Coming Loop | Armin Ronacher's Thoughts and Writings,"Loops, harnesses, and why even loop skeptics may end up with them.",,2026-06-23,2026,,Armin Ronacher's Thoughts and Writings,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0508,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,"Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop",https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735,external,www.theregister.com,"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","Gives readers a concrete source in Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop when they need to bound risk before recurring or unattended execution.","Contextual source from www.theregister.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1082,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1082,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,operator;security,risk-analysis,ok,https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735,"Loop engineering, latest AI buzzword, still needs humans in the loop",Prompting less and automating more comes with a price,,2026-06-24,2026,,theregister,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0509,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents,https://arxiv.org/abs/2607.01641,external,arxiv.org,"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.",Gives readers a concrete source in When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.01641; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1083,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1083,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;delegation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.01641,[2607.01641] When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents,"LLM agents increasingly rely on iterative execution to solve tasks through planning, tool use, state updates, and agent collaboration. While this design enables flexible automation, it also creates a new class of failures: an agent may repeatedly execute model calls, tools, workflow transitions, or agent handoffs when the feedback path is not effectively bounded. We call this problem Infinite Agentic Loops (IALs). IALs are not ordinary programming loops; they arise from the interaction between agent logic, framework semantics, runtime observations, and termination mechanisms. Such failures can amplify a single request into long running model and tool execution, causing cost exhaustion, model denial of service, context growth, and repeated external side effects. We propose IAL-Scan, a static analysis tool for detecting IAL failures in real-world LLM agent projects. IAL-Scan abstracts heterogeneous agent code into a framework independent Agent IR, builds an Agentic Loop Dependence Graph (ALDG) to recover explicit and framework induced feedback paths, and checks whether these paths can repeatedly reach costly or state growing operations without an effective bound. We evaluate IAL-Scan on 6,549 LLM agent repositories. It reports 74 potential findings, among which manual review confirms 68 IAL failures across 47 projects, achieving 91.9% precision.",Xinyi Hou; Shenao Wang; Yanjie Zhao; Haoyu Wang,2026-07-02,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.01641,2026-07-17T05:43:07+00:00
-ale-0510,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents,https://arxiv.org/abs/2607.07436,external,arxiv.org,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.",Gives readers a concrete source in The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07436; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1084,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1084,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07436,[2607.07436] The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents,"A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased reward, which is false for the LLM judges that reference-free tasks force upon us. We show that a biased judge does not merely add noise; it \emph{silently switches off the curator}. We make this precise with a corrupted-reward analysis and, isolating the causal channel by injecting corruption on top of a deterministic reward, a behavioral study on a reference-free report-writing testbed with a code-generation cross-check. Symmetric noise leaves retirement intact, but \emph{false-pass} bias (failures slipping through as passes) disables contribution-based retirement past a sharp threshold that no amount of data can cross. Separating genuine retirement from cap-eviction churn shows this \emph{mechanism} failure is universal, holding across domains and failure rates and sparing only near-zero-false-pass, verifier-like graders. The downstream \emph{outcome}, though, is regime-dependent: eval quality degrades only where the same corruption also starves skill synthesis, and otherwise holds steady, so the disabled curator is \emph{silent}, surfacing in no aggregate metric. The contribution is a behavioral safety result, not a performance one. A cheap defect-injection audit then tells an operator, before deployment, which side of the threshold their judge occupies.",Xing Zhang; Yanwei Cui; Guanghui Wang; Ziyuan Li; Wei Qiu; Bing Zhu; Peiyang He,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07436,2026-07-17T05:43:07+00:00
-ale-0511,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows,https://arxiv.org/abs/2607.07504,external,arxiv.org,"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.",Gives readers a concrete source in Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07504; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1085,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1085,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07504,[2607.07504] Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows,"Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results. Reusable skill files are meant to avoid prompting from scratch by packaging guidance for a task family. Expert-written skills can encode high-quality guidance, but writing and maintaining them across many data-science task families creates a manual bottleneck. We ask whether LLM-generated skills offer a useful low-curation alternative: do they improve performance over the task prompt alone? We test this question across four lifecycle stages: data preparation, data extraction, statistical analysis, and reporting, using one generated skill per stage. We find no reliable improvement from full generated skills over No-Skill prompting. We then ask whether any part of the skill is useful by ablating different skill components. The main ablation covers 56 tasks, nine model configurations, and three providers, yielding 7,560 runs. Compared with prompting using the task alone, neither the full generated skill nor any ablated skill variant significantly improves performance; all p-values are at least 0.396, and the total spread across variants is only 1.2 pp. A supplemental token-matched control adds 1,512 runs and finds that Full skills perform similarly to task-irrelevant skill-formatted content. The results caution against using one LLM-generated skill per data-science workflow as a default single-shot prompting strategy.",Wei-Jung Huang,2026-07-08,2026,KDD 2026 Workshop on AI Data Scientist,arXiv,,KDD 2026 Workshop on AI Data Scientist,cs.AI,arxiv-api,,,,,,,2607.07504,2026-07-17T05:43:07+00:00
-ale-0512,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Verification Horizon: No Silver Bullet for Coding Agent Rewards,https://arxiv.org/abs/2606.26300,external,arxiv.org,"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.",Gives readers a concrete source in The Verification Horizon: No Silver Bullet for Coding Agent Rewards when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.26300; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1086,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1086,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.26300,[2606.26300] The Verification Horizon: No Silver Bullet for Coding Agent Rewards,"A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult -- reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent -- manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions -- scalability, faithfulness, and robustness -- and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world agent tasks, and an automated agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks. These experiences collectively point to a core observation: no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator.",Binghai Wang; Chenlong Zhang; Dayiheng Liu; Jiajun Zhang; Jiawei Chen; Mingze Li; Mouxiang Chen; Rongyao Fang; Siyuan Zhang; Xuwu Wang; Yuheng Jing; Zeyao Ma; Zeyu Cui,2026-06-24,2026,arXiv,arXiv,,Authors are listed alphabetically by their first names,cs.AI,arxiv-api,,,,,,,2606.26300,2026-07-17T05:43:07+00:00
-ale-0513,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Write Code Like a Human Will Maintain It,https://unstack.io/write-code-like-a-human-will-maintain-it,external,unstack.io,"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.",Gives readers a concrete source in Write Code Like a Human Will Maintain It when they need to bound risk before recurring or unattended execution.,"Contextual source from unstack.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1087,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1087,,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation,operator;security,risk-analysis,ok,https://unstack.io/write-code-like-a-human-will-maintain-it,Write code like a human will maintain it,"One of the best things about LLMs is that they'll write code for you, all day long. Who cares about DRY? You don't have to be the one updating the same long con...",,2026-07-10,2026,,Unstack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0514,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Blog,📝,Claude Code Sends 33k Tokens Before Reading the Prompt,https://systima.ai/blog/claude-code-vs-opencode-token-overhead,external,systima.ai,"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.",Gives readers a concrete source in Claude Code Sends 33k Tokens Before Reading the Prompt when they need to bound risk before recurring or unattended execution.,"Contextual source from systima.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1088,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1088,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;budget,operator;security,practitioner-analysis,ok,https://systima.ai/blog/claude-code-vs-opencode-token-overhead,Claude Code Sends 4.7x More Tokens Than OpenCode Before Reading Your Prompt | Systima Blog,"Claude Code vs OpenCode token overhead measured at the API boundary. Out-of-the-box baselines, instruction file weight, MCP schema tax, subagent multipliers, and cache-write behaviour.",Systima,2026-07-12,2026,,Systima,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0515,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Rethinking the Evaluation of Harness Evolution for Agents,https://arxiv.org/abs/2607.12227,external,arxiv.org,"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.",Gives readers a concrete source in Rethinking the Evaluation of Harness Evolution for Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.12227; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1089,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1089,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.12227,[2607.12227] Rethinking the Evaluation of Harness Evolution for Agents,"We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. To address these concerns, we conduct an extensive evaluation comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluate evolved harnesses on held-out tasks to assess whether the discovered improvements generalize. Experiments on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6 show that automatic harness evolution does not consistently outperform simple test-time scaling methods and exhibits limited generalization. Our results raise important questions about the effectiveness of automatic harness evolution and highlight the need for fairer evaluation protocols and benchmarks for automatic harness design. Our code is available at https://github.com/rethinking-harness-evolution.",Yike Wang; Huaisheng Zhu; Zhengyu Hu; Yige Yuan; Zhengyu Chen; Shakti Senthil; Hannaneh Hajishirzi; Yulia Tsvetkov; Pradeep Dasigi; Teng Xiao,2026-07-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.12227,2026-07-17T05:43:07+00:00
-ale-0516,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes,https://arxiv.org/abs/2607.13071,external,arxiv.org,"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.",Gives readers a concrete source in Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13071; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1090,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1090,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification;state;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13071,[2607.13071] Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes,"Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth. This paper documents a failure mode in Claude Code where partial standard output from timed-out commands (exit code 143) is recorded in compaction summaries as confirmed results, propagating false positives across sessions and model versions without re-verification. The underlying mechanism is a conflation of observation and persistence, where information that appeared in the terminal is treated as equivalent to information written to durable storage. This finding extends the analysis of LLM self-evaluation failures reported in prior work on non-determinism in LLM-as-judge grading by showing that agentic tools exhibit analogous reliability deficits when reporting on their own operational outcomes. The failure has direct implications for any workflow that relies on agentic session continuity for data processing, scientific computation, or multi-step automation.",Hiroki Tamba,2026-07-11,2026,arXiv,arXiv,,"8 pages, companion to arXiv:2606.26185",cs.SE,arxiv-api,,,,,,,2607.13071,2026-07-17T05:43:07+00:00
-ale-0517,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0,https://arxiv.org/abs/2607.14004,external,arxiv.org,"Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.",Gives readers a concrete source in Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14004; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1091,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1091,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14004,[2607.14004] Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0,"Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimization is applied recursively as new failures and new tasks appear over time. The central question this raises is whether optimizer-driven gains compound: after an agent has been optimized once, can it be optimized again on newly arrived tasks without eroding the gains the first round produced? We study this question with a two-phase continual-learning evaluation built from hard tasks in Terminal-Bench 2.0, comparing three approaches to agent-harness optimization (GEPA, Meta Harness, and RELAI's Verifiable Continual Learning, RELAI-VCL) under identical optimization budgets. All three methods improve over the baseline agent in the conventional, static, single-phase setting. However, once new tasks are introduced, the methods diverge sharply: GEPA's optimized agent transfers below the unoptimized baseline, Meta Harness transfers well but fails to improve further once given a second optimization budget, and RELAI-VCL is the only method that both transfers positively to unseen tasks and continues improving after those tasks are folded into the optimization objective, reaching the highest pass rate at every evaluated stage and the highest lifelong average pass rate overall (76.4% vs. 66.0% for GEPA, 64.6% for Meta Harness, and 58.7% for the baseline). Our key observation was that optimization gains compounded only when regression control was built into the optimization loop, providing an inductive bias against shortcut solutions that fail to generalize.",Wenxiao Wang; Priyatham Kattakinda; Soheil Feizi,2026-07-15,2026,arXiv,arXiv,,Technical Report by RELAI (relai.ai),cs.AI,arxiv-api,,,,,,,2607.14004,2026-07-17T05:43:07+00:00
-ale-0518,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Does Multi-Agent Debate Improve AI Feedback on Research Papers?,https://arxiv.org/abs/2607.14713,external,arxiv.org,"In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.",Gives readers a concrete source in Does Multi-Agent Debate Improve AI Feedback on Research Papers? when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14713; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1092,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1092,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;verification;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14713,[2607.14713] Does Multi-Agent Debate Improve AI Feedback on Research Papers?,"Probably not, at least for meta-analyses in economics. In a pre-registered, identity-masked, within-paper experiment, the authors of 44 meta-analyses ranked three AI reports on their own paper by usefulness for improving it: a single pass by a frontier model against two multi-agent debate tools we built and expected to win. All reports were held to a common length and template. The authors preferred the single pass, by 0.66 rank points over mad-research (95% CI 0.32 to 1.00) and 0.57 over paper-workshop (0.16 to 0.95), though paper-workshop spent roughly thirty times the tokens. Authors who recalled their journal referee report usually placed it first and never last; in a separate exercise, three AI judges almost always placed the real journal referee report last. Among the three AI reports, Gemini (the judge whose model family wrote none of the reports) would have ranked paper-workshop first in the authors' place, reversing the single-pass preference. The reversal warns against substituting an AI judge for the author. We measure perceived usefulness for finished papers; whether AI should referee papers is a separate question.",Tomas Havranek; Zuzana Irsova,2026-07-16,2026,arXiv,arXiv,,"29 pages, 1 figure, 6 tables. Pre-registered on OSF; data, code, judge prompts, and blinded reports in the replication package on Zenodo. Project page: https://meta-analysis.cz/debate",econ.GN,arxiv-api,,,,,,,2607.14713,2026-07-17T05:43:07+00:00
-ale-0519,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Harness Engineering by ai-boost,https://github.com/ai-boost/awesome-harness-engineering,external,github.com,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Comprehensive list for the agent harness layer that Loop Engineering builds on.,Gives readers a concrete source in Awesome Harness Engineering by ai-boost when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,104 stars; 333 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1100,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1100,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/ai-boost/awesome-harness-engineering,"GitHub - ai-boost/awesome-harness-engineering: Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. · GitHub","Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. - ai-boost/awesome-harness-engineering",,2026-03-29,2026,ai-boost/awesome-harness-engineering,GitHub,,,,github-api,ai-boost/awesome-harness-engineering,3104,333,NOASSERTION,2026-03-29T15:39:49Z,2026-07-17T04:40:20Z,,2026-07-17T05:43:07+00:00
-ale-0520,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Harness Engineering by walkinglabs,https://github.com/walkinglabs/awesome-harness-engineering,external,github.com,"High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.",Gives readers a concrete source in Awesome Harness Engineering by walkinglabs when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,635 stars; 294 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1101,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1101,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/walkinglabs/awesome-harness-engineering,GitHub - walkinglabs/awesome-harness-engineering: 🛠️ Awesome tools & guides for harness engineering. · GitHub,🛠️ Awesome tools & guides for harness engineering. - walkinglabs/awesome-harness-engineering,,2026-03-29,2026,walkinglabs/awesome-harness-engineering,GitHub,,,,github-api,walkinglabs/awesome-harness-engineering,3635,294,NOASSERTION,2026-03-29T11:29:37Z,2026-07-17T02:25:47Z,,2026-07-17T05:43:07+00:00
-ale-0521,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Agent Harness,https://github.com/AutoJunjie/awesome-agent-harness,external,github.com,"Curated tools and resources for environments, constraints, and feedback around coding agents.","Curated tools and resources for environments, constraints, and feedback around coding agents.","Curated tools and resources for environments, constraints, and feedback around coding agents.","Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Curated tools and resources for environments, constraints, and feedback around coding agents.",Gives readers a concrete source in Awesome Agent Harness when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (484 stars; 46 forks; updated 2026-07-14); popularity is context, not proof of reliability.",medium,README.md,1102,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1102,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",workspace,builder,curated-index,ok,https://github.com/AutoJunjie/awesome-agent-harness,GitHub - AutoJunjie/awesome-agent-harness · GitHub,Contribute to AutoJunjie/awesome-agent-harness development by creating an account on GitHub.,,2026-03-05,2026,AutoJunjie/awesome-agent-harness,GitHub,,,,github-api,AutoJunjie/awesome-agent-harness,484,46,,2026-03-05T13:19:10Z,2026-07-14T10:24:06Z,,2026-07-17T05:43:07+00:00
-ale-0522,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Context Engineering,https://github.com/Meirtz/Awesome-Context-Engineering,external,github.com,Survey-style list for context engineering across LLMs and agents.,Survey-style list for context engineering across LLMs and agents.,Survey-style list for context engineering across LLMs and agents.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Survey-style list for context engineering across LLMs and agents.,Gives readers a concrete source in Awesome Context Engineering when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,238 stars; 258 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1103,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1103,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context,builder,curated-index,ok,https://github.com/Meirtz/Awesome-Context-Engineering,"GitHub - Meirtz/Awesome-Context-Engineering: 🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. · GitHub","🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. - Meirtz/Awesome-Context-Engineering",,2025-07-02,2025,Meirtz/Awesome-Context-Engineering,GitHub,,,,github-api,Meirtz/Awesome-Context-Engineering,3238,258,MIT,2025-07-02T17:46:03Z,2026-07-17T01:18:38Z,,2026-07-17T05:43:07+00:00
-ale-0523,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Prompt Engineering,https://github.com/promptslab/Awesome-Prompt-Engineering,external,github.com,Classic adjacent list for prompt techniques and prompting resources.,Classic adjacent list for prompt techniques and prompting resources.,Classic adjacent list for prompt techniques and prompting resources.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Classic adjacent list for prompt techniques and prompting resources.,Gives readers a concrete source in Awesome Prompt Engineering when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (6,170 stars; 728 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,1104,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1104,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/promptslab/Awesome-Prompt-Engineering,"GitHub - promptslab/Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc · GitHub","This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc - GitHub - promptslab/Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc",,2023-02-09,2023,promptslab/Awesome-Prompt-Engineering,GitHub,,,,github-api,promptslab/Awesome-Prompt-Engineering,6170,728,Apache-2.0,2023-02-09T18:22:52Z,2026-07-16T21:53:00Z,,2026-07-17T05:43:07+00:00
-ale-0524,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome LLM Agents,https://github.com/kaushikb11/awesome-llm-agents,external,github.com,"General list of LLM agent papers, frameworks, and applications.","General list of LLM agent papers, frameworks, and applications.","General list of LLM agent papers, frameworks, and applications.","Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: General list of LLM agent papers, frameworks, and applications.",Gives readers a concrete source in Awesome LLM Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,533 stars; 329 forks; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1105,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1105,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/kaushikb11/awesome-llm-agents,GitHub - kaushikb11/awesome-llm-agents: A curated list of awesome LLM agents frameworks. · GitHub,A curated list of awesome LLM agents frameworks. Contribute to kaushikb11/awesome-llm-agents development by creating an account on GitHub.,,2023-04-04,2023,kaushikb11/awesome-llm-agents,GitHub,,,,github-api,kaushikb11/awesome-llm-agents,1533,329,,2023-04-04T10:22:43Z,2026-07-15T09:52:09Z,,2026-07-17T05:43:07+00:00
-ale-0525,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome AI Agents,https://github.com/e2b-dev/awesome-ai-agents,external,github.com,Broad AI agent ecosystem map.,Broad AI agent ecosystem map.,Broad AI agent ecosystem map.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Broad AI agent ecosystem map.,Gives readers a concrete source in Awesome AI Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28,826 stars; 3,173 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1106,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1106,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/e2b-dev/awesome-ai-agents,GitHub - e2b-dev/awesome-ai-agents: A list of AI autonomous agents · GitHub,A list of AI autonomous agents. Contribute to e2b-dev/awesome-ai-agents development by creating an account on GitHub.,,2023-06-19,2023,e2b-dev/awesome-ai-agents,GitHub,,,,github-api,e2b-dev/awesome-ai-agents,28826,3173,NOASSERTION,2023-06-19T00:20:06Z,2026-07-17T05:44:47Z,,2026-07-17T05:43:07+00:00
-ale-0526,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome CLI Coding Agents,https://github.com/bradAGI/awesome-cli-coding-agents,external,github.com,"Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.",Gives readers a concrete source in Awesome CLI Coding Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (819 stars; 219 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1107,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1107,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",delegation,builder,curated-index,ok,https://github.com/bradAGI/awesome-cli-coding-agents,"GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. · GitHub","Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. - GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure.",,2026-02-07,2026,bradAGI/awesome-cli-coding-agents,GitHub,,,,github-api,bradAGI/awesome-cli-coding-agents,819,219,,2026-02-07T00:53:24Z,2026-07-17T02:53:08Z,,2026-07-17T05:43:07+00:00
-ale-0527,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Self-Evolving Agents,https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents,external,github.com,"Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.",Gives readers a concrete source in Awesome Self-Evolving Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (328 stars; 19 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1108,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1108,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents,"GitHub - XMUDeepLIT/Awesome-Self-Evolving-Agents: A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. · GitHub","A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. - XMUDeepLIT/Awesome-Self-Evolving-Agents",,2026-02-09,2026,XMUDeepLIT/Awesome-Self-Evolving-Agents,GitHub,,,,github-api,XMUDeepLIT/Awesome-Self-Evolving-Agents,328,19,,2026-02-09T10:57:30Z,2026-07-17T03:11:44Z,,2026-07-17T05:43:07+00:00
-ale-0528,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome AI Agent Papers,https://github.com/VoltAgent/awesome-ai-agent-papers,external,github.com,"Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.",Gives readers a concrete source in Awesome AI Agent Papers when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,588 stars; 165 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1109,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1109,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/VoltAgent/awesome-ai-agent-papers,"GitHub - VoltAgent/awesome-ai-agent-papers: A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. · GitHub","A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. - VoltAgent/awesome-ai-agent-papers",,2026-02-10,2026,VoltAgent/awesome-ai-agent-papers,GitHub,,,,github-api,VoltAgent/awesome-ai-agent-papers,1588,165,MIT,2026-02-10T10:58:31Z,2026-07-17T02:05:21Z,,2026-07-17T05:43:07+00:00
-ale-0529,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,awesome-ralph,https://github.com/snwfdhmp/awesome-ralph,external,github.com,"Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Primary-source operational guidance rather than commentary. Resource-specific angle: Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.",Gives readers a concrete source in awesome-ralph when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (913 stars; 71 forks; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,1110,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1110,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/snwfdhmp/awesome-ralph,"GitHub - snwfdhmp/awesome-ralph: A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. · GitHub","A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. - snwfdhmp/awesome-ralph",,2026-01-19,2026,snwfdhmp/awesome-ralph,GitHub,,,,github-api,snwfdhmp/awesome-ralph,913,71,,2026-01-19T08:42:54Z,2026-07-13T12:09:52Z,,2026-07-17T05:43:07+00:00
-ale-0530,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Agent Loops,https://github.com/serenakeyitan/awesome-agent-loops,external,github.com,"Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.",Gives readers a concrete source in Awesome Agent Loops when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (194 stars; 15 forks; CC-BY-4.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1111,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1111,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective;trigger,builder,curated-index,ok,https://github.com/serenakeyitan/awesome-agent-loops,"GitHub - serenakeyitan/awesome-agent-loops: A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. · GitHub","A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. - serenakeyitan/awesome-agent-loops",,2026-06-09,2026,serenakeyitan/awesome-agent-loops,GitHub,,,,github-api,serenakeyitan/awesome-agent-loops,194,15,CC-BY-4.0,2026-06-09T01:26:51Z,2026-07-15T11:44:20Z,,2026-07-17T05:43:07+00:00
-ale-0531,Discovery And Distribution,discovery-and-distribution,Template,🧾,Landing page,https://chaoyue0307.github.io/awesome-loop-engineering/,external,chaoyue0307.github.io,SEO-friendly entry point for the repository.,SEO-friendly entry point for the repository.,SEO-friendly entry point for the repository.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: SEO-friendly entry point for the repository.,Gives readers a concrete source in Landing page when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Repository-native template, schema, checklist, or guide; signal comes from reuse inside this project.",medium,README.md,1119,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1119,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,reusable-artifact,ok,https://chaoyue0307.github.io/awesome-loop-engineering/,Awesome Loop Engineering,"509 audited resources, 15 operational patterns, loop contracts, and runnable examples for recurring, verified AI-agent systems.",Chaoyue He,,,,chaoyue0307.github.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0532,Discovery And Distribution,discovery-and-distribution,List,🧭,Hugging Face dataset mirror,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,external,huggingface.co,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Gives readers a concrete source in Hugging Face dataset mirror when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Contextual source from huggingface.co; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1120,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1120,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,cy0307/awesome-loop-engineering · Datasets at Hugging Face,We’re on a journey to advance and democratize artificial intelligence through open source and open science.,,,,,Hugging Face,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0533,Discovery And Distribution,discovery-and-distribution,Template,🧾,Landing page source,docs/index.html,local_path,,Source for the static landing page.,Source for the static landing page.,Source for the static landing page.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Source for the static landing page.,Gives readers a concrete source in Landing page source when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1121,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1121,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/index.html,Landing page source,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0534,Discovery And Distribution,discovery-and-distribution,Template,🧾,Sitemap,docs/sitemap.xml,local_path,,Crawl hints for the landing page and core repository pages.,Crawl hints for the landing page and core repository pages.,Crawl hints for the landing page and core repository pages.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Crawl hints for the landing page and core repository pages.,Gives readers a concrete source in Sitemap when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1122,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1122,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/sitemap.xml,Sitemap,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0535,Discovery And Distribution,discovery-and-distribution,Template,🧾,Robots file,docs/robots.txt,local_path,,Allows indexing and points crawlers to the sitemap.,Allows indexing and points crawlers to the sitemap.,Allows indexing and points crawlers to the sitemap.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Allows indexing and points crawlers to the sitemap.,Gives readers a concrete source in Robots file when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1123,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1123,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/robots.txt,Robots file,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0536,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Release notes,https://github.com/ChaoYue0307/awesome-loop-engineering/releases,external,github.com,"Versioned changelog of new resources, patterns, and repository changes.","Versioned changelog of new resources, patterns, and repository changes.","Versioned changelog of new resources, patterns, and repository changes.","Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Versioned changelog of new resources, patterns, and repository changes.",Gives readers a concrete source in Release notes when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (26 stars; 4 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1140,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1140,2026-07-15,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,reusable-artifact,ok,https://github.com/ChaoYue0307/awesome-loop-engineering/releases,Releases · ChaoYue0307/awesome-loop-engineering · GitHub,"🔁 509 audited resources for recurring AI-agent systems: 15 operational patterns, reusable loop contracts, runnable templates, and an interactive Resource Atlas. - Releases · ChaoYue0307/awesome-loop-engineering",,2026-06-09,2026,GitHub Releases,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,26,4,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T05:07:37Z,,2026-07-17T05:43:07+00:00
-ale-0537,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Roadmap,ROADMAP.md,local_path,,"Near-term work, pattern priorities, gallery goals, and open questions.","Near-term work, pattern priorities, gallery goals, and open questions.","Near-term work, pattern priorities, gallery goals, and open questions.","Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Near-term work, pattern priorities, gallery goals, and open questions.",Gives readers a concrete source in Roadmap when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1141,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1141,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/ROADMAP.md,Roadmap,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0538,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Launch article,posts/launch.md,local_path,,Shareable explanation of the concept and repository.,Shareable explanation of the concept and repository.,Shareable explanation of the concept and repository.,Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Shareable explanation of the concept and repository.,Gives readers a concrete source in Launch article when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1142,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1142,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/posts/launch.md,Launch article,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0539,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Discussion guide,meta/DISCUSSIONS.md,local_path,,"Suggested discussion categories, starter prompts, and moderation standard.","Suggested discussion categories, starter prompts, and moderation standard.","Suggested discussion categories, starter prompts, and moderation standard.","The resource is directly reusable as a starting artifact. Resource-specific angle: Suggested discussion categories, starter prompts, and moderation standard.",Gives readers a concrete source in Discussion guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1143,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1143,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/DISCUSSIONS.md,Discussion guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
-ale-0540,Roadmap And Discussion,roadmap-and-discussion,Pattern,🔁,Show your Loop Engineering patterns,https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/2,external,github.com,Community discussion for real or anonymized loop examples.,Community discussion for real or anonymized loop examples.,Community discussion for real or anonymized loop examples.,Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Community discussion for real or anonymized loop examples.,Gives readers a concrete source in Show your Loop Engineering patterns when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (26 stars; 4 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1144,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1144,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,operational-pattern,ok,https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/2,Show your Loop Engineering patterns · ChaoYue0307/awesome-loop-engineering · Discussion #2 · GitHub,Show your Loop Engineering patterns,,2026-06-09,2026,GitHub Discussions,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,26,4,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T05:07:37Z,,2026-07-17T05:43:07+00:00
+ale-0001,Concept Guides,concept-guides,Template,🧾,Canonical Definition,DEFINITION.md,local_path,,"Short definition, positioning, minimal loop test, and citation note.","Short definition, positioning, minimal loop test, and citation note.","Short definition, positioning, minimal loop test, and citation note.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Short definition, positioning, minimal loop test, and citation note.","Gives readers a concrete source in Canonical Definition when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,265,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L265,,Learn,learn,Understand the field and its boundaries.,verification,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/DEFINITION.md,Canonical Definition,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0002,Concept Guides,concept-guides,Template,🧾,Loop Engineering Manifesto,MANIFESTO.md,local_path,,"Concise statement of the concept, commitments, non-goals, and success standard.","Concise statement of the concept, commitments, non-goals, and success standard.","Concise statement of the concept, commitments, non-goals, and success standard.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Concise statement of the concept, commitments, non-goals, and success standard.","Gives readers a concrete source in Loop Engineering Manifesto when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,266,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L266,,Learn,learn,Understand the field and its boundaries.,objective,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/MANIFESTO.md,Loop Engineering Manifesto,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0003,Concept Guides,concept-guides,Template,🧾,Loop Engineering Taxonomy,TAXONOMY.md,local_path,,"Classification by trigger, intake, verification, state model, topology, and operating domain.","Classification by trigger, intake, verification, state model, topology, and operating domain.","Classification by trigger, intake, verification, state model, topology, and operating domain.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Classification by trigger, intake, verification, state model, topology, and operating domain.","Gives readers a concrete source in Loop Engineering Taxonomy when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,267,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L267,,Learn,learn,Understand the field and its boundaries.,trigger;intake;verification;state,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/TAXONOMY.md,Loop Engineering Taxonomy,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0004,Concept Guides,concept-guides,Critique,⚠️,Loop Engineering Anti-Patterns,ANTI-PATTERNS.md,local_path,,"Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Common failure modes such as prompt loops with no contract, infinite retries, model self-approval, hidden state, and unsafe autonomy.","Gives readers a concrete source in Loop Engineering Anti-Patterns when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,268,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L268,,Learn,learn,Understand the field and its boundaries.,state;budget;escalation,newcomer,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/ANTI-PATTERNS.md,Loop Engineering Anti-Patterns,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0005,Concept Guides,concept-guides,Template,🧾,Comparison Guide,COMPARISON.md,local_path,,"Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Distinguishes Loop Engineering from prompt engineering, context engineering, harness engineering, workflow automation, agent workflows, and evaluation loops.","Gives readers a concrete source in Comparison Guide when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,269,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L269,,Learn,learn,Understand the field and its boundaries.,context;verification,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/COMPARISON.md,Comparison Guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0006,Concept Guides,concept-guides,Template,🧾,Sourced Signals And Quotes,QUOTES.md,local_path,,Short sourced signals from linked public materials that anchor the emerging concept.,Short sourced signals from linked public materials that anchor the emerging concept.,Short sourced signals from linked public materials that anchor the emerging concept.,Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Short sourced signals from linked public materials that anchor the emerging concept.,"Gives readers a concrete source in Sourced Signals And Quotes when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,270,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L270,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/QUOTES.md,Sourced Signals And Quotes,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0007,Concept Guides,concept-guides,Template,🧾,Outreach Kit,meta/OUTREACH.md,local_path,,"Conservative messages for inviting corrections, sources, and real-world loop patterns.","Conservative messages for inviting corrections, sources, and real-world loop patterns.","Conservative messages for inviting corrections, sources, and real-world loop patterns.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Conservative messages for inviting corrections, sources, and real-world loop patterns.","Gives readers a concrete source in Outreach Kit when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Repository-native artifact maintained and validated by this project's checks.,medium,README.md,271,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L271,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer;builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/OUTREACH.md,Outreach Kit,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0008,Start Here,start-here,Blog,📝,Loop Engineering by Addy Osmani,https://addyosmani.com/blog/loop-engineering/,external,addyosmani.com,"Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Addy Osmani's framing of loop engineering as the layer above manually prompting coding agents, with concrete primitives across Codex and Claude Code; also on [Substack](https://addyo.substack.com/p/loop-engineering) with the original discussion trail and Steinberger and Cherny quotations.","Gives readers a concrete source in Loop Engineering by Addy Osmani when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,351,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L351,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://addyosmani.com/blog/loop-engineering/,AddyOsmani.com - Loop Engineering,You don't really need to be good at prompting anymore. The thing to get good at is the loop that does the prompting for you. It's five building blocks plus s...,Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0009,Start Here,start-here,Blog,📝,Peter Steinberger on designing loops,https://x.com/steipete/status/2063697162748260627,external,x.com,"The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: The June 2026 post - ""you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents"" - that catalyzed the current discussion.","Gives readers a concrete source in Peter Steinberger on designing loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,352,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L352,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://x.com/steipete/status/2063697162748260627,"Peter Steinberger 🦞 on X: ""Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents."" / X",Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.,,2026-06-07,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0010,Start Here,start-here,Blog,📝,Boris Cherny: five tips for running Opus autonomously for hours or days,https://x.com/bcherny/status/2063792263067754658,external,x.com,"The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: The Claude Code creator's compact loop recipe: auto-mode permissions, dynamic workflows, `/goal` or `/loop`, the cloud runner, and end-to-end self-verification.","Gives readers a concrete source in Boris Cherny: five tips for running Opus autonomously for hours or days when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,353,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L353,,Learn,learn,Understand the field and its boundaries.,objective;workspace;verification,newcomer,practitioner-analysis,ok,https://x.com/bcherny/status/2063792263067754658,"Boris Cherny on X: ""Seeing a number of benchmarks showing Opus is the best model for long-running work. Five tips for running Opus autonomously for hours/days: 1. Use auto mode for permissions, so Claude doesn’t ask for approval 2. Use dynamic workflows, to have Claude orchestrate"" / X","Seeing a number of benchmarks showing Opus is the best model for long-running work. Five tips for running Opus autonomously for hours/days: 1. Use auto mode for permissions, so Claude doesn’t ask for approval 2. Use dynamic workflows, to have Claude orchestrate",,2026-06-08,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0011,Start Here,start-here,Blog,📝,Loop Engineering by Cobus Greyling,https://cobusgreyling.substack.com/p/loop-engineering,external,cobusgreyling.substack.com,"Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Concise explanation of the shift from prompting agents to designing loops that discover work, delegate, verify, persist, and continue.","Gives readers a concrete source in Loop Engineering by Cobus Greyling when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from cobusgreyling.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,354,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L354,,Learn,learn,Understand the field and its boundaries.,intake;delegation;verification;state,newcomer,practitioner-analysis,ok,https://cobusgreyling.substack.com/p/loop-engineering,Loop Engineering,The core of Loop Engineering,Cobus Greyling,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0012,Start Here,start-here,Blog,📝,Stop Prompting. Design the Loop.,https://www.pulumi.com/blog/stop-prompting-design-the-loop/,external,www.pulumi.com,"Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Practical breakdown of loop building blocks - automations, worktrees, skills, connectors, subagents - plus external memory and verification through oracles such as tests and builds.","Gives readers a concrete source in Stop Prompting. Design the Loop. when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from www.pulumi.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,355,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L355,,Learn,learn,Understand the field and its boundaries.,workspace;context;delegation;verification;exit,newcomer,practitioner-analysis,ok,https://www.pulumi.com/blog/stop-prompting-design-the-loop/,Stop Prompting. Design the Loop. | Pulumi Blog,"The unit of work moved from the prompt to the loop. The five pieces of loop engineering, the memory that makes it compound, and what it won't do for you.",Engin Diri,2026-06-09,2026,,pulumi,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0013,Start Here,start-here,Blog,📝,"Writing Loops, Not Prompts, Explained",https://rico.codes/loops-not-prompts,external,rico.codes,"Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Rico Kahler's break-even model for when a recurring task justifies building a loop instead of prompting, with stop conditions, evidence collection, and an execution-horizon framing for moving from execution-bound to judgment-bound work.","Gives readers a concrete source in Writing Loops, Not Prompts, Explained when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from rico.codes; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,356,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L356,,Learn,learn,Understand the field and its boundaries.,exit,newcomer,practitioner-analysis,ok,https://rico.codes/loops-not-prompts,"Writing Loops, Not Prompts, Explained | rico.codes","Loop engineering is not about abandoning prompts. It is about moving repeated steering work into verifiable systems so attention can stay on judgment, review, and taste.",,,,,rico.codes,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0014,Start Here,start-here,Blog,📝,Loop Engineering: A Guide for Engineers and Practitioners,https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943,external,medium.com,"Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","The resource is directly reusable as a starting artifact. Resource-specific angle: Adnan Masood's practitioner guide that organizes loop design into triggers, topologies, verifiers, and termination rules, with coverage of failure modes, cost control, and observability for production agent loops.","Gives readers a concrete source in Loop Engineering: A Guide for Engineers and Practitioners when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,357,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L357,,Learn,learn,Understand the field and its boundaries.,trigger;budget;exit,newcomer,practitioner-analysis,ok,https://medium.com/@adnanmasood/loop-engineering-a-guide-for-engineers-and-practitioners-893bb65ea943,"Medium Loop Engineering: A Guide for Engineers and Practitioners | by Adnan Masood, PhD. | Jun, 2026 | Medium","Loop engineering: designing the control system that prompts, verifies, and stops AI agents in production. A field guide for engineers.","Adnan Masood, PhD.",2026-06-24,2026,,Medium,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0015,Start Here,start-here,Blog,📝,"Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive",https://sderosiaux.substack.com/p/loop-engineering-cheap-generation,external,sderosiaux.substack.com,"Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Stephane Derosiaux's essay on the economics of the loop layer (generation becomes abundant while judgment becomes the bottleneck), proposing evaluator agents that must act rather than merely review, and cataloging failure modes such as unverified merges and quota depletion.","Gives readers a concrete source in Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from sderosiaux.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,358,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L358,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://sderosiaux.substack.com/p/loop-engineering-cheap-generation,"Loop Engineering: When Generation Gets Cheap, Judgment Gets Expensive","Agentic loops make code, plans, and PRs abundant. The scarce part is knowing what is right.",Stephane Derosiaux,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0016,Start Here,start-here,Blog,📝,Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development,https://x.com/AndrewYNg/status/2071988145667928442,external,x.com,"Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Andrew Ng's letter laying out three product-development loops (agentic coding in minutes, developer feedback in hours, external feedback in days) and arguing that human-in-the-loop persists wherever the human knows something the AI does not.","Gives readers a concrete source in Andrew Ng on Loop Engineering and the Three Loops of AI-Native Product Development when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,359,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L359,,Learn,learn,Understand the field and its boundaries.,state;escalation,newcomer,practitioner-analysis,ok,https://x.com/AndrewYNg/status/2071988145667928442,"Andrew Ng on X: ""“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d https://t.co/bhuRw8lrFC"" / X","“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d",,2026-06-30,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0017,Start Here,start-here,Blog,📝,From Prompting Agents to Loop Engineering,https://x.com/omarsar0/status/2068008743153832264,external,x.com,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: DAIR.AI founder Elvis Saravia's X article examining the claim that you should stop prompting coding agents and start designing loops that prompt them for you.,"Gives readers a concrete source in From Prompting Agents to Loop Engineering when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,360,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L360,,Learn,learn,Understand the field and its boundaries.,exit,newcomer,practitioner-analysis,ok,https://x.com/omarsar0/status/2068008743153832264,"elvis on X: ""https://t.co/d8LgEwfVH6"" / X",https://t.co/d8LgEwfVH6,,2026-06-19,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0018,Start Here,start-here,Blog,📝,My Lord! AI Programming Undergoes Another Major Shift,https://eu.36kr.com/en/p/3844224911346184,external,eu.36kr.com,"Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Broad coverage of the Boris Cherny and Peter Steinberger discussion, including the distinction between cold-start scripts and persistent agent loops.","Gives readers a concrete source in My Lord! AI Programming Undergoes Another Major Shift when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from eu.36kr.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,361,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L361,,Learn,learn,Understand the field and its boundaries.,state,newcomer,practitioner-analysis,ok,https://eu.36kr.com/en/p/3844224911346184,My Lord! AI Programming Undergoes Another Major Shift: Claude Code Father & Lobster Founder Endorse New Paradigm - Could It Kill Prompt Engineering?,Stop writing prompts for programming agents now.,,,,,eu.36kr.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0019,Start Here,start-here,Blog,📝,The Anthropic leader who built Claude Code ditched prompting - now he writes loops,https://thenewstack.io/loop-engineering/,external,thenewstack.io,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: The New Stack's report on Boris Cherny's shift from prompting to loop writing and what it changes about developer workflow.,"Gives readers a concrete source in The Anthropic leader who built Claude Code ditched prompting - now he writes loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from thenewstack.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,362,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L362,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://thenewstack.io/loop-engineering/,The Anthropic leader who built Claude Code says he ditched prompting — now he just writes loops. - The New Stack,Loop engineering — the practice of designing automated agent workflows instead of prompting manually — is reshaping how developers use Claude Code and OpenAI Codex in 2026.,Janakiram MSV,2026-06-10,2026,,The New Stack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0020,Start Here,start-here,Blog,📝,Engineering for Agents That Never Sleep,https://nader.substack.com/p/engineering-for-agents-that-never,external,nader.substack.com,"Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Captures the early community framing of Loop Engineering as repeated agent delegation rather than prompt craft. Resource-specific angle: Cognition's Nader Dabit predicts the human-initiated share of Devin sessions will invert from 70/30 to 10/90 within a year as signals like alerts and failing tests trigger agents directly, recasting the engineer's job as designing triggers, constraints, and quality gates.","Gives readers a concrete source in Engineering for Agents That Never Sleep when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from nader.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,363,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L363,,Learn,learn,Understand the field and its boundaries.,trigger;verification;escalation,newcomer,practitioner-analysis,ok,https://nader.substack.com/p/engineering-for-agents-that-never,Engineering for Agents That Never Sleep - by Nader Dabit,Originally posted on X.,Nader Dabit,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0021,Start Here,start-here,Blog,📝,Loop Engineering Orange Book,https://github.com/alchaincyf/loop-engineering-orange-book,external,github.com,"Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","The resource is directly reusable as a starting artifact. Resource-specific angle: Plain-language bilingual (Chinese and English) field guide to loop engineering by HuaShu, framing the discipline as one floor above harness engineering: the outer system that decides when and why agents run.","Gives readers a concrete source in Loop Engineering Orange Book when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Inspectable GitHub source (1,022 stars; 98 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,364,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L364,,Learn,learn,Understand the field and its boundaries.,whole-loop,newcomer,practitioner-analysis,ok,https://github.com/alchaincyf/loop-engineering-orange-book,GitHub - alchaincyf/loop-engineering-orange-book: 别再问我什么是 Loop Engineering — 橙皮书系列。A plain-language guide to loop engineering (中文 + English PDF). Free. · GitHub,别再问我什么是 Loop Engineering — 橙皮书系列。A plain-language guide to loop engineering (中文 + English PDF). Free. - alchaincyf/loop-engineering-orange-book,,2026-06-15,2026,alchaincyf/loop-engineering-orange-book,GitHub,,,,github-api,alchaincyf/loop-engineering-orange-book,1022,98,NOASSERTION,2026-06-15T05:27:07Z,2026-07-17T02:30:11Z,,2026-07-17T05:43:07+00:00
+ale-0022,Start Here,start-here,Blog,📝,How I AI: How to Write AI Agent Loops in Claude Code and Codex,https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops,external,www.lennysnewsletter.com,"Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Mozilla distinguished engineer Brian Grinstead demonstrates goal-based and scheduled loops, including a daily PR-review loop with per-PR subagents, on Lenny's Newsletter.","Gives readers a concrete source in How I AI: How to Write AI Agent Loops in Claude Code and Codex when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from www.lennysnewsletter.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,365,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L365,,Learn,learn,Understand the field and its boundaries.,objective;trigger;delegation,newcomer,practitioner-analysis,ok,https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loops,🎙️ How I AI: How to write AI agent loops in Claude Code and Codex + How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead,"Your weekly listens from How I AI, part of the Lenny’s Podcast Network",Lenny Rachitsky,,,,lennysnewsletter.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0023,Start Here,start-here,Paper,📄,"Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control",https://arxiv.org/abs/2607.14890,external,arxiv.org,"Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Defines evidence-gated lifecycle control for agent loops and reports zero false-DONE outcomes across 10 scenarios and zero accepts across 18 tampering classes; its 9,240-cell ablation identifies which gates prevent error amplification, while noting the evaluation covers one model family and 24 tasks.","Gives readers a concrete source in Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.14890; inspect its method and evaluation before treating results as production evidence.,medium,README.md,366,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L366,2026-07-17,Learn,learn,Understand the field and its boundaries.,verification;exit,newcomer;researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14890,"[2607.14890] Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control","Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence. We present Proof-or-Stop Lifecycle Control, a method that permits lifecycle transitions only when fresh, tracked-source-state-bound, mechanically verifiable evidence satisfies the relevant gate. The method treats agent outputs as claims rather than lifecycle state, and uses proof operationally to mean gate-admissible evidence under a stated trust model, not semantic program correctness. We evaluate an open-source implementation through mechanism tests, a powered control-policy ablation, and operated self-application evidence. The unattended-loop engine passed 10 of 10 scenarios with zero false-DONE, and local-key receipt bundles rejected 18 tamper classes with zero false accepts. In a 9,240-cell ablation, the pre-registered A4 versus A2-prime comparison reduced visible-pass/hidden-fail amplification from 31 of 1,800 injected cells under a compute-budgeted naive loop to 2 of 1,800 under the gated loop, a 1.6 percentage-point improvement in not-amplified rate with a 95 percent confidence interval of [0.8, 2.5]. A near-compute A3 versus A4 comparison, 14 of 1,800 versus 2 of 1,800, indicates that the gain is associated with enforcing review as a lifecycle gate rather than merely adding a reviewer. The self-application corpus contains 565 stories and 1,007 review findings, with 94.8 percent resolved, plus a 68-row high/critical cross-vendor exhibit. These results support Proof-or-Stop as a model-agnostic, host-neutral control layer for deciding which autonomous-agent claims a lifecycle may act on. The evaluation is limited to one model family, 24 ablation tasks, and a self-hosted corpus.",Jek Huang; Jeffery Hsia; Jiayi Sun; Freddie Shi; Wei Huang; Ian H. White,2026-07-16,2026,arXiv,arXiv,,"48 pages, 10 figures, 29 numbered tables. Preprint v1",cs.AI,arxiv-api,,,,,,,2607.14890,2026-07-17T05:43:07+00:00
+ale-0024,Pattern Library,pattern-library,Pattern,🔁,PR babysitter,patterns/pr-babysitter.md,local_path,,"Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Repeatedly checks review comments, CI, merge conflicts, stale threads, and readiness to merge.",Gives readers a concrete source in PR babysitter when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,445,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L445,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/pr-babysitter.md,PR babysitter,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0025,Pattern Library,pattern-library,Pattern,🔁,CI repair loop,patterns/ci-repair-loop.md,local_path,,"Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Reproduces failing checks, patches narrowly, reruns evidence, and escalates when failures are outside scope.",Gives readers a concrete source in CI repair loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,446,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L446,,Design,design,Specify a loop contract and operating pattern.,escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/ci-repair-loop.md,CI repair loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0026,Pattern Library,pattern-library,Pattern,🔁,Docs drift collector,patterns/docs-drift-collector.md,local_path,,"Finds mismatches between docs and code, proposes small patches, and verifies examples.","Finds mismatches between docs and code, proposes small patches, and verifies examples.","Finds mismatches between docs and code, proposes small patches, and verifies examples.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Finds mismatches between docs and code, proposes small patches, and verifies examples.",Gives readers a concrete source in Docs drift collector when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,447,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L447,,Design,design,Specify a loop contract and operating pattern.,verification,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/docs-drift-collector.md,Docs drift collector,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0027,Pattern Library,pattern-library,Pattern,🔁,Deploy verifier,patterns/deploy-verifier.md,local_path,,"Watches rollout signals, compares them with release expectations, and stops on anomalies.","Watches rollout signals, compares them with release expectations, and stops on anomalies.","Watches rollout signals, compares them with release expectations, and stops on anomalies.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Watches rollout signals, compares them with release expectations, and stops on anomalies.",Gives readers a concrete source in Deploy verifier when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,448,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L448,,Design,design,Specify a loop contract and operating pattern.,exit,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/deploy-verifier.md,Deploy verifier,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0028,Pattern Library,pattern-library,Pattern,🔁,Feedback clusterer,patterns/feedback-clusterer.md,local_path,,"Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Periodically groups GitHub, Linear, Slack, support, or social feedback into actionable themes.",Gives readers a concrete source in Feedback clusterer when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,449,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L449,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/feedback-clusterer.md,Feedback clusterer,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0029,Pattern Library,pattern-library,Pattern,🔁,Dependency triage loop,patterns/dependency-triage-loop.md,local_path,,"Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Classifies dependency updates, applies safe groups, verifies them, and escalates risky upgrades.",Gives readers a concrete source in Dependency triage loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,450,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L450,,Design,design,Specify a loop contract and operating pattern.,intake;verification;escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/dependency-triage-loop.md,Dependency triage loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0030,Pattern Library,pattern-library,Pattern,🔁,Evaluation regression loop,patterns/evaluation-regression-loop.md,local_path,,"Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Investigates degraded agent evals with baseline traces, targeted reruns, and repair proposals.",Gives readers a concrete source in Evaluation regression loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,451,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L451,,Design,design,Specify a loop contract and operating pattern.,verification,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/evaluation-regression-loop.md,Evaluation regression loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0031,Pattern Library,pattern-library,Pattern,🔁,Security review loop,patterns/security-review-loop.md,local_path,,"Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Reviews sensitive diffs with evidence-backed findings, safe permissions, and human approval boundaries.",Gives readers a concrete source in Security review loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,452,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L452,,Design,design,Specify a loop contract and operating pattern.,workspace;escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/security-review-loop.md,Security review loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0032,Pattern Library,pattern-library,Pattern,🔁,Cost-control loop,patterns/cost-control-loop.md,local_path,,"Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Monitors agent workflow spend, identifies waste, proposes scoped savings, and preserves quality gates.",Gives readers a concrete source in Cost-control loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,453,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L453,,Design,design,Specify a loop contract and operating pattern.,budget,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/cost-control-loop.md,Cost-control loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0033,Pattern Library,pattern-library,Pattern,🔁,Bug hunting loop,patterns/bug-hunting-loop.md,local_path,,"Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Discovers, reproduces, minimizes, and reports bugs with concrete evidence.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Discovers, reproduces, minimizes, and reports bugs with concrete evidence.",Gives readers a concrete source in Bug hunting loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,454,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L454,,Design,design,Specify a loop contract and operating pattern.,intake,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/bug-hunting-loop.md,Bug hunting loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0034,Pattern Library,pattern-library,Pattern,🔁,Enterprise approval loop,patterns/enterprise-approval-loop.md,local_path,,Drives a permissioned change through required gates and approvers with a full audit trail.,Drives a permissioned change through required gates and approvers with a full audit trail.,Drives a permissioned change through required gates and approvers with a full audit trail.,"Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Drives a permissioned change through required gates and approvers with a full audit trail.",Gives readers a concrete source in Enterprise approval loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,455,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L455,,Design,design,Specify a loop contract and operating pattern.,escalation,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/enterprise-approval-loop.md,Enterprise approval loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0035,Pattern Library,pattern-library,Pattern,🔁,Incident response loop,patterns/incident-response-loop.md,local_path,,"Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Triages an alert into an owned, evidence-backed incident with a postmortem seed.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Triages an alert into an owned, evidence-backed incident with a postmortem seed.",Gives readers a concrete source in Incident response loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,456,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L456,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/incident-response-loop.md,Incident response loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0036,Pattern Library,pattern-library,Pattern,🔁,Data-quality loop,patterns/data-quality-loop.md,local_path,,Validates each dataset refresh against quality rules and quarantines bad versions.,Validates each dataset refresh against quality rules and quarantines bad versions.,Validates each dataset refresh against quality rules and quarantines bad versions.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Validates each dataset refresh against quality rules and quarantines bad versions.,Gives readers a concrete source in Data-quality loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,457,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L457,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/data-quality-loop.md,Data-quality loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0037,Pattern Library,pattern-library,Pattern,🔁,Release-note loop,patterns/release-note-loop.md,local_path,,"Drafts release notes from merged commits, issues, and PRs with linked evidence.","Drafts release notes from merged commits, issues, and PRs with linked evidence.","Drafts release notes from merged commits, issues, and PRs with linked evidence.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Drafts release notes from merged commits, issues, and PRs with linked evidence.",Gives readers a concrete source in Release-note loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,458,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L458,,Design,design,Specify a loop contract and operating pattern.,intake,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/release-note-loop.md,Release-note loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0038,Pattern Library,pattern-library,Pattern,🔁,Model-routing loop,patterns/model-routing-loop.md,local_path,,"Routes tasks across models on measured quality, latency, privacy, and cost.","Routes tasks across models on measured quality, latency, privacy, and cost.","Routes tasks across models on measured quality, latency, privacy, and cost.","Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths. Resource-specific angle: Routes tasks across models on measured quality, latency, privacy, and cost.",Gives readers a concrete source in Model-routing loop when they need to turn a recurring-agent idea into an explicit loop contract.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,459,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L459,,Design,design,Specify a loop contract and operating pattern.,budget,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/model-routing-loop.md,Model-routing loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0039,Core Loop Primitives,core-loop-primitives,Docs,📚,Scheduled tasks - ChatGPT Learn,https://learn.chatgpt.com/docs/automations?surface=app,external,learn.chatgpt.com,"Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official guidance for recurring background tasks, triage inboxes, skills, and isolated workspaces.",Gives readers a concrete source in Scheduled tasks - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,467,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L467,,Design,design,Specify a loop contract and operating pattern.,trigger;intake;workspace,builder,official-documentation,ok,https://learn.chatgpt.com/docs/automations?surface=app,Scheduled tasks | ChatGPT Learn,Schedule recurring tasks in ChatGPT,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0040,Core Loop Primitives,core-loop-primitives,Docs,📚,Follow a goal - ChatGPT Learn,https://learn.chatgpt.com/use-cases/follow-goals,external,learn.chatgpt.com,"Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official guidance for durable objectives with stopping conditions, validation commands, checkpoints, and progress logs.",Gives readers a concrete source in Follow a goal - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,468,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L468,,Design,design,Specify a loop contract and operating pattern.,objective;state;exit,builder,official-documentation,ok,https://learn.chatgpt.com/use-cases/follow-goals,Follow a goal | ChatGPT use cases,Use `/goal` when a task needs Codex to keep working across turns toward a verifiable stopping condition.,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0041,Core Loop Primitives,core-loop-primitives,Docs,📚,Git worktrees - ChatGPT Learn,https://learn.chatgpt.com/docs/environments/git-worktrees,external,learn.chatgpt.com,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Primary-source operational guidance rather than commentary. Resource-specific angle: Official worktree model for isolated parallel tasks and handoffs between local and background workspaces.,Gives readers a concrete source in Git worktrees - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,469,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L469,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/environments/git-worktrees,Worktrees | ChatGPT Learn,Use Git worktrees in Codex in the ChatGPT desktop app to run chats in parallel,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0042,Core Loop Primitives,core-loop-primitives,Docs,📚,Prompting - ChatGPT Learn,https://learn.chatgpt.com/docs/prompting,external,learn.chatgpt.com,"Explains the Codex loop, threads, context, and goal-oriented prompting.","Explains the Codex loop, threads, context, and goal-oriented prompting.","Explains the Codex loop, threads, context, and goal-oriented prompting.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Explains the Codex loop, threads, context, and goal-oriented prompting.",Gives readers a concrete source in Prompting - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,470,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L470,,Design,design,Specify a loop contract and operating pattern.,objective;context,builder,official-documentation,ok,https://learn.chatgpt.com/docs/prompting,Prompting | ChatGPT Learn,"Write useful prompts for Chat mode, Work mode, and Codex",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0043,Core Loop Primitives,core-loop-primitives,Docs,📚,Customization overview - ChatGPT Learn,https://learn.chatgpt.com/docs/customization/overview,external,learn.chatgpt.com,"Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Maps `AGENTS.md`, memories, skills, MCP, and subagents into a coherent customization stack.",Gives readers a concrete source in Customization overview - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,471,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L471,,Design,design,Specify a loop contract and operating pattern.,context;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/customization/overview,Customization | ChatGPT Learn,"How to customize Codex with project guidance, skills, MCP, and subagents",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0044,Core Loop Primitives,core-loop-primitives,Docs,📚,Build skills - ChatGPT Learn,https://learn.chatgpt.com/docs/build-skills,external,learn.chatgpt.com,"Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official skill format for reusable workflows, scripts, MCP dependencies, invocation policy, and plugin packaging.",Gives readers a concrete source in Build skills - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,472,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L472,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://learn.chatgpt.com/docs/build-skills,Build skills | ChatGPT Learn,Give Codex new capabilities and expertise,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0045,Core Loop Primitives,core-loop-primitives,Docs,📚,Plugins - ChatGPT Learn,https://learn.chatgpt.com/docs/plugins,external,learn.chatgpt.com,"Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Bundles skills, app integrations, and MCP servers into reusable loop capabilities.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Bundles skills, app integrations, and MCP servers into reusable loop capabilities.",Gives readers a concrete source in Plugins - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,473,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L473,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://learn.chatgpt.com/docs/plugins,Plugins | ChatGPT Learn,"Browse, install, and use plugins on supported ChatGPT and Codex surfaces",,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0046,Core Loop Primitives,core-loop-primitives,Tool,🧰,dotskills,https://github.com/vincentkoc/dotskills,external,github.com,"A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: A `.skills` registry of curated Codex and OpenClaw skills, framed as an ""ADE Loop"" (Agent Development Environment to registry to Skills Gym) where reusable skills are developed, shared, and evaluated across runs.",Gives readers a concrete source in dotskills when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (95 stars; 9 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,474,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L474,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,source-implementation,ok,https://github.com/vincentkoc/dotskills,"GitHub - vincentkoc/dotskills: 🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns. · GitHub","🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns. - vincentkoc/dotskills",,2026-02-17,2026,vincentkoc/dotskills,GitHub,,,,github-api,vincentkoc/dotskills,95,9,MIT,2026-02-17T05:08:29Z,2026-07-16T23:51:40Z,,2026-07-17T05:43:07+00:00
+ale-0047,Core Loop Primitives,core-loop-primitives,Docs,📚,Developer commands - ChatGPT Learn,https://learn.chatgpt.com/docs/developer-commands?surface=cli,external,learn.chatgpt.com,"CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: CLI commands for switching agent threads, browsing skills, inspecting MCP tools, and using subagent workflows.",Gives readers a concrete source in Developer commands - ChatGPT Learn when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,475,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L475,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/docs/developer-commands?surface=cli,Developer commands | ChatGPT Learn,Reference for commands and slash commands in Codex developer surfaces,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0048,Core Loop Primitives,core-loop-primitives,Pattern,🔁,Autonomous Loops,https://claudecodeguide.dev/docs/patterns/autonomous-loops,external,claudecodeguide.dev,"Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Claude Code pattern using task files, stop hooks, restart behavior, hard limits, and a kill switch.",Gives readers a concrete source in Autonomous Loops when they need to turn a recurring-agent idea into an explicit loop contract.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,476,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L476,,Design,design,Specify a loop contract and operating pattern.,exit,builder,operational-pattern,ok,https://claudecodeguide.dev/docs/patterns/autonomous-loops,Claude Code Autonomous Loops | Claude Code Guide,"Point Claude Code at a problem, walk away, come back to a green build. Task templates, kill switches, and why boundaries matter more than anything else.",,,,,claudecodeguide.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0049,Core Loop Primitives,core-loop-primitives,Docs,📚,Claude Code Glossary,https://code.claude.com/docs/en/glossary.md,external,code.claude.com,"Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Defines the agentic loop, hooks, subagents, skills, MCP, and related primitives in Claude Code terminology.",Gives readers a concrete source in Claude Code Glossary when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,477,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L477,,Design,design,Specify a loop contract and operating pattern.,delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/glossary.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0050,Core Loop Primitives,core-loop-primitives,Docs,📚,Keep Claude working toward a goal,https://code.claude.com/docs/en/goal,external,code.claude.com,`/goal` runs turn after turn until a completion condition is met by a verifier.,`/goal` runs turn after turn until a completion condition is met by a verifier.,`/goal` runs turn after turn until a completion condition is met by a verifier.,Verification is promoted from a final check to a loop-control signal. Resource-specific angle: `/goal` runs turn after turn until a completion condition is met by a verifier.,Gives readers a concrete source in Keep Claude working toward a goal when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,478,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L478,,Design,design,Specify a loop contract and operating pattern.,objective;exit,builder,official-documentation,ok,https://code.claude.com/docs/en/goal,Keep Claude working toward a goal - Claude Code Docs,Set a completion condition with /goal and Claude keeps working across turns until the condition is met.,,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0051,Core Loop Primitives,core-loop-primitives,Docs,📚,Run prompts on a schedule,https://code.claude.com/docs/en/scheduled-tasks,external,code.claude.com,"`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","`/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: `/loop`, scheduled tasks, reminders, monitor tools, and session-scoped recurring prompts.",Gives readers a concrete source in Run prompts on a schedule when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,479,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L479,,Design,design,Specify a loop contract and operating pattern.,trigger;workspace,builder,official-documentation,ok,https://code.claude.com/docs/en/scheduled-tasks,Run prompts on a schedule - Claude Code Docs,"Use /loop and the cron scheduling tools to run prompts repeatedly, poll for status, or set one-time reminders within a Claude Code session.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0052,Core Loop Primitives,core-loop-primitives,Docs,📚,Automate work with routines,https://code.claude.com/docs/en/routines,external,code.claude.com,"Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Claude Code routines: persistent cloud automations triggered by schedules, API calls, or GitHub events, with connectors, scoped environments, and branch-push limits.",Gives readers a concrete source in Automate work with routines when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,480,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L480,,Design,design,Specify a loop contract and operating pattern.,trigger;state,builder,official-documentation,ok,https://code.claude.com/docs/en/routines,Automate work with routines - Claude Code Docs,"Put Claude Code on autopilot. Define routines that run on a schedule, trigger on API calls, or react to GitHub events from Anthropic-managed cloud infrastructure.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0053,Core Loop Primitives,core-loop-primitives,Docs,📚,Desktop scheduled tasks,https://code.claude.com/docs/en/desktop-scheduled-tasks,external,code.claude.com,"Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Local recurring runs on your own machine, with the persistence, file-access, permission, worktree, and missed-run trade-offs that distinguish them from `/loop` and cloud routines.",Gives readers a concrete source in Desktop scheduled tasks when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,481,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L481,,Design,design,Specify a loop contract and operating pattern.,trigger;workspace;state,builder,official-documentation,ok,https://code.claude.com/docs/en/desktop-scheduled-tasks,Schedule recurring tasks in Claude Code Desktop - Claude Code Docs,"Set up scheduled tasks in Claude Code Desktop to run Claude automatically on a recurring basis for daily code reviews, dependency audits, or morning briefings.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0054,Core Loop Primitives,core-loop-primitives,Docs,📚,Run parallel sessions with worktrees,https://code.claude.com/docs/en/worktrees,external,code.claude.com,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.,"Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Worktree isolation for parallel sessions and subagents so concurrent edits do not collide.",Gives readers a concrete source in Run parallel sessions with worktrees when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,482,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L482,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/worktrees,Run parallel sessions with worktrees - Claude Code Docs,"Isolate parallel Claude Code sessions in separate git worktrees so changes don't collide. Covers the --worktree flag, subagent isolation, .worktreeinclude, cleanup, and non-git VCS hooks.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0055,Core Loop Primitives,core-loop-primitives,Docs,📚,Automate actions with hooks,https://code.claude.com/docs/en/hooks-guide,external,code.claude.com,Claude Code hooks guide for deterministic lifecycle control around model actions.,Claude Code hooks guide for deterministic lifecycle control around model actions.,Claude Code hooks guide for deterministic lifecycle control around model actions.,The resource is directly reusable as a starting artifact. Resource-specific angle: Claude Code hooks guide for deterministic lifecycle control around model actions.,Gives readers a concrete source in Automate actions with hooks when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,483,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L483,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/hooks-guide,Automate actions with hooks - Claude Code Docs,"Run shell commands automatically when Claude Code edits files, finishes tasks, or needs input. Format code, send notifications, validate commands, and enforce project rules.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0056,Core Loop Primitives,core-loop-primitives,Docs,📚,Hooks reference,https://code.claude.com/docs/en/hooks.md,external,code.claude.com,"Event-level reference for session, turn, tool-call, and subagent hooks.","Event-level reference for session, turn, tool-call, and subagent hooks.","Event-level reference for session, turn, tool-call, and subagent hooks.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Event-level reference for session, turn, tool-call, and subagent hooks.",Gives readers a concrete source in Hooks reference when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,484,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L484,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/hooks.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0057,Core Loop Primitives,core-loop-primitives,Docs,📚,Common workflows - Claude Code,https://code.claude.com/docs/en/common-workflows,external,code.claude.com,"Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Practical workflows for worktrees, subagents, CI, batch processing, planning, and resuming prior work.",Gives readers a concrete source in Common workflows - Claude Code when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,485,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L485,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/common-workflows,Common workflows - Claude Code Docs,"Step-by-step guides for exploring codebases, fixing bugs, refactoring, testing, and other everyday tasks with Claude Code.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0058,Core Loop Primitives,core-loop-primitives,Docs,📚,Manage multiple agents with agent view,https://code.claude.com/docs/en/agent-view.md,external,code.claude.com,"Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Dashboard for dispatching, monitoring, and attaching to background agent sessions.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Dashboard for dispatching, monitoring, and attaching to background agent sessions.",Gives readers a concrete source in Manage multiple agents with agent view when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,486,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L486,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-view.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0059,Core Loop Primitives,core-loop-primitives,Docs,📚,Run agents in parallel,https://code.claude.com/docs/en/agents.md,external,code.claude.com,"Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Compares agent view, subagents, agent teams, worktrees, tasks, and workflows for parallel work.",Gives readers a concrete source in Run agents in parallel when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,487,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L487,,Design,design,Specify a loop contract and operating pattern.,workspace;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/agents.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0060,Core Loop Primitives,core-loop-primitives,Docs,📚,Orchestrate subagents at scale with dynamic workflows,https://code.claude.com/docs/en/workflows,external,code.claude.com,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Moves loop state and branching into workflow scripts so large tasks do not overload the conversation context.,Gives readers a concrete source in Orchestrate subagents at scale with dynamic workflows when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,488,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L488,,Design,design,Specify a loop contract and operating pattern.,context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/workflows,Orchestrate subagents at scale with dynamic workflows - Claude Code Docs,"Dynamic workflows orchestrate many subagents from a script Claude writes and you can rerun. Use them for codebase audits, large migrations, and cross-checked research.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0061,Core Loop Primitives,core-loop-primitives,Docs,📚,Create plugins,https://code.claude.com/docs/en/plugins,external,code.claude.com,"Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: Packaging model-invoked skills, agents, hooks, MCP servers, monitors, and settings as shareable loop components.",Gives readers a concrete source in Create plugins when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,489,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L489,,Design,design,Specify a loop contract and operating pattern.,whole-loop,builder,official-documentation,ok,https://code.claude.com/docs/en/plugins,Create plugins - Claude Code Docs,"Create custom plugins to extend Claude Code with skills, agents, hooks, and MCP servers.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0062,Core Loop Primitives,core-loop-primitives,Docs,📚,Model Context Protocol,https://modelcontextprotocol.io/docs/getting-started/intro,external,modelcontextprotocol.io,Standard protocol for exposing tools and data sources to agent loops.,Standard protocol for exposing tools and data sources to agent loops.,Standard protocol for exposing tools and data sources to agent loops.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Standard protocol for exposing tools and data sources to agent loops.,Gives readers a concrete source in Model Context Protocol when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from modelcontextprotocol.io; use it for current product or standard behavior.,high,README.md,490,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L490,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,official-documentation,ok,https://modelcontextprotocol.io/docs/getting-started/intro,What is the Model Context Protocol (MCP)? - Model Context Protocol,,,,,,Model Context Protocol,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0063,Core Loop Primitives,core-loop-primitives,Docs,📚,Allowing GitHub Copilot CLI to work autonomously,https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot,external,docs.github.com,"Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Copilot CLI autopilot mode plus `/every` and `/after` scheduling, turning the CLI into an unattended loop that runs steps until a task is complete.",Gives readers a concrete source in Allowing GitHub Copilot CLI to work autonomously when they need to turn a recurring-agent idea into an explicit loop contract.,Primary official documentation from docs.github.com; use it for current product or standard behavior.,high,README.md,491,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L491,,Design,design,Specify a loop contract and operating pattern.,trigger,builder,official-documentation,ok,https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot,Allowing GitHub Copilot CLI to work autonomously - GitHub Docs,"The CLI's autopilot mode lets Copilot CLI work autonomously on a task, carrying out multiple steps until the task is complete.",,,,,GitHub Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0064,Core Loop Primitives,core-loop-primitives,Tool,🧰,opencode-scheduler,https://github.com/different-ai/opencode-scheduler,external,github.com,"OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.","Breaks loop design into operational primitives that can be combined across agents and runtimes. Resource-specific angle: OpenCode plugin that runs recurring agent jobs through OS-native schedulers (launchd on macOS, systemd on Linux), with workdir-scoped jobs, timeouts, and skipped ticks when the previous run is still active.",Gives readers a concrete source in opencode-scheduler when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (441 stars; 30 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,492,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L492,,Design,design,Specify a loop contract and operating pattern.,budget,builder,source-implementation,ok,https://github.com/different-ai/opencode-scheduler,GitHub - different-ai/opencode-scheduler: OpenCode plugin for scheduling recurring jobs using launchd (Mac) or systemd (Linux) · GitHub,OpenCode plugin for scheduling recurring jobs using launchd (Mac) or systemd (Linux) - different-ai/opencode-scheduler,,2026-01-04,2026,different-ai/opencode-scheduler,GitHub,,,,github-api,different-ai/opencode-scheduler,441,30,MIT,2026-01-04T03:04:58Z,2026-07-17T02:28:49Z,,2026-07-17T05:43:07+00:00
+ale-0065,Core Loop Primitives,core-loop-primitives,Tool,🧰,Agent-Loop-Skills,https://github.com/gaasher/Agent-Loop-Skills,external,github.com,"Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reusable verification-gated loops (autoresearch, scientific writing, data analysis, code and prompt optimization, red-teaming) packaged as open-standard Agent Skills, each with a feedback signal, run ledger, and termination conditions.",Gives readers a concrete source in Agent-Loop-Skills when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (133 stars; 15 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,493,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L493,,Design,design,Specify a loop contract and operating pattern.,verification;exit,builder,source-implementation,ok,https://github.com/gaasher/Agent-Loop-Skills,"GitHub - gaasher/Agent-Loop-Skills: Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts. · GitHub","Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts. - gaasher/Agent-Loop-Skills",,2026-06-15,2026,gaasher/Agent-Loop-Skills,GitHub,,,,github-api,gaasher/Agent-Loop-Skills,133,15,MIT,2026-06-15T02:01:33Z,2026-07-17T01:28:31Z,,2026-07-17T05:43:07+00:00
+ale-0066,Core Loop Primitives,core-loop-primitives,Tool,🧰,launch-your-agent,https://github.com/anthropics/launch-your-agent,external,github.com,"Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.","Primary-source operational guidance rather than commentary. Resource-specific angle: Anthropic's official Claude Code skill set that operationalizes an interview, launch, grade-against-definition-of-done, iterate, and schedule loop for Claude Managed Agents, leaving a live recurring scheduled agent plus an eval scaffold and roadmap.",Gives readers a concrete source in launch-your-agent when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (808 stars; 154 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,494,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L494,,Design,design,Specify a loop contract and operating pattern.,trigger;verification;exit,builder,source-implementation,ok,https://github.com/anthropics/launch-your-agent,"GitHub - anthropics/launch-your-agent: Claude Code skills that take a founder from idea to a live Claude Managed Agent: interview, scope a v0, launch in their own account, grade it, iterate, and schedule it · GitHub","Claude Code skills that take a founder from idea to a live Claude Managed Agent: interview, scope a v0, launch in their own account, grade it, iterate, and schedule it - anthropics/launch-your-agent",,2026-06-16,2026,anthropics/launch-your-agent,GitHub,,,,github-api,anthropics/launch-your-agent,808,154,Apache-2.0,2026-06-16T14:49:50Z,2026-07-17T03:04:34Z,,2026-07-17T05:43:07+00:00
+ale-0067,Official Runtime Guides,official-runtime-guides,Docs,📚,Run long horizon tasks with Codex,https://developers.openai.com/blog/run-long-horizon-tasks-with-codex,external,developers.openai.com,"OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: OpenAI's runbook for plan-edit-test-observe-repair-document-repeat work, including specs, plans, status logs, and validation gates.",Gives readers a concrete source in Run long horizon tasks with Codex when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,507,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L507,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,official-documentation,ok,https://developers.openai.com/blog/run-long-horizon-tasks-with-codex,Run long horizon tasks with Codex | OpenAI Developers,OpenAI Developer Blog,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0068,Official Runtime Guides,official-runtime-guides,Docs,📚,Best practices - ChatGPT Learn,https://learn.chatgpt.com/guides/best-practices,external,learn.chatgpt.com,"Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official best practices for context, `AGENTS.md`, MCP, skills, subagents, and automations.",Gives readers a concrete source in Best practices - ChatGPT Learn when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from learn.chatgpt.com; use it for current product or standard behavior.,high,README.md,508,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L508,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation,builder,official-documentation,ok,https://learn.chatgpt.com/guides/best-practices,Best practices | ChatGPT Learn,Getting started with Codex and proven practices for better results,,,,,ChatGPT Learn,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0069,Official Runtime Guides,official-runtime-guides,Docs,📚,Agents SDK,https://developers.openai.com/api/docs/guides/agents,external,developers.openai.com,"OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: OpenAI guide for agent orchestration, tool execution, approvals, state, guardrails, and observability.",Gives readers a concrete source in Agents SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,509,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L509,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;state;escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents,Agents SDK | OpenAI API,Learn how the OpenAI Agents SDK fits together and which docs to read next.,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0070,Official Runtime Guides,official-runtime-guides,Docs,📚,Agents - OpenAI Agents SDK,https://openai.github.io/openai-agents-python/agents/,external,openai.github.io,"SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: SDK primitives for agents, tools, handoffs, guardrails, and runner-managed loops.",Gives readers a concrete source in Agents - OpenAI Agents SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from openai.github.io; use it for current product or standard behavior.,high,README.md,510,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L510,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,official-documentation,ok,https://openai.github.io/openai-agents-python/agents/,Agents - OpenAI Agents SDK,,,,,,openai.github.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0071,Official Runtime Guides,official-runtime-guides,Docs,📚,Running agents,https://developers.openai.com/api/docs/guides/agents/running-agents,external,developers.openai.com,"OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: OpenAI guide to turns, state, approvals, sessions, and continuation in the SDK runtime loop.",Gives readers a concrete source in Running agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,511,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L511,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/running-agents,Running agents | OpenAI API,"Learn how to run agents, stream output, and choose the right conversation-state strategy in the OpenAI Agents SDK.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0072,Official Runtime Guides,official-runtime-guides,Docs,📚,Integrations and observability,https://developers.openai.com/api/docs/guides/agents/integrations-observability,external,developers.openai.com,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: OpenAI guide to MCP wiring and traces as the basis for debugging and evaluation loops.,Gives readers a concrete source in Integrations and observability when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,512,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L512,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/integrations-observability,Integrations and observability | OpenAI API,Learn how to integrate MCP into Agents SDK workflows and how to trace and debug runs.,,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0073,Official Runtime Guides,official-runtime-guides,Docs,📚,Sandbox Agents,https://developers.openai.com/api/docs/guides/agents/sandboxes,external,developers.openai.com,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Splits the harness control plane from the sandbox execution plane for long-running file and command work.,Gives readers a concrete source in Sandbox Agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,513,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L513,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/sandboxes,Sandbox Agents | OpenAI API,"Learn how sandboxes fit into Agents SDK workflows, when to use them, and how orchestration stays separate from execution.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0074,Official Runtime Guides,official-runtime-guides,Docs,📚,Guardrails and human review,https://developers.openai.com/api/docs/guides/agents/guardrails-approvals,external,developers.openai.com,Approval and validation boundaries for sensitive agent actions.,Approval and validation boundaries for sensitive agent actions.,Approval and validation boundaries for sensitive agent actions.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Approval and validation boundaries for sensitive agent actions.",Gives readers a concrete source in Guardrails and human review when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,514,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L514,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,official-documentation,ok,https://developers.openai.com/api/docs/guides/agents/guardrails-approvals,Guardrails and human review | OpenAI API,"Learn how to use guardrails and human review in the OpenAI Agents SDK for safer, more controlled workflows.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0075,Official Runtime Guides,official-runtime-guides,Blog,📝,ChatGPT Work and the Codex Desktop App,https://openai.com/index/chatgpt-for-your-most-ambitious-work/,external,openai.com,"OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: OpenAI's July 9, 2026 launch of ChatGPT Work, a GPT-5.6-powered agent that stays with a project for hours by breaking a goal into smaller steps and completing them independently across apps and files, alongside Codex merging into the ChatGPT desktop app with in-sidebar PR review and cross-repository projects, extending the long-horizon coding-agent loop pattern to general knowledge work.",Gives readers a concrete source in ChatGPT Work and the Codex Desktop App when they need to choose an implementation surface for repeatable agent work.,"Contextual source from openai.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,515,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L515,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective,builder,practitioner-analysis,restricted,https://openai.com/index/chatgpt-for-your-most-ambitious-work/,,,,,,,OpenAI,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0076,Official Runtime Guides,official-runtime-guides,Docs,📚,Building agents with the Claude Agent SDK,https://code.claude.com/docs/en/agent-sdk/overview.md,external,code.claude.com,"Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Claude SDK overview for tool-using agents, subagents, state, permissions, and streaming.",Gives readers a concrete source in Building agents with the Claude Agent SDK when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,520,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L520,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-sdk/overview.md,,,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0077,Official Runtime Guides,official-runtime-guides,Docs,📚,How the agent loop works,https://code.claude.com/docs/en/agent-sdk/agent-loop,external,code.claude.com,Official walkthrough of the inner agent loop that outer recurring loops build on.,Official walkthrough of the inner agent loop that outer recurring loops build on.,Official walkthrough of the inner agent loop that outer recurring loops build on.,Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of the inner agent loop that outer recurring loops build on.,Gives readers a concrete source in How the agent loop works when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,521,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L521,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/agent-sdk/agent-loop,How the agent loop works - Claude Code Docs,"Understand the message lifecycle, tool execution, context window, and architecture that power your SDK agents.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0078,Official Runtime Guides,official-runtime-guides,Docs,📚,Extend Claude with skills,https://code.claude.com/docs/en/skills,external,code.claude.com,Claude Code skill system for reusable loop instructions and assets.,Claude Code skill system for reusable loop instructions and assets.,Claude Code skill system for reusable loop instructions and assets.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Claude Code skill system for reusable loop instructions and assets.",Gives readers a concrete source in Extend Claude with skills when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,522,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L522,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/skills,Extend Claude with skills - Claude Code Docs,"Create, manage, and share skills to extend Claude's capabilities in Claude Code. Includes custom commands and bundled skills.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0079,Official Runtime Guides,official-runtime-guides,Docs,📚,Create custom subagents,https://code.claude.com/docs/en/sub-agents,external,code.claude.com,"Claude Code custom subagents with isolated context, model choice, and tool permissions.","Claude Code custom subagents with isolated context, model choice, and tool permissions.","Claude Code custom subagents with isolated context, model choice, and tool permissions.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Claude Code custom subagents with isolated context, model choice, and tool permissions.",Gives readers a concrete source in Create custom subagents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,523,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L523,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation,builder,official-documentation,ok,https://code.claude.com/docs/en/sub-agents,Create custom subagents - Claude Code Docs,Create and use specialized AI subagents in Claude Code for task-specific workflows and improved context management.,,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0080,Official Runtime Guides,official-runtime-guides,Docs,📚,Writing effective tools for AI agents,https://www.anthropic.com/engineering/writing-tools-for-agents,external,www.anthropic.com,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Anthropic's guidance on evaluating and improving tool specs using agentic loops and realistic tasks.",Gives readers a concrete source in Writing effective tools for AI agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,524,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L524,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,official-documentation,ok,https://www.anthropic.com/engineering/writing-tools-for-agents,Writing effective tools for AI agents—using AI agents \ Anthropic,Writing effective tools for AI agents—using AI agents,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0081,Official Runtime Guides,official-runtime-guides,Docs,📚,Introducing advanced tool use on the Claude Developer Platform,https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3,external,www.anthropic.com,"Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Tool search, programmatic tool calling, and tool-use examples for scaling large tool libraries without flooding context.",Gives readers a concrete source in Introducing advanced tool use on the Claude Developer Platform when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,525,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L525,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context,builder,official-documentation,ok,https://www.anthropic.com/engineering/advanced-tool-use?e45d281a_page=3,Introducing advanced tool use on the Claude Developer Platform \ Anthropic,"Claude can now discover, learn, and execute tools dynamically to enable agents that take action in the real world. Here’s how.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0082,Official Runtime Guides,official-runtime-guides,Docs,📚,Effective harnesses for long-running agents,https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents,external,www.anthropic.com,"Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Anthropic's guidance for agents that work across many context windows: durable progress artifacts, environment setup, and self-verification.",Gives readers a concrete source in Effective harnesses for long-running agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from www.anthropic.com; use it for current product or standard behavior.,high,README.md,526,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L526,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,official-documentation,ok,https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents,Effective harnesses for long-running agents \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0083,Official Runtime Guides,official-runtime-guides,Docs,📚,Claude Code best practices,https://code.claude.com/docs/en/best-practices,external,code.claude.com,Widely cited workflow guidance that underlies many recurring Claude Code loops.,Widely cited workflow guidance that underlies many recurring Claude Code loops.,Widely cited workflow guidance that underlies many recurring Claude Code loops.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Widely cited workflow guidance that underlies many recurring Claude Code loops.",Gives readers a concrete source in Claude Code best practices when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,527,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L527,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/best-practices,Best practices for Claude Code - Claude Code Docs,"Tips and patterns for getting the most out of Claude Code, from configuring your environment to scaling across parallel sessions.",,,,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0084,Official Runtime Guides,official-runtime-guides,Docs,📚,Claude Managed Agents: Scheduled Deployments and Vaults,https://claude.com/blog/whats-new-in-claude-managed-agents,external,claude.com,"Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Scheduled deployments for Claude Managed Agents, where each cron firing starts a fresh session to complete the task, plus environment-variable vaults that let sandboxed agents authenticate tools while the real secret attaches only at the network boundary.",Gives readers a concrete source in Claude Managed Agents: Scheduled Deployments and Vaults when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from claude.com; use it for current product or standard behavior.,high,README.md,528,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L528,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,official-documentation,ok,https://claude.com/blog/whats-new-in-claude-managed-agents,New in Claude Managed Agents: run agents on a schedule and store environment variables in vaults | Claude by Anthropic,Claude Managed Agents now supports scheduled deployments and vaults: run agents on a cron schedule and securely authenticate CLI tools and other services.,,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0085,Official Runtime Guides,official-runtime-guides,Blog,📝,Getting Started with Loops,https://claude.com/blog/getting-started-with-loops,external,claude.com,"Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official Claude Code team guide by Delba de Oliveira and Michael Segner (June 30, 2026) that defines loops as agents repeating cycles of work until a stop condition is met, categorizes turn-based, goal-based (/goal), time-based (/loop, /schedule), and proactive loops by trigger and stop criteria, and recommends encoding verification as skills with quantitative success criteria alongside token-spend management.",Gives readers a concrete source in Getting Started with Loops when they need to choose an implementation surface for repeatable agent work.,"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,529,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L529,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;trigger;verification;budget;exit,builder,practitioner-analysis,ok,https://claude.com/blog/getting-started-with-loops,Loop engineering: Getting started with loops | Claude by Anthropic,"Loop engineering with Anthropic's Claude Code: design turn-based, goal, time, and proactive agent loops that run to a stop condition.",,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0086,Official Runtime Guides,official-runtime-guides,Docs,📚,"Claude Code What's New, Week 28",https://code.claude.com/docs/en/whats-new/2026-w28,external,code.claude.com,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Weekly digest whose loop-integrity features include auto mode blocking tampering with session transcript files and confirmation prompts before destructive operations in unattended runs.","Gives readers a concrete source in Claude Code What's New, Week 28 when they need to choose an implementation surface for repeatable agent work.",Primary official documentation from code.claude.com; use it for current product or standard behavior.,high,README.md,530,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L530,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://code.claude.com/docs/en/whats-new/2026-w28,"Week 28 · July 6–10, 2026 - Claude Code Docs","Browse external sites from the Desktop app's built-in browser, run a full setup checkup with /doctor, and pick up auto mode transcript protections and agent view upgrades.",,,2026,,Claude Code Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0087,Official Runtime Guides,official-runtime-guides,Docs,📚,GitHub Agentic Workflows,https://github.github.com/gh-aw/,external,github.github.com,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.,"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Repository automation that runs coding agents in GitHub Actions on events or schedules with guardrails.",Gives readers a concrete source in GitHub Agentic Workflows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.github.com; use it for current product or standard behavior.,high,README.md,535,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L535,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://github.github.com/gh-aw/,Home | GitHub Agentic Workflows,Write repository automation workflows in natural language using markdown files and run them as GitHub Actions. Use AI agents with strong guardrails to automate your development workflow.,,,,,GitHub Agentic Workflows,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0088,Official Runtime Guides,official-runtime-guides,Docs,📚,Continuous AI,https://githubnext.com/projects/continuous-ai/,external,githubnext.com,"GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: GitHub Next's umbrella framing for CI/CD-style AI automation across the software lifecycle, the category that agentic workflows demonstrate.",Gives readers a concrete source in Continuous AI when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from githubnext.com; use it for current product or standard behavior.,high,README.md,536,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L536,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://githubnext.com/projects/continuous-ai/,Continuous AI,Exploring LLM-powered automation in platform-based software collaboration,,,,,githubnext.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0089,Official Runtime Guides,official-runtime-guides,Blog,📝,Automate repository tasks with GitHub Agentic Workflows,https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/,external,github.blog,"Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).","Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of writing Markdown-defined agentic workflows with guardrails for triage, QA, and docs chores, announced in the [technical preview changelog](https://github.blog/changelog/2026-02-13-github-agentic-workflows-are-now-in-technical-preview/).",Gives readers a concrete source in Automate repository tasks with GitHub Agentic Workflows when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,537,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L537,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,practitioner-analysis,ok,https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/,Automate repository tasks with GitHub Agentic Workflows - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.","Don Syme, Peli de Halleux",2026-02-13,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0090,Official Runtime Guides,official-runtime-guides,Blog,📝,Continuous AI in practice: What developers can automate today with agentic CI,https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/,external,github.blog,"Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Concrete agentic-CI automations available today, with recurring patterns for triage, review, and documentation upkeep.",Gives readers a concrete source in Continuous AI in practice: What developers can automate today with agentic CI when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,538,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L538,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,practitioner-analysis,ok,https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/,Continuous AI in practice: What developers can automate today with agentic CI - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning.,GitHub Staff,2026-02-05,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0091,Official Runtime Guides,official-runtime-guides,Docs,📚,About GitHub Copilot coding agent,https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent,external,docs.github.com,"GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: GitHub's autonomous coding agent: assign an issue, the agent works in an isolated Actions-powered workspace, and a reviewable pull request comes back.",Gives readers a concrete source in About GitHub Copilot coding agent when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.github.com; use it for current product or standard behavior.,high,README.md,539,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L539,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;workspace,builder,official-documentation,ok,https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-cloud-agent,About GitHub Copilot cloud agent - GitHub Docs,"Copilot can research a repository, create an implementation plan, and make code changes on a branch. You can review the diff, iterate, and create a pull request when you're ready.",,,,,GitHub Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0092,Official Runtime Guides,official-runtime-guides,Blog,📝,GitHub Copilot: Meet the new coding agent,https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/,external,github.blog,"Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Launch overview of the issue-to-PR delegation loop, including iteration on review feedback.",Gives readers a concrete source in GitHub Copilot: Meet the new coding agent when they need to choose an implementation surface for repeatable agent work.,"Contextual source from github.blog; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,540,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L540,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,practitioner-analysis,ok,https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/,GitHub Copilot: Meet the new coding agent - The GitHub Blog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Copilot has a new feature: a coding agent that can implement a task or issue, run in the background with GitHub Actions, and more.",Thomas Dohmke,2025-05-19,2025,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0093,Official Runtime Guides,official-runtime-guides,Docs,📚,GitHub Copilot for Jira Is Now Generally Available,https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/,external,github.blog,"General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: General availability of Copilot for Jira: delegate a Jira issue to the Copilot coding agent, monitor session progress inside the issue, and send follow-up instructions that continue the same draft pull request instead of starting a new one.",Gives readers a concrete source in GitHub Copilot for Jira Is Now Generally Available when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,541,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L541,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,official-documentation,ok,https://github.blog/changelog/2026-06-25-github-copilot-for-jira-is-now-generally-available/,GitHub Copilot for Jira is now generally available - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Copilot for Jira is now generally available. Since launching the public preview in March 2026, we have shipped a series of enhancements based on your feedback, including model selection,…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0094,Official Runtime Guides,official-runtime-guides,Docs,📚,Copilot Agent Session Streaming (Public Preview),https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/,external,github.blog,"Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Public preview that streams Copilot agent session activity, including prompts, responses, and tool calls, from cloud agents, the CLI, and IDEs to SIEM-compatible endpoints and a REST API, giving enterprises an audit trail for delegated agent work.",Gives readers a concrete source in Copilot Agent Session Streaming (Public Preview) when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,542,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L542,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,official-documentation,ok,https://github.blog/changelog/2026-07-02-copilot-agent-session-streaming-is-now-in-public-preview/,Copilot agent session streaming is now in public preview - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"GitHub Enterprise Cloud customers with enterprise managed users can now access GitHub Copilot agent session data across all Copilot clients, including: Cloud agents operating on github.com and data resident deployments…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0095,Official Runtime Guides,official-runtime-guides,Docs,📚,Security Reviews in the GitHub Copilot App,https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app,external,github.blog,"Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Changelog adding on-demand security reviews inside the Copilot app, so an agent's proposed changes can be scanned for vulnerabilities before they are merged.",Gives readers a concrete source in Security Reviews in the GitHub Copilot App when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from github.blog; use it for current product or standard behavior.,high,README.md,543,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L543,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://github.blog/changelog/2026-07-14-security-reviews-now-available-in-the-github-copilot-app/,Security reviews now available in the GitHub Copilot app - GitHub Changelog LinkedIn icon Instagram icon YouTube icon X icon TikTok icon Twitch icon GitHub icon,"You can now run a security review on your in-flight code changes directly from the GitHub Copilot app. The /security-review slash command is shipping in public preview, bringing the same…",,,2026,,The GitHub Blog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0096,Official Runtime Guides,official-runtime-guides,Docs,📚,Cursor cloud agents,https://cursor.com/docs/cloud-agent,external,cursor.com,Remote agents that work asynchronously in isolated environments and hand results back for review.,Remote agents that work asynchronously in isolated environments and hand results back for review.,Remote agents that work asynchronously in isolated environments and hand results back for review.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Remote agents that work asynchronously in isolated environments and hand results back for review.",Gives readers a concrete source in Cursor cloud agents when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,548,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L548,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://cursor.com/docs/cloud-agent,Cloud Agents | Cursor Docs,Run Agent in the cloud for continuous coding assistance.,,,,,Cursor Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0097,Official Runtime Guides,official-runtime-guides,Docs,📚,Cursor 3.8: Improvements to Cursor Automations,https://cursor.com/changelog/06-18-26,external,cursor.com,"Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Cursor 3.8 changelog introducing an /automate skill that configures an automation's triggers, instructions, and tools from a plain-language description, plus Slack emoji-reaction and five new GitHub event triggers for dispatching cloud agents.",Gives readers a concrete source in Cursor 3.8: Improvements to Cursor Automations when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,549,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L549,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,official-documentation,ok,https://cursor.com/changelog/06-18-26,Improvements to Cursor Automations · Cursor,,,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0098,Official Runtime Guides,official-runtime-guides,Blog,📝,Expanding Our Long-Running Agents Research Preview,https://cursor.com/blog/long-running-agents,external,cursor.com,"Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Cursor's research preview of days-long autonomous agents gated by an upfront human-approved plan and cross-checked by multiple agents, reporting merge rates comparable to standard agents on runs as large as 52 hours and 151k lines.",Gives readers a concrete source in Expanding Our Long-Running Agents Research Preview when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,550,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L550,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,practitioner-analysis,ok,https://cursor.com/blog/long-running-agents,Expanding our long-running agents research preview · Cursor,"Long-running agents are now available in the Cursor web app for Ultra, Teams, and Enterprise users.",Cursor Team,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0099,Official Runtime Guides,official-runtime-guides,Docs,📚,"Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks",https://cursor.com/changelog/side-chat,external,cursor.com,"Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Cursor changelog adding cloud-agent hooks (before-submit, after-response, after-thought, stop, and subagent-start) that the platform pitches for gating and observing background agent runs.","Gives readers a concrete source in Cursor 3.11: Side Chats, Transcript Search, and Cloud Agent Hooks when they need to choose an implementation surface for repeatable agent work.",Primary official documentation from cursor.com; use it for current product or standard behavior.,high,README.md,551,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L551,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;exit,builder,official-documentation,ok,https://cursor.com/changelog/side-chat,Side Chats and Conversation Search · Cursor,,,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0100,Official Runtime Guides,official-runtime-guides,Docs,📚,Jules,https://jules.google/docs,external,jules.google,"Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Google's asynchronous coding agent that plans, executes tasks in isolated cloud VMs, and returns reviewable diffs.",Gives readers a concrete source in Jules when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from jules.google; use it for current product or standard behavior.,high,README.md,556,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L556,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://jules.google/docs,Getting started | Jules,Set up and run your first task with Jules,,,,,Jules,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0101,Official Runtime Guides,official-runtime-guides,Docs,📚,Devin Docs,https://docs.devin.ai/get-started/devin-intro,external,docs.devin.ai,"Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.","Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Documentation for a long-running autonomous software engineer with sessions, playbooks, knowledge, and review boundaries.",Gives readers a concrete source in Devin Docs when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.devin.ai; use it for current product or standard behavior.,high,README.md,557,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L557,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,official-documentation,ok,https://docs.devin.ai/get-started/devin-intro,Introducing Devin - Devin Docs,"Devin is the AI software engineer, built to help ambitious engineering teams crush their backlogs.",,,,,Devin Docs,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0102,Official Runtime Guides,official-runtime-guides,Blog,📝,"Amp: Agents, Anywhere",https://ampcode.com/news/agents-anywhere,external,ampcode.com,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.,"Shows how production platforms expose loops through concrete tools, permissions, skills, agents, and automation features. Resource-specific angle: Amp launches remote agent creation on any machine with shell access plus a headless runner mode that lets multiple agents run concurrently without a terminal UI.","Gives readers a concrete source in Amp: Agents, Anywhere when they need to choose an implementation surface for repeatable agent work.","Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,558,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L558,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation;state,builder,practitioner-analysis,ok,https://ampcode.com/news/agents-anywhere,Amp,Remotely start agents anywhere you can run 'amp',,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0103,Research Foundations,research-foundations,Paper,📄,ReAct: Synergizing Reasoning and Acting in Language Models,https://arxiv.org/abs/2210.03629,external,arxiv.org,Foundational reason-act-observe loop for tool-using language agents.,Foundational reason-act-observe loop for tool-using language agents.,Foundational reason-act-observe loop for tool-using language agents.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Foundational reason-act-observe loop for tool-using language agents.,"Gives readers a concrete source in ReAct: Synergizing Reasoning and Acting in Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2210.03629; inspect its method and evaluation before treating results as production evidence.,medium,README.md,566,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L566,,Learn,learn,Understand the field and its boundaries.,workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2210.03629,[2210.03629] ReAct: Synergizing Reasoning and Acting in Language Models,"While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io",Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du; Izhak Shafran; Karthik Narasimhan; Yuan Cao,2022-10-06,2022,arXiv,arXiv,,v3 is the ICLR camera ready version with some typos fixed. Project site with code: https://react-lm.github.io,cs.CL,arxiv-api,,,,,,,2210.03629,2026-07-17T05:43:07+00:00
+ale-0104,Research Foundations,research-foundations,Paper,📄,Reflexion: Language Agents with Verbal Reinforcement Learning,https://arxiv.org/abs/2303.11366,external,arxiv.org,Converts environment feedback into written reflections stored in memory for future attempts.,Converts environment feedback into written reflections stored in memory for future attempts.,Converts environment feedback into written reflections stored in memory for future attempts.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Converts environment feedback into written reflections stored in memory for future attempts.,"Gives readers a concrete source in Reflexion: Language Agents with Verbal Reinforcement Learning when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2303.11366; inspect its method and evaluation before treating results as production evidence.,medium,README.md,567,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L567,,Learn,learn,Understand the field and its boundaries.,context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2303.11366,[2303.11366] Reflexion: Language Agents with Verbal Reinforcement Learning,"Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning. We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback. Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials. Reflexion is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals, and obtains significant improvements over a baseline agent across diverse tasks (sequential decision-making, coding, language reasoning). For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%. We also conduct ablation and analysis studies using different feedback signals, feedback incorporation methods, and agent types, and provide insights into how they affect performance.",Noah Shinn; Federico Cassano; Edward Berman; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao,2023-03-20,2023,arXiv,arXiv,,v4 contains a few additional experiments,cs.AI,arxiv-api,,,,,,,2303.11366,2026-07-17T05:43:07+00:00
+ale-0105,Research Foundations,research-foundations,Paper,📄,Self-Refine: Iterative Refinement with Self-Feedback,https://arxiv.org/abs/2303.17651,external,arxiv.org,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Generate-feedback-refine loop where a model improves outputs over repeated passes.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Generate-feedback-refine loop where a model improves outputs over repeated passes.,"Gives readers a concrete source in Self-Refine: Iterative Refinement with Self-Feedback when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2303.17651; inspect its method and evaluation before treating results as production evidence.,medium,README.md,568,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L568,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2303.17651,[2303.17651] Self-Refine: Iterative Refinement with Self-Feedback,"Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses it to refine itself, iteratively. Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs. Across all evaluated tasks, outputs generated with Self-Refine are preferred by humans and automatic metrics over those generated with the same LLM using conventional one-step generation, improving by ~20% absolute on average in task performance. Our work demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using our simple, standalone approach.",Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark,2023-03-30,2023,arXiv,arXiv,,"Code, data, and demo at https://selfrefine.info/",cs.CL,arxiv-api,,,,,,,2303.17651,2026-07-17T05:43:07+00:00
+ale-0106,Research Foundations,research-foundations,Paper,📄,CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,https://arxiv.org/abs/2305.11738,external,arxiv.org,Uses tools to ground critique and correction rather than relying only on introspection.,Uses tools to ground critique and correction rather than relying only on introspection.,Uses tools to ground critique and correction rather than relying only on introspection.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Uses tools to ground critique and correction rather than relying only on introspection.,"Gives readers a concrete source in CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.11738; inspect its method and evaluation before treating results as production evidence.,medium,README.md,569,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L569,,Learn,learn,Understand the field and its boundaries.,workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.11738,[2305.11738] CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,"Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially ""black boxes"" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.",Zhibin Gou; Zhihong Shao; Yeyun Gong; Yelong Shen; Yujiu Yang; Nan Duan; Weizhu Chen,2023-05-19,2023,arXiv,arXiv,,ICLR 2024,cs.CL,arxiv-api,,,,,,,2305.11738,2026-07-17T05:43:07+00:00
+ale-0107,Research Foundations,research-foundations,Paper,📄,Tree of Thoughts,https://arxiv.org/abs/2305.10601,external,arxiv.org,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Search over multiple reasoning branches; relevant when loop design needs exploration before committing.,"Gives readers a concrete source in Tree of Thoughts when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.10601; inspect its method and evaluation before treating results as production evidence.,medium,README.md,570,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L570,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.10601,[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models,"Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.",Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Thomas L. Griffiths; Yuan Cao; Karthik Narasimhan,2023-05-17,2023,arXiv,arXiv,,NeurIPS 2023 camera ready version. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm,cs.CL,arxiv-api,,,,,,,2305.10601,2026-07-17T05:43:07+00:00
+ale-0108,Research Foundations,research-foundations,Paper,📄,Graph of Thoughts,https://arxiv.org/abs/2308.09687,external,arxiv.org,"Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Generalizes thought structures beyond chains and trees, useful for complex loop planning and aggregation.","Gives readers a concrete source in Graph of Thoughts when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2308.09687; inspect its method and evaluation before treating results as production evidence.,medium,README.md,571,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L571,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2308.09687,[2308.09687] Graph of Thoughts: Solving Elaborate Problems with Large Language Models,"We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (""LLM thoughts"") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.",Maciej Besta; Nils Blach; Ales Kubicek; Robert Gerstenberger; Michal Podstawski; Lukas Gianinazzi; Joanna Gajda; Tomasz Lehmann; Hubert Niewiadomski; Piotr Nyczyk; Torsten Hoefler,2023-08-18,2023,Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI'24),arXiv,10.1609/aaai.v38i16.29720,,cs.CL,arxiv-api,,,,,,,2308.09687,2026-07-17T05:43:07+00:00
+ale-0109,Research Foundations,research-foundations,Paper,📄,Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models,https://arxiv.org/abs/2310.04406,external,arxiv.org,"Combines search, action, and environment feedback for language agents.","Combines search, action, and environment feedback for language agents.","Combines search, action, and environment feedback for language agents.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Combines search, action, and environment feedback for language agents.","Gives readers a concrete source in Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2310.04406; inspect its method and evaluation before treating results as production evidence.,medium,README.md,572,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L572,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2310.04406,[2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models,"While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leveraging the in-context learning ability of LMs, we integrate Monte Carlo Tree Search into LATS to enable LMs as agents, along with LM-powered value functions and self-reflections for proficient exploration and enhanced decision-making. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that surpasses the constraints of existing techniques. Our experimental evaluation across diverse domains, including programming, interactive question-answering (QA), web navigation, and math, validates the effectiveness and generality of LATS in decision-making while maintaining competitive or improved reasoning performance. Notably, LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5. Code can be found at https://github.com/lapisrocks/LanguageAgentTreeSearch",Andy Zhou; Kai Yan; Michal Shlapentokh-Rothman; Haohan Wang; Yu-Xiong Wang,2023-10-06,2023,arXiv,arXiv,,Code at https://github.com/lapisrocks/LanguageAgentTreeSearch,cs.AI,arxiv-api,,,,,,,2310.04406,2026-07-17T05:43:07+00:00
+ale-0110,Research Foundations,research-foundations,Paper,📄,Voyager: An Open-Ended Embodied Agent with Large Language Models,https://arxiv.org/abs/2305.16291,external,arxiv.org,"Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Demonstrates lifelong skill acquisition through iterative exploration, feedback, and a skill library.","Gives readers a concrete source in Voyager: An Open-Ended Embodied Agent with Large Language Models when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2305.16291; inspect its method and evaluation before treating results as production evidence.,medium,README.md,573,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L573,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2305.16291,[2305.16291] Voyager: An Open-Ended Embodied Agent with Large Language Models,"We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.",Guanzhi Wang; Yuqi Xie; Yunfan Jiang; Ajay Mandlekar; Chaowei Xiao; Yuke Zhu; Linxi Fan; Anima Anandkumar,2023-05-25,2023,arXiv,arXiv,,Project website and open-source codebase: https://voyager.minedojo.org/,cs.AI,arxiv-api,,,,,,,2305.16291,2026-07-17T05:43:07+00:00
+ale-0111,Research Foundations,research-foundations,Paper,📄,Generative Agents: Interactive Simulacra of Human Behavior,https://arxiv.org/abs/2304.03442,external,arxiv.org,Introduces reflection and memory mechanisms for long-running agent behavior.,Introduces reflection and memory mechanisms for long-running agent behavior.,Introduces reflection and memory mechanisms for long-running agent behavior.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Introduces reflection and memory mechanisms for long-running agent behavior.,"Gives readers a concrete source in Generative Agents: Interactive Simulacra of Human Behavior when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2304.03442; inspect its method and evaluation before treating results as production evidence.,medium,README.md,574,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L574,,Learn,learn,Understand the field and its boundaries.,context;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2304.03442,[2304.03442] Generative Agents: Interactive Simulacra of Human Behavior,"Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.",Joon Sung Park; Joseph C. O'Brien; Carrie J. Cai; Meredith Ringel Morris; Percy Liang; Michael S. Bernstein,2023-04-07,2023,arXiv,arXiv,,,cs.HC,arxiv-api,,,,,,,2304.03442,2026-07-17T05:43:07+00:00
+ale-0112,Research Foundations,research-foundations,Paper,📄,Measuring AI Ability to Complete Long Software Tasks,https://arxiv.org/abs/2503.14499,external,arxiv.org,"METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: METR's task-length time horizon metric; grounds why loop budgets, checkpoints, and escalation matter as autonomous work gets longer.","Gives readers a concrete source in Measuring AI Ability to Complete Long Software Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2503.14499; inspect its method and evaluation before treating results as production evidence.,medium,README.md,575,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L575,,Learn,learn,Understand the field and its boundaries.,state;budget;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2503.14499,[2503.14499] Measuring AI Ability to Complete Long Software Tasks,"Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can complete with 50% success rate. We first timed humans with relevant domain expertise on a combination of RE-Bench, HCAST, and 66 novel shorter tasks. On these tasks, current frontier AI models such as Claude 3.7 Sonnet have a 50% time horizon of around 50 minutes. Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024. The increase in AI models' time horizons seems to be primarily driven by greater reliability and ability to adapt to mistakes, combined with better logical reasoning and tool use capabilities. We discuss the limitations of our results -- including their degree of external validity -- and the implications of increased autonomy for dangerous capabilities. If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month.",Thomas Kwa; Ben West; Joel Becker; Amy Deng; Katharyn Garcia; Max Hasin; Sami Jawhar; Megan Kinniment; Nate Rush; Sydney Von Arx; Ryan Bloom; Thomas Broadley; Haoxing Du; Brian Goodrich; Nikola Jurkovic; Luke Harold Miles; Seraphina Nix; Tao Lin; Chris Painter; Neev Parikh; David Rein; Lucas Jun Koba Sato; Hjalmar Wijk; Daniel M. Ziegler; Elizabeth Barnes; Lawrence Chan,2025-03-18,2025,NeurIPS 2025,arXiv,,"v4: added Chris Painter as listed author, consistent with listing in the pdf",cs.AI,arxiv-api,,,,,,,2503.14499,2026-07-17T05:43:07+00:00
+ale-0113,Research Foundations,research-foundations,Blog,📝,Measuring AI Ability to Complete Long Tasks,https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/,external,metr.org,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Accessible summary of the 50% task-completion time horizon and its doubling trend.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Accessible summary of the 50% task-completion time horizon and its doubling trend.,"Gives readers a concrete source in Measuring AI Ability to Complete Long Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,576,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L576,,Learn,learn,Understand the field and its boundaries.,exit,builder,practitioner-analysis,ok,https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/,Measuring AI Ability to Complete Long Tasks - METR Substack twitter Bluesky,"We propose measuring AI performance in terms of the *length* of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks.",,2025-03-19,2025,METR Blog,metr.org,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0114,Research Foundations,research-foundations,Paper,📄,Reflection-Driven Control for Trustworthy Code Agents,https://arxiv.org/abs/2512.21354,external,arxiv.org,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Elevates reflection from an external pass to an internal control loop that monitors the agent's decision path during generation and constrains risky steps with low overhead.,"Gives readers a concrete source in Reflection-Driven Control for Trustworthy Code Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2512.21354; inspect its method and evaluation before treating results as production evidence.,medium,README.md,577,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L577,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2512.21354,[2512.21354] Reflection-Driven Control for Trustworthy Code Agents,"Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs. To address this, we introduce Reflection-Driven Control, a standardized and pluggable control module that can be seamlessly integrated into general agent architectures. Reflection-Driven Control elevates ""self-reflection"" from a post hoc patch into an explicit step in the agent's own reasoning process: during generation, the agent continuously runs an internal reflection loop that monitors and evaluates its own decision path. When potential risks are detected, the system retrieves relevant repair examples and secure coding guidelines from an evolving reflective memory, injecting these evidence-based constraints directly into subsequent reasoning steps. We instantiate Reflection-Driven Control in the setting of secure code generation and systematically evaluate it across eight classes of security-critical programming tasks. Empirical results show that Reflection-Driven Control substantially improves the security and policy compliance of generated code while largely preserving functional correctness, with minimal runtime and token overhead. Taken together, these findings indicate that Reflection-Driven Control is a practical path toward trustworthy AI coding agents: it enables designs that are simultaneously autonomous, safer by construction, and auditable.",Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao; Ivor Tsang,2025-12-22,2025,arXiv,arXiv,,Accepted to AAAI 2026 Workshop on Trust and Control in Agentic AI (TrustAgent),cs.CR,arxiv-api,,,,,,,2512.21354,2026-07-17T05:43:07+00:00
+ale-0115,Research Foundations,research-foundations,Paper,📄,Hyperagents,https://arxiv.org/abs/2603.19461,external,arxiv.org,"Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","Gives readers a concrete source in Hyperagents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence.,medium,README.md,578,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L578,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.19461,[2603.19461] Hyperagents,"Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\""odel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.",Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina,2026-03-19,2026,arXiv,arXiv,,Code at https://github.com/facebookresearch/Hyperagents,cs.AI,arxiv-api,,,,,,,2603.19461,2026-07-17T05:43:07+00:00
+ale-0116,Research Foundations,research-foundations,Paper,📄,PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks,https://arxiv.org/abs/2512.03549,external,arxiv.org,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.,"Gives readers a concrete source in PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence.,medium,README.md,579,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L579,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2512.03549,[2512.03549] PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks,"We introduce PARC, a coding agent for the autonomous and robust execution of long-horizon computational tasks. PARC is built on a hierarchical multi-agent architecture incorporating task planning, execution, and a mechanism that evaluates its own actions and their outcomes from an independent context and provides feedback, namely self-assessment and self-feedback. This design enables PARC to detect and correct high-level strategic errors and sustain progress without human intervention. We evaluate PARC across computational science and data science tasks. In materials science, it autonomously reproduces key results from studies on lithium-ion conduction and alloy segregation. In particular, it coordinates dozens of parallel simulation tasks, each requiring roughly 43 hours of computation, managing orchestration, monitoring, and error correction end-to-end. In Kaggle-based experiments, starting from minimal natural-language instructions, PARC conducts data analysis and implements search strategies, producing solutions competitive with human-engineered baselines. These results highlight the potential of integrating a hierarchical multi-agent system with self-assessment and self-feedback to enable AI systems capable of independent, large-scale scientific and analytical work.",Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara,2025-12-03,2025,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2512.03549,2026-07-17T05:43:07+00:00
+ale-0117,Research Foundations,research-foundations,Paper,📄,When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents,https://arxiv.org/abs/2603.17104,external,arxiv.org,"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","Gives readers a concrete source in When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence.,medium,README.md,580,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L580,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.17104,[2603.17104] When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents,"Current coding-agent benchmarks usually pro- vide the full task specification upfront. Real research coding often does not: the intended system is progressively disclosed through in- teraction, requiring the agent to track durable design commitments across a long session. We introduce a benchmark for this setting and study faithfulne Ss Loss U nder eM ergent s Pecification (SLUMP), defined as the reduc- tion in final implementation faithfulness un- der emergent specification relative to a single- shot specification control. The benchmark con- tains 20 recent ML papers (10 ICML 2025, 10 NeurIPS 2025), 371 atomic verifiable compo- nents, and interaction scripts of approximately 60 coding requests that progressively disclose the target design without revealing the paper itself. Final repositories are scored with a five-level component-faithfulness rubric and accompanied by an exposure audit to verify that scored components are recoverable from the visible interaction. Evaluated on Claude Code and Codex, the single-shot specification control achieves higher overall implementation fidelity on 16/20 and 14/20 papers, respectively. Structural integration degrades under emergent specification on both platforms, while seman- tic faithfulness loss is substantial on Claude Code and small on Codex. As a mitigation case study, we introduce ProjectGuard, an exter- nal project-state layer for specification tracking. On Claude Code, ProjectGuard recovers 90% of the faithfulness gap, increases fully faith- ful components from 118 to 181, and reduces severe failures from 72 to 49. These results identify specification tracking as a distinct eval- uation target for long-horizon coding agents.",Lu Yan; Xuan Chen; Xiangyu Zhang,2026-03-17,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2603.17104,2026-07-17T05:43:07+00:00
+ale-0118,Research Foundations,research-foundations,Tool,🧰,Reflexion code,https://github.com/noahshinn/reflexion,external,github.com,Reference implementation and experiments for verbal reinforcement loops.,Reference implementation and experiments for verbal reinforcement loops.,Reference implementation and experiments for verbal reinforcement loops.,Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Reference implementation and experiments for verbal reinforcement loops.,"Gives readers a concrete source in Reflexion code when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Inspectable GitHub source (3,205 stars; 312 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,581,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L581,,Learn,learn,Understand the field and its boundaries.,whole-loop,builder,source-implementation,ok,https://github.com/noahshinn/reflexion,GitHub - noahshinn/reflexion: [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning · GitHub,[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning - noahshinn/reflexion,,2023-03-22,2023,noahshinn/reflexion,GitHub,,,,github-api,noahshinn/reflexion,3205,312,MIT,2023-03-22T06:38:53Z,2026-07-16T12:01:06Z,,2026-07-17T05:43:07+00:00
+ale-0119,Research Foundations,research-foundations,Paper,📄,Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting,https://arxiv.org/abs/2607.00038,external,arxiv.org,"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","Gives readers a concrete source in Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence.,medium,README.md,582,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L582,,Learn,learn,Understand the field and its boundaries.,objective;trigger;context;verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00038,[2607.00038] Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting,"In mid-2026 a slogan reorganized how practitioners talk about coding agents: stop prompting your agent, start designing the loop that prompts it. We take this claim seriously and give it a careful treatment. We call the object of the new practice the loop specification: a bounded, reusable artifact, made of a trigger, a goal, a verification step, a stopping rule and a memory, that a human hands to an agent harness (such as Claude Code or Codex) so the agent pursues a goal on its own, in place of step-by-step prompting. We distinguish this external loop specification from two things it is often confused with: an ordinary programming loop, and the internal perceive-act-observe cycle that the harness already provides as plumbing. We position loop engineering as a new layer in the progression from prompt to context to harness to loop, and we argue, against the stronger headlines, that it does not retire prompt engineering; loop and prompt are distinct tools with distinct uses. We offer four contributions: a definition and scope for the discipline; an anatomy and taxonomy of loop specifications organized around trigger, goal type, a five-level verification ladder, architecture, and named terminal states; a descriptive analysis of the Loop Library, a public corpus of fifty real loops that we code by hand; and a set of design principles and anti-patterns grounded in the scientific literature on self-correction, reward hacking and model-as-judge fragility. The corpus shows that practice has matured most where the discipline says it matters: seventy percent of loops verify in the autonomous zone of the ladder and seventy-four percent name their terminal states, while automated triggering and durable memory remain comparatively underdeveloped. We close with the limits the practice must respect, including the verification burden, comprehension debt and the risk of cognitive surrender.",Sandeco Macedo,2026-06-28,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.00038,2026-07-17T05:43:07+00:00
+ale-0120,Research Foundations,research-foundations,Paper,📄,From Question Answering to Task Completion: A Survey on Agent System and Harness Design,https://arxiv.org/abs/2606.20683,external,arxiv.org,"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","Gives readers a concrete source in From Question Answering to Task Completion: A Survey on Agent System and Harness Design when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence.,medium,README.md,583,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L583,,Learn,learn,Understand the field and its boundaries.,context;verification;state;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.20683,[2606.20683] From Question Answering to Task Completion: A Survey on Agent System and Harness Design,"LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. As agent systems have evolved from prompt engineering to workflows and context engineering, harness engineering, and agent-native training with co-evolution, a central question has become increasingly important: where does the bottleneck in agent performance reside, in the foundation model, in the execution harness, or in the coupling between them? This survey examines LLM-based agents through a model-harness lens. We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model coupled with an execution harness. We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities: observation, context, control, action, state, and verification. Using this decomposition, we map task properties and domain pressures to harness configurations, review benchmark and evaluation practices, and synthesize model-harness evidence on how runtime design affects long-horizon task completion, efficiency, and reliability. Finally, we identify open challenges in value-aware evaluation, safety, harness generalization, and model-harness co-evolution. Rather than treating agents as models with auxiliary tools, this survey argues that agent quality -- including success, efficiency, safety, and generalization -- emerges from the interaction between model capability, runtime infrastructure, task structure, and evaluation design. A collection of papers discussed in this survey is provided in https://github.com/ggjy/Awesome-Agent-Engineering.",Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang,2026-06-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.20683,2026-07-17T05:43:07+00:00
+ale-0121,Research Foundations,research-foundations,Paper,📄,MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems,https://arxiv.org/abs/2605.22794,external,arxiv.org,"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","Gives readers a concrete source in MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence.,medium,README.md,584,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L584,,Learn,learn,Understand the field and its boundaries.,verification;state;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.22794,[2605.22794] MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems,"Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifacts -- skill files, prompt configurations, memory schemas, workflow graphs -- and leave the agent harness untouched. Since routing, hook ordering, state invariants, and dispatch live in code rather than in any text artifact, an entire class of structural failure is physically unreachable from the text layer. We argue that source-level adaptation is a fundamentally more general medium: it is Turing-complete, a strict superset of every text-mutable scope, takes effect deterministically rather than through base-model compliance, and does not erode under long-context drift. We present MOSS, a system that performs self-rewriting at the source level on production agentic substrates. Each evolution is anchored to an automatically curated batch of production-failure evidence and proceeds through a deterministic multi-stage pipeline; code modification is delegated to a pluggable external coding-agent CLI while MOSS retains stage ordering and verdicts. Candidates are verified by replaying the batch against the candidate image in ephemeral trial workers, then promoted via user-consent-gated, in-place container swap with health-probe-gated rollback. On OpenClaw, MOSS lifts a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention.",Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo,2026-05-21,2026,arXiv,arXiv,,"12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss",cs.AI,arxiv-api,,,,,,,2605.22794,2026-07-17T05:43:07+00:00
+ale-0122,Research Foundations,research-foundations,Blog,📝,METR Time Horizon 1.1,https://metr.org/blog/2026-1-29-time-horizon-1-1/,external,metr.org,"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","Gives readers a concrete source in METR Time Horizon 1.1 when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,585,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L585,,Learn,learn,Understand the field and its boundaries.,whole-loop,builder,practitioner-analysis,ok,https://metr.org/blog/2026-1-29-time-horizon-1-1/,Time Horizon 1.1 - METR Substack twitter Bluesky,"We’re releasing a new version of our time horizon estimates (TH1.1), using more tasks and a new eval infrastructure.",,2026-01-29,2026,METR Blog,metr.org,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0123,Research Foundations,research-foundations,Paper,📄,MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution,https://arxiv.org/abs/2607.05297,external,arxiv.org,"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","Gives readers a concrete source in MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence.,medium,README.md,586,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L586,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05297,[2607.05297] MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution,"Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability. However, a fixed, hand-authored skill is rarely optimal, and cannot adapt to the diversity of tasks an agent encounters. Self-improving agents address this by rewriting their own skill files from execution traces, yielding meaningful gains on challenging benchmarks. Yet such self-evolution remains non-recursive: it improves only the task skill (what the agent does) while the improvement procedure (how it improves) is authored once and held fixed. We introduce MetaSkill-Evolve, a two-timescale framework that makes agentic skill improvement recursive: every branch carries both a task skill $s$ and a branch-local meta-skill $m=(\psi,\sigma,\alpha,\pi,\varepsilon)$ whose five components parameterise the Analyzer, Retriever, Allocator, Proposer, and Evolver agents of the improvement pipeline. Task skills evolve on a fast loop while the meta-skill evolves on a slower one under the same pipeline applied to itself, with no additional model or objective. With all five pipeline agents sharing a single frozen backbone, MetaSkill-Evolve outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks (OfficeQA, SealQA, ALFWorld), improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.",Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma,2026-07-06,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.05297,2026-07-17T05:43:07+00:00
+ale-0124,Research Foundations,research-foundations,Paper,📄,SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe,https://arxiv.org/abs/2607.03451,external,arxiv.org,"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","Gives readers a concrete source in SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence.,medium,README.md,587,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L587,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03451,[2607.03451] SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe,"While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optimization via Zeroth-Order (ZO) optimization, mapping classical counterparts (central difference, trust regions) to recent literature. Noting that unlike blind numerical perturbations in classical ZO, skill trajectories serve as interpretable debugging feedback. Grounded in Claude Code philosophy and PAC learning, we establish three principles for convergence and generalization: file-system-based trajectory exploration, consensus attribute mining, and independent validation gating. Eliminating redundancies, we propose SkillOpt-Lite. It accelerates convergence and outperforms full SkillOpt: improving LiveMath by +8.8 points on GPT-5.5 and +25.4 points on GPT-5.4-nano, allowing the nano model to surpass standard GPT-5.4 optimized by SkillOpt. Finally, we integrate our framework into production coding agents like VSCode Copilot, enabling developers to evolve agent skills via one line of vibe. Because our framework treats all agent components simply as standard editable code, this minimal pipeline naturally generalizes to full harness optimization (HarnessOpt). On SpreadsheetBench, HarnessOpt enables GPT-5.4-nano to achieve 0.7758 accuracy, outperforming the larger GPT-5.5 running standard pipelines (0.7620). Code is available at https://github.com/EvolvingLMMs-Lab/SkillOpt-Lite.",Yifei Shen; Bo Li; Xinjie Zhang,2026-07-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.03451,2026-07-17T05:43:07+00:00
+ale-0125,Research Foundations,research-foundations,Paper,📄,Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops,https://arxiv.org/abs/2607.07663,external,arxiv.org,"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","Gives readers a concrete source in Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence.,medium,README.md,588,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L588,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07663,[2607.07663] Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops,"AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself. This literature is described under a vocabulary (""self-refine,"" ""self-reward,"" ""self-play,"" ""self-evolve"") that conflates fundamentally different ambitions. We survey 1,250 arXiv papers (2024-2026) along two axes: what the system improves -- its behavior in deployment, its policy through training, its evaluator, or the research process itself -- and the degree of loop closure (human-in-the-loop to fully closed). The taxonomy separates bounded self-refinement -- convergent, evaluable, and already industrial practice -- from open-ended recursive self-improvement (RSI), which remains bounded by grounding requirements, collapse dynamics, and compute constraints on every measured axis. Its distinctive feature is a dedicated category for self-evaluation: every improvement loop is a claim that some signal can substitute for human judgment. We survey the evaluator design space -- judges, process reward models, verifiers, rubrics, meta-evaluation -- order the signals into a verification hierarchy from formal verifiers (strongest) to intrinsic self-assessment (weakest), and observe that demonstrated self-improvement strength tracks this hierarchy, that its failure modes (self-confirming loops, model collapse, diversity collapse) follow from its violations, and that the ""research direction-setting"" bottleneck keeping humans in the loop sits at the top of that hierarchy. We connect the technical literature to the theory of RSI limits and to the safety and governance questions raised by frontier-lab accounts of closing the loop, and identify governance-grade measurement of self-improvement as the field's most underpopulated niche.",Mingguang Chen; Licheng Wang; Bo Qu,2026-07-08,2026,arXiv,arXiv,,"42 pages, 6 figures",cs.AI,arxiv-api,,,,,,,2607.07663,2026-07-17T05:43:07+00:00
+ale-0126,Research Foundations,research-foundations,Paper,📄,From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents,https://arxiv.org/abs/2607.07321,external,arxiv.org,"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","Gives readers a concrete source in From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence.,medium,README.md,589,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L589,,Learn,learn,Understand the field and its boundaries.,workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07321,[2607.07321] From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents,"Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to reinvent low-level logic for every recurring workflow, leading to increased reasoning overhead and failure rates. In this study, we propose that agents can achieve self-evolution by synthesizing these atomic actions into reusable Standard Operating Procedures (SOPs), which function as callable higher-order tools that encapsulate multi-step logic. We further introduce EvoSOP, a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset through a systematic lifecycle of construction, merging, evaluation, and pruning. Extensive experiments demonstrate that EvoSOP significantly boosts task success rates while substantially reducing the number of interaction rounds compared to baselines. Our analysis also reveals that iterative tool optimization fosters reliable and efficient tool-use patterns, providing a scalable pathway for the development of self-evolving agents.",Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07321,2026-07-17T05:43:07+00:00
+ale-0127,Research Foundations,research-foundations,Paper,📄,TTHE: Test-Time Harness Evolution,https://arxiv.org/abs/2607.08124,external,arxiv.org,"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","Gives readers a concrete source in TTHE: Test-Time Harness Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence.,medium,README.md,590,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L590,,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08124,[2607.08124] TTHE: Test-Time Harness Evolution,"The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time. This limits adaptation when the test distribution, failure modes, or tool interactions differ from those seen during development. We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs. We introduce Test-Time Harness Evolution (TTHE), which treats the executable harness as the state of test-time adaptation. During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces, without gold labels or task-specific supervision; a judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs. Crucially, TTHE does not update model weights, require gold labels, or train a separate adaptation model: solver, proposers, and judge are different roles and harnesses around the same frozen LLM, so all adaptation occurs through changes to the surrounding program. Across text-to-SQL, competitive programming, software engineering, data-science coding, and agentic tool-use tasks, TTHE improves fixed ReAct-style baseline harnesses, yielding persistent, inspectable improvements rather than a pre-searched workflow or per-query retries. These results recast test-time adaptation for LLM agents as evolution over executable control programs and identify execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.",Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han,2026-07-09,2026,arXiv,arXiv,,"15 pages, 5 figures",cs.SE,arxiv-api,,,,,,,2607.08124,2026-07-17T05:43:07+00:00
+ale-0128,Research Foundations,research-foundations,Paper,📄,DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment,https://arxiv.org/abs/2607.07820,external,arxiv.org,"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","Gives readers a concrete source in DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence.,medium,README.md,591,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L591,,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07820,[2607.07820] DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment,"Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.",Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. Fung,2026-07-08,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.07820,2026-07-17T05:43:07+00:00
+ale-0129,Research Foundations,research-foundations,Paper,📄,What Makes a Good Bug Report for an AI Agent?,https://arxiv.org/abs/2607.07593,external,arxiv.org,"Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","Gives readers a concrete source in What Makes a Good Bug Report for an AI Agent? when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence.,medium,README.md,592,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L592,,Learn,learn,Understand the field and its boundaries.,intake,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07593,[2607.07593] What Makes a Good Bug Report for an AI Agent?,"Automated program repair (APR) agents are transitioning from research benchmarks to developer workflows, yet they still begin with bug reports written for human developers. While decades of research have established what makes a good bug report for humans (e.g., steps to reproduce, stack traces), it remains unclear whether these features transfer to LLM-based agents. We study this question in two analyses. First, we use statistical modeling to examine associations between 27 bug-report features and repair success across 433 SWE-bench Verified issues attempted by 87 repair agents. We find that fix suggestions, reproduction scripts, repository source code, and localization info are associated with higher resolution likelihood, while longer reports are associated with lower odds. Second, we conduct controlled ablations across 2 models and 17 problem-statement mutations on SWE-bench Pro, varying the information available to an agent while holding the underlying task fixed. We remove or isolate selected bug-report content, delete fault-localization cues, and test structural changes that flatten lists or remove section headers. We find that both models depend on localization cues and expected behavior, and that structural changes alone can reduce solve rates, even without removing any content. The two models diverge in how they handle missing information: Qwen searches more widely and can exhaust its turn budget, while Gemma commits to a plausible interpretation early and patches on it. Our findings indicate that a good bug report for an agent overlaps with, but is not identical to, a good report for a human: agents benefit most from concrete, executable, and well-localized information, whereas some qualities long emphasized for human readers, such as natural language steps to reproduce and readable descriptions, contribute little or even correlate with lower success.",Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07593,2026-07-17T05:43:07+00:00
+ale-0130,Research Foundations,research-foundations,Paper,📄,AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution,https://arxiv.org/abs/2607.08252,external,arxiv.org,"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","Gives readers a concrete source in AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08252; inspect its method and evaluation before treating results as production evidence.,medium,README.md,593,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L593,,Learn,learn,Understand the field and its boundaries.,verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08252,[2607.08252] AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution,"Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace this failure to model-level convergence toward high-probability behavioral channels and system-level context gravity from State, memory, history, and environment summaries. We introduce AutoPersonas, a multi-timescale life-environment engine for bounded persona-level recursive self-evolution. It separates environment-side Occurrences, accumulated Observations, and persona State. Its OSO loop admits divergent future-facing material while requiring evidence-governed absorption before State or reachability changes. A three-year compressed simulation exposed environment watermark shells, occurrence-hardening gaps, slow-change accumulation failures, recursive indecision, and weak relationship persistence. An eight-model 40-day stress test generated 1,600 events and found mean rolling 5-day action-category repetition of 95.2%-97.6%, with all models crossing 90% by day 11. Semantic re-keeping found 79.0%-88.0% macro-theme repetition across all direct-loop runs. In a same-runtime 40-day A/B, context-slice masking plus per-sample divergence targeting reduced macro-theme repetition from 61.8% to 36.3% and roughly doubled cumulative theme count. A juvenile-goblin fictional-world run reproduced the anti-fixation regime without hard real-world intrusions. These results support a bounded claim: separating controlled divergence from evidence-governed absorption can reduce persona-environment self-locking while preserving identity continuity.",Mengchen Li,2026-07-09,2026,arXiv,arXiv,,"52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included",cs.AI,arxiv-api,,,,,,,2607.08252,2026-07-17T05:43:07+00:00
+ale-0131,Research Foundations,research-foundations,Paper,📄,Agentic Data Environments,https://arxiv.org/abs/2607.07397,external,arxiv.org,"Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Vision paper from the IEEE Data Engineering Bulletin reframing data systems as the active execution environment agents operate in, spanning files, APIs, applications, and system state, arguing the substrate under recurring agent loops should both amplify agent capability and enforce safety guarantees that bound the cost of failure.","Gives readers a concrete source in Agentic Data Environments when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.07397; inspect its method and evaluation before treating results as production evidence.,medium,README.md,594,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L594,,Learn,learn,Understand the field and its boundaries.,state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07397,[2607.07397] Agentic Data Environments,"Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While databases remain central to modern computing, agents operate over a broader data environment spanning files, APIs, applications, and system state. In this talk, I will outline early work on Agentic Data Environments -- the execution substrate in which agents operate -- that both amplify agent capabilities and enforce safety guarantees. This perspective reframes data systems from passive stores of state into active substrates for safe, reliable execution.",Elaine Ang; Chenxi Huang; Georgios Liargkovas; Jerry Liu; Jinhui Liu; Nikos Pagonas; Charlie Summers; Haonan Wang; Jiakai Xu; Tianle Zhou; Yusen Zhang; Zhou Yu; Zhuo Zhang; Tianyi Peng; Kostis Kaffes; Eugene Wu,2026-07-08,2026,IEEE Data Bulletin Vol. 50 No. 1 2026,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07397,2026-07-17T05:43:07+00:00
+ale-0132,Research Foundations,research-foundations,Paper,📄,"Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation",https://arxiv.org/abs/2607.08938,external,arxiv.org,"Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Meta agent maps observed failure modes to harness adaptation strategies, letting small-model agents recover ~90% of frontier-LLM performance at ~4% of the cost, the harness itself becomes the optimization target.","Gives readers a concrete source in Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.08938; inspect its method and evaluation before treating results as production evidence.,medium,README.md,595,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L595,,Learn,learn,Understand the field and its boundaries.,budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08938,"[2607.08938] Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation","Frontier LLM agents are automating many business tasks, but their high inference cost makes large-scale deployment unsustainable. Small language models (SLMs) offer a cheaper alternative, yet they typically fall short when swapped into a harness designed for a frontier LLM. We show that for many routine business tasks, SLM agents can match LLM performance at 90% lower cost, when paired with an adapted harness that can be automatically discovered by a meta agent. The key insight is that much of the task difficulty is shared across instances and can be lifted from the model into the harness via tailored instructions, tools, and orchestration loops. To study this systematically, we create a framework that maps agent failure modes to harness adaptation strategies, and build a harness optimizer that automatically discovers effective adaptations from failure trajectories. Across seven business-oriented agentic tasks and three SLM families, we found optimized harnesses significantly improve performance on 16 of 21 task-SLM pairs, with seven pairs closing the SLM-LLM performance gap and the best SLM agent recovering 89.7% of LLM performance at 4% of the cost. Our analysis further shows that adaptation works best for tasks with more repetitive workflows and for SLMs with sufficient base capabilities. Together, these results suggest that harness adaptation can expand the practical deployment range of SLM agents in routine business tasks.",Chenyang Yang; Xinran Zhao; Tongshuang Wu; Christian Kästner,2026-07-09,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.08938,2026-07-17T05:43:07+00:00
+ale-0133,Research Foundations,research-foundations,Paper,📄,Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills,https://arxiv.org/abs/2607.09065,external,arxiv.org,"First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: First large-scale empirical study of public agent-skill repositories and marketplaces, characterizing which software-engineering activities get packaged as reusable skills, their coverage across the development lifecycle, how they evolve, and how they are evaluated - an activity-centric map of the skills layer that agent loops compose (Cao, Cheung, et al., HKUST).","Gives readers a concrete source in Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.09065; inspect its method and evaluation before treating results as production evidence.,medium,README.md,596,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L596,,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09065,[2607.09065] Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills,"Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances in AI agents have introduced a potentially new reusable artifact: skills. Emerging agent skill repositories and marketplaces enable developers to package, share, and reuse SE expertise as reusable skills. This trend raises a fundamental question: what SE activities are being encapsulated into reusable skills? Existing studies primarily focus on a broad range of skills acquisition, safety, or benchmarking, while lacking a systematic understanding of SE-specific skills and their coverage across the software development lifecycle. To address this gap, we conduct the first large-scale empirical study of SE skills in public repositories and marketplaces. We collect and analyze a large corpus of SE skills, examining the activities they encapsulate, lifecycle coverage, evolution characteristics, and evaluation mechanisms. Our findings reveal that SE activities are increasingly becoming reusable artifacts via skills and suggest promising research opportunities for skill recommendation and engineering-oriented structuring, as well as the need for mechanisms to encapsulate high-context SE activities into reusable skills. Overall, our study provides the first activity-centric characterization of SE skills and reveals how SE activities are increasingly being transformed into reusable skills. These findings offer new insights into skill reuse, ecosystem development, and the future of agent-centric SE.",Jialun Cao; Xinru Yan; Songqiang Chen; Yaojie Lu; Zhongxin Liu; Shing-Chi Cheung,2026-07-10,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.09065,2026-07-17T05:43:07+00:00
+ale-0134,Research Foundations,research-foundations,Blog,📝,Harness Engineering for Self-Improvement,https://lilianweng.github.io/posts/2026-07-04-harness/,external,lilianweng.github.io,"Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Lilian Weng's deep-dive arguing the harness, the system surrounding a base model that orchestrates execution, matters as much as raw intelligence for recursive self-improvement, with a taxonomy of harness components and failure modes.","Gives readers a concrete source in Harness Engineering for Self-Improvement when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","Contextual source from lilianweng.github.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,597,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L597,,Learn,learn,Understand the field and its boundaries.,delegation,builder,practitioner-analysis,ok,https://lilianweng.github.io/posts/2026-07-04-harness/,Harness Engineering for Self-Improvement | Lil'Log,"The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights directly, or more broadly the model improves the training pipeline and the deployment system, which in turn enables a better successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI).",Lilian Weng,2026-07-04,2026,,lilianweng.github.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0135,Research Foundations,research-foundations,Paper,📄,"Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime",https://arxiv.org/abs/2607.11346,external,arxiv.org,"Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Compiles safety-critical standard operating procedures into executable pseudo-code run by a program-guided stack machine that pages the active frame while the LLM does semantic execution, finding runtime guidance is capability-gated (it helps strong models and harms weak ones) across a six-model, seven-domain SOPBench study.","Gives readers a concrete source in Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.11346; inspect its method and evaluation before treating results as production evidence.,medium,README.md,598,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L598,2026-07-15,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11346,"[2607.11346] Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents","Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM performs semantic execution. A three-arm SOPBench study across six models separates representation from runtime: compiled text never significantly hurts and gains up to 16.0 points where official prose underperforms. Runtime guidance is capability-gated. Two strong models independently show positive seven-domain PG contrasts (58:19 and 75:31 discordant pairs), whereas weak models are harmed. A full-program cursor ablation (active frame first, complete program retained) recovers much of the strong-model refusal gain; selective visibility adds a smaller improvement. Paired probe and audit measurements track this divide to spontaneous state discipline rather than reconstruction ability. On Bank the three primary arms rise from 70.4 to 86.4 to 92.8, with 100% refusal correctness. Practical guidance: compile first; enable active-frame paging only after a model-level discipline check.",Chenglin Yu; Li Yin; Ying Yu; Qingxin Fan; RunyangRay Zhong; Hongxia Yang; Ming Li,2026-07-13,2026,arXiv,arXiv,,"9 pages, 3 figures, 5 tables",cs.AI,arxiv-api,,,,,,,2607.11346,2026-07-17T05:43:07+00:00
+ale-0136,Research Foundations,research-foundations,Paper,📄,Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation,https://arxiv.org/abs/2607.11288,external,arxiv.org,"Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Industrial self-evolving agentic OS that treats exploit capability as a mutable, versioned kernel: the agent observes its own failures, synthesizes new capabilities, proves them against a live target, and hot-loads them back, a self-improvement loop with in-loop verification.","Gives readers a concrete source in Mako: A Self-Evolving Agentic Operating System for Autonomous Web Exploitation when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.11288; inspect its method and evaluation before treating results as production evidence.,medium,README.md,599,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L599,2026-07-15,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11288,[2607.11288] Mako: A Self-Evolving Agentic Operating System (SE-AOS) for Autonomous Web Exploitation,"We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-loading them back into itself. Mako is the first SE-AOS instance for security research and the autonomous web exploitation engine developed within LaunchSafe. LaunchSafe builds autonomous security agents for continuous offensive testing and agent-driven security research; Mako is the core engine behind that platform. On the public XBOW validation-benchmarks, 104 containerised, CTF-style web applications spanning 26 vulnerability classes across three difficulty tiers, Mako achieves full-suite coverage: it drives every one of the 104 targets to emit a cryptographically fresh, per-build flag, under a verification regime that makes fabricated or memorised results impossible. Our central result is a law of autonomous exploitation: once a capability exists and is discoverable, difficulty collapses; capability, not reasoning, is what is scarce, together with an architecture and formalism that turn that law into a self-improving system. Mako further runs a gated self-evolution loop that proposes, sandboxes, and commits improvements to its own agents and rules when fitness does not regress. We deliberately withhold the operational results, payloads, exploit chains, and tool source, because a system that reduces full-spectrum web exploitation to a repeatable, machine-speed pipeline is dual-use research of concern. We publish the science; we withhold the weapon.",Praneeth Narisetty; Shiva Nagendra Babu Kore,2026-07-13,2026,arXiv,arXiv,,"13 pages, 10 figures, 8 tables",cs.CR,arxiv-api,,,,,,,2607.11288,2026-07-17T05:43:07+00:00
+ale-0137,Research Foundations,research-foundations,Paper,📄,How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,https://arxiv.org/abs/2607.10856,external,arxiv.org,"Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Mixed-methods study of how practitioners actually design software-engineering agents, surfacing the recurring loop, harness, and verification decisions teams make and where their mental models diverge from benchmark assumptions.","Gives readers a concrete source in How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.10856; inspect its method and evaluation before treating results as production evidence.,medium,README.md,600,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L600,2026-07-15,Learn,learn,Understand the field and its boundaries.,verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10856,[2607.10856] How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,"The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little is known about how developers build these systems in practice: existing studies mine repositories or examine deployment, but few investigate how SE agents are constructed. Through semi-structured interviews with 20 practitioners from 12 organizations and an online survey of 80 practitioners, this paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face. We find that as implementation becomes cheaper, bottlenecks shift rather than disappear: long-standing non-coding work such as requirements, coordination, review, and deployment becomes more visible, while reviewing and evaluating agent output becomes new and central. We characterize a seven-stage workflow and a shift toward evaluation-driven development, in which evaluation steers iteration and specifications become versioned artifacts read by both humans and agents. We further identify six challenges that teams face, together with the practices they adopt to address them, including unreliable evaluation signals, comprehension debt as code outpaces understanding, and behavioral changes introduced by provider-side model updates.",Yunbo Lyu; David Williams; Jieke Shi; Zhensu Sun; Chao Peng; Zhou Yang; Federica Sarro; David Lo,2026-07-12,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.10856,2026-07-17T05:43:07+00:00
+ale-0138,Research Foundations,research-foundations,Paper,📄,Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,https://arxiv.org/abs/2607.10113,external,arxiv.org,"Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Survey and taxonomy of how agent skill libraries are created, evaluated, retired, and reused over time, organizing the fast-growing self-evolving-skills literature into a lifecycle framework.","Gives readers a concrete source in Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.",Research source arXiv:2607.10113; inspect its method and evaluation before treating results as production evidence.,medium,README.md,601,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L601,2026-07-15,Learn,learn,Understand the field and its boundaries.,whole-loop,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10113,[2607.10113] Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,"Large language model agents increasingly store reusable procedures outside the model. These reusable procedures are often called \emph{skills}: they may be code functions, natural-language instructions, SKILL.md packages, workflow graphs, or learned adapters that a future agent can retrieve and invoke. This taxonomy-driven survey asks how such skill libraries change over time. Across a $124$-paper $2023$--$2026$ audit set, we synthesize dynamic skill systems as \emph{lifecycle-managed, verified, evolving artifact stores}: agents collect evidence from interaction, propose skill updates, verify and admit candidates, organize them for retrieval and composition, repair or prune stale entries, and govern sharing through provenance and rollback. We organize the literature around three survey tools. First, a $\text{six}$-sense taxonomy distinguishes the structurally different artifacts called ``skills'' in current papers. Second, an $\text{eight}$-stage lifecycle architecture identifies the recurring design decisions behind evidence acquisition, proposal, verification/admission, storage, retrieval/composition, maintenance, distillation/portability, and governance. Third, a lightweight skill-record schema and $\text{ten}$-operator vocabulary provide common terms for comparing library updates without elevating them into a separate method contribution. Using this structure, we synthesize evidence-graded patterns with explicit caveats: admission and repair are repeatedly important, verifier quality materially affects skill-aware RL, flat retrieval can degrade as libraries grow, and current benchmarks still under-report library trajectories, usage--utility gaps, and safety surfaces. We close with concrete reporting standards and open problems for evaluating dynamic skills as changing libraries rather than static prompt or tool collections.",Yubo Li,2026-07-11,2026,"Transactions on Machine Learning Research, 2026",arXiv,,"Accepted by TMLR (2026.07), OpenReview Link: https://openreview.net/forum?id=cjU3YbcRr8",cs.AI,arxiv-api,,,,,,,2607.10113,2026-07-17T05:43:07+00:00
+ale-0139,Agent Workflow Patterns,agent-workflow-patterns,Docs,📚,Building Effective Agents,https://www.anthropic.com/engineering/building-effective-agents,external,www.anthropic.com,"Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns.",Gives readers a concrete source in Building Effective Agents when they need to turn a recurring-agent idea into an explicit loop contract.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,609,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L609,,Design,design,Specify a loop contract and operating pattern.,delegation,builder,technical-documentation,ok,https://www.anthropic.com/engineering/building-effective-agents,Building Effective AI Agents \ Anthropic,"Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0140,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,"Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime",https://www.preprints.org/manuscript/202603.1756,external,www.preprints.org,"Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects.","Gives readers a concrete source in Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime when they need to turn a recurring-agent idea into an explicit loop contract.",Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,610,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L610,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,researcher;evaluator,research-paper,restricted,https://www.preprints.org/manuscript/202603.1756,,,Chaoyue He; Xin Zhou; Di Wang; Hong Xu; Wei Liu; Chunyan Miao,2026-04-23,2026,Preprints.org,Preprints.org,10.20944/preprints202603.1756.v2,Version 2; the primary source states that this preprint is not peer-reviewed.,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0141,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,How we built our multi-agent research system,https://www.anthropic.com/engineering/multi-agent-research-system,external,www.anthropic.com,"Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops.",Gives readers a concrete source in How we built our multi-agent research system when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,611,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L611,,Design,design,Specify a loop contract and operating pattern.,context;delegation,builder,practitioner-analysis,ok,https://www.anthropic.com/engineering/multi-agent-research-system,How we built our multi-agent research system \ Anthropic,On the the engineering challenges and lessons learned from building Claude's Research system,,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0142,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Building Effective AI Agents: Architecture Patterns and Implementation Frameworks,https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf,external,resources.anthropic.com,"PDF overview of agent architecture patterns, including generator-evaluator loops.","PDF overview of agent architecture patterns, including generator-evaluator loops.","PDF overview of agent architecture patterns, including generator-evaluator loops.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: PDF overview of agent architecture patterns, including generator-evaluator loops.",Gives readers a concrete source in Building Effective AI Agents: Architecture Patterns and Implementation Frameworks when they need to turn a recurring-agent idea into an explicit loop contract.,Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,612,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L612,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,researcher;evaluator,research-paper,ok,https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf,,,Anthropic,2025-12-03,2025,Anthropic eBook,Anthropic,,Date verified from the primary PDF creation metadata.,,pdf-metadata,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0143,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,AI Agent Architectures,https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/,external,hld.handbook.academy,"System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies.",Gives readers a concrete source in AI Agent Architectures when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from hld.handbook.academy; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,613,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L613,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,practitioner-analysis,ok,https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/,"AI Agent Architectures (ReAct, Reflection, Planning, Tool Use, Memory) - The HLD Handbook","The canonical patterns for turning an LLM into an agent: ReAct's think-act-observe loop, reflection and self-critique, planner-executor decomposition, tool use and function calling, and how agents manage short- and long-term memory.",,,,,The HLD Handbook,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0144,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,What Are Agentic Workflows?,https://weaviate.io/blog/what-are-agentic-workflows,external,weaviate.io,"Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Accessible taxonomy of planning, tool use, reflection, and memory patterns.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Accessible taxonomy of planning, tool use, reflection, and memory patterns.",Gives readers a concrete source in What Are Agentic Workflows? when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from weaviate.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,614,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L614,,Design,design,Specify a loop contract and operating pattern.,workspace;context,builder,practitioner-analysis,ok,https://weaviate.io/blog/what-are-agentic-workflows,"What Are Agentic Workflows? Patterns, Memory, Use Cases, and Examples | Weaviate","Agentic workflows combine AI agents, tools, and agent memory to create adaptive systems. Learn the core patterns, use cases, and real-world examples.",,2025-03-06,2025,,weaviate.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0145,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,Agent Planning & Reflection Patterns,https://learnaivisually.com/tracks/ai-agents/planning-reflection,external,learnaivisually.com,"Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Visual explanation of plan-execute, observe, reflect, retry, and stop patterns.",Gives readers a concrete source in Agent Planning & Reflection Patterns when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from learnaivisually.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,615,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L615,,Design,design,Specify a loop contract and operating pattern.,budget;exit,builder,practitioner-analysis,ok,https://learnaivisually.com/tracks/ai-agents/planning-reflection,Agent Planning & Reflection Patterns | Learn AI Visually LAV LAV,"When agents should plan, retry, pause, or stop. Reasoning budget, ReAct, Reflexion, and termination logic — each tied to a 'when' decision.",,,,,Learn AI Visually,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0146,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,Agentic Design Patterns,https://addyosmani.com/agents/04-agentic-design-patterns/,external,addyosmani.com,"Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents.",Gives readers a concrete source in Agentic Design Patterns when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,616,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L616,,Design,design,Specify a loop contract and operating pattern.,workspace,builder,practitioner-analysis,ok,https://addyosmani.com/agents/04-agentic-design-patterns/,AddyOsmani.com - Lesson 4: agentic design patterns,"Addy Osmani is an engineering and evangelism leader who spent over 14 years at Google leading developer experience across Chrome and, in recent years, AI (Gemini, coding agents, and agentic engineering), most recently as a Director at Google Cloud AI.",Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0147,Agent Workflow Patterns,agent-workflow-patterns,Pattern,🔁,12 Factor Agents,https://github.com/humanlayer/12-factor-agents,external,github.com,"Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior.",Gives readers a concrete source in 12 Factor Agents when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (24,352 stars; 1,848 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,617,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L617,,Design,design,Specify a loop contract and operating pattern.,state,builder,operational-pattern,ok,https://github.com/humanlayer/12-factor-agents,GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? · GitHub,What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents,,2025-03-30,2025,humanlayer/12-factor-agents,GitHub,,,,github-api,humanlayer/12-factor-agents,24352,1848,NOASSERTION,2025-03-30T22:10:39Z,2026-07-17T04:31:16Z,,2026-07-17T05:43:07+00:00
+ale-0148,Agent Workflow Patterns,agent-workflow-patterns,Pattern,🔁,Durable Execution for Agentic Workflows,https://arizenai.com/durable-execution/,external,arizenai.com,"Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows.",Gives readers a concrete source in Durable Execution for Agentic Workflows when they need to turn a recurring-agent idea into an explicit loop contract.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,618,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L618,,Design,design,Specify a loop contract and operating pattern.,state,builder,operational-pattern,ok,https://arizenai.com/durable-execution/,Durable Execution for Agentic Workflows | Arizen,A while loop is at-most-once across process boundaries. Production agents need exactly-once. The architecture must encode the guarantee.,,2026-03-30,2026,,Arizen,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0149,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Code as Agent Harness,https://arxiv.org/abs/2605.18747,external,arxiv.org,"Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on.",Gives readers a concrete source in Code as Agent Harness when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2605.18747; inspect its method and evaluation before treating results as production evidence.,medium,README.md,619,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L619,,Design,design,Specify a loop contract and operating pattern.,delegation;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.18747,[2605.18747] Code as Agent Harness,"Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.",Xuying Ning; Katherine Tieu; Dongqi Fu; Tianxin Wei; Zihao Li; Yuanchen Bei; Jiaru Zou; Mengting Ai; Zhining Liu; Ting-Wei Li; Lingjie Chen; Yanjun Zhao; Ke Yang; Bingxuan Li; Cheng Qian; Gaotang Li; Xiao Lin; Zhichen Zeng; Ruizhong Qiu; Sirui Chen; Yifan Sun; Xiyuan Yang; Ruida Wang; Rui Pan; Chenyuan Yang; Dylan Zhang; Liri Fang; Zikun Cui; Yang Cao; Pan Chen; Dorothy Sun; Ren Chen; Mahesh Srinivasan; Nipun Mathur; Yinglong Xia; Hong Li; Hong Yan; Pan Lu; Lingming Zhang; Tong Zhang; Hanghang Tong; Jingrui He,2026-05-18,2026,arXiv,arXiv,,GitHub: https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers,cs.CL,arxiv-api,,,,,,,2605.18747,2026-07-17T05:43:07+00:00
+ale-0150,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Agentic Agile-V: From Vibe Coding to Verified Engineering,https://arxiv.org/abs/2605.20456,external,arxiv.org,"Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts.",Gives readers a concrete source in Agentic Agile-V: From Vibe Coding to Verified Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2605.20456; inspect its method and evaluation before treating results as production evidence.,medium,README.md,620,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L620,,Design,design,Specify a loop contract and operating pattern.,delegation;verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.20456,[2605.20456] Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development,"Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim that autonomous code generation automatically improves engineering outcomes. Controlled studies report productivity gains in some enterprise tasks, slowdowns in mature open-source work, moderate but heterogeneous meta-analytic effects, and persistent failures in repository setup, dependency handling, permission gating, and hardware verification. This paper argues that the central problem is no longer prompt engineering; it is engineering process control. It synthesizes evidence from agentic software engineering, GitHub-scale adoption studies, repository-level agent configuration, productivity trials, issue-resolution benchmarks, and hardware/RTL verification research. It proposes Agentic Agile-V, a process framework that uses Agile-V as the lifecycle backbone and a task-level SCOPE-V loop - Specify, Constrain, Orchestrate, Prove, Evolve, and Verify - to convert conversational intent into structured engineering artifacts and acceptance evidence. The paper contributes: (i) a taxonomy of minimum input artifacts for agentic software, firmware, and hardware work; (ii) a conversation-to-contract gate that separates exploratory dialogue from implementation; (iii) risk-adaptive feature, bug-fix, testing, and hardware workflows; and (iv) an evidence-bundle acceptance model for agent-generated artifacts. The paper concludes that agentic AI does not eliminate engineering discipline; it increases the value of requirements, constraints, traceability, independent verification, and human approval.",Christopher Koch,2026-05-19,2026,arXiv,arXiv,,"7 pages, 1 figure",cs.SE,arxiv-api,,,,,,,2605.20456,2026-07-17T05:43:07+00:00
+ale-0151,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,Agentic Software Engineering: Foundational Pillars and a Research Roadmap,https://arxiv.org/abs/2509.06216,external,arxiv.org,"Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside.",Gives readers a concrete source in Agentic Software Engineering: Foundational Pillars and a Research Roadmap when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2509.06216; inspect its method and evaluation before treating results as production evidence.,medium,README.md,621,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L621,,Design,design,Specify a loop contract and operating pattern.,delegation;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2509.06216,[2509.06216] Agentic Software Engineering: Foundational Pillars and a Research Roadmap,"Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Agentic SE era, comprising two symbiotic modalities: SE for Humans and SE for Agents. This duality demands a radical reimagining of the foundational pillars of SE (actors, processes, tools, and artifacts) which manifest differently across each modality. We propose two purpose-built workbenches to support this vision. The Agent Command Environment (ACE) serves as a command center where humans orchestrate and mentor agent teams, handling outputs such as Merge-Readiness Packs (MRPs) and Consultation Request Packs (CRPs). The Agent Execution Environment (AEE) is a digital workspace where agents perform tasks while invoking human expertise when facing ambiguity or complex trade-offs. This bi-directional partnership, which supports agent-initiated human callbacks and handovers, gives rise to new, structured engineering activities (i.e., processes) that redefine human-AI collaboration, elevating the practice from agentic coding to true agentic software engineering. This paper presents the Structured Agentic Software Engineering (SASE) vision, outlining several of the foundational pillars for the future of SE. The paper culminates in a research roadmap that identifies a few key challenges and opportunities while briefly discussing the resulting impact of this future on SE education. Our goal is not to offer a definitive solution, but to provide a conceptual scaffold with structured vocabulary to catalyze a community-wide dialogue, pushing the SE community to think beyond its classic, human-centric tenets toward a disciplined, scalable, and trustworthy agentic future.",Ahmed E. Hassan; Hao Li; Dayi Lin; Bram Adams; Tse-Hsun Chen; Yutaro Kashiwa; Dong Qiu,2025-09-07,2025,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2509.06216,2026-07-17T05:43:07+00:00
+ale-0152,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,The Art of Loop Engineering,https://www.langchain.com/blog/the-art-of-loop-engineering,external,www.langchain.com,"LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example.",Gives readers a concrete source in The Art of Loop Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,622,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L622,,Design,design,Specify a loop contract and operating pattern.,trigger;verification,builder,practitioner-analysis,ok,https://www.langchain.com/blog/the-art-of-loop-engineering,The Art of Loop Engineering,"Agents automate real-world work, but reliable performance requires more than a good model, it requires a carefully designed harness built for specific tasks. This post explores the core agent loop, how stacking and extending loops builds more effective agents, and how to instrument each level with LangChain primitives.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0153,Agent Workflow Patterns,agent-workflow-patterns,Tool,🧰,Loopy,https://github.com/Forward-Future/loopy,external,github.com,"Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows.",Gives readers a concrete source in Loopy when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (2,731 stars; 237 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,623,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L623,,Design,design,Specify a loop contract and operating pattern.,verification;exit,builder,source-implementation,ok,https://github.com/Forward-Future/loopy,"GitHub - Forward-Future/loopy: A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. · GitHub","A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. - Forward-Future/loopy",,2026-06-12,2026,Forward-Future/loopy,GitHub,,,,github-api,Forward-Future/loopy,2731,237,MIT,2026-06-12T14:44:22Z,2026-07-17T01:28:31Z,,2026-07-17T05:43:07+00:00
+ale-0154,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,The Factory Model: How Coding Agents Changed Software Engineering,https://addyosmani.com/blog/factory-model/,external,addyosmani.com,"Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running ""thirty minutes, an hour, several hours and increasingly days"" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay.",Gives readers a concrete source in The Factory Model: How Coding Agents Changed Software Engineering when they need to turn a recurring-agent idea into an explicit loop contract.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,624,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L624,,Design,design,Specify a loop contract and operating pattern.,verification,builder,practitioner-analysis,ok,https://addyosmani.com/blog/factory-model/,AddyOsmani.com - The Factory Model: How Coding Agents Changed Software Engineering,Software engineering is not about writing code anymore. It is about building the factory that builds your software.,Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0155,Agent Workflow Patterns,agent-workflow-patterns,Docs,📚,2026 Agentic Coding Trends Report,https://resources.anthropic.com/2026-agentic-coding-trends-report,external,resources.anthropic.com,"Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).","Primary-source operational guidance rather than commentary. Resource-specific angle: Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public).",Gives readers a concrete source in 2026 Agentic Coding Trends Report when they need to turn a recurring-agent idea into an explicit loop contract.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,625,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L625,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,builder,technical-documentation,ok,https://resources.anthropic.com/2026-agentic-coding-trends-report,2026 Agentic Coding Trends Report,"How coding agents are transforming software development - and what it means for engineering teams in 2026. Insights on multi-agent systems, human-AI collaboration, and scaling agentic coding across organizations. Includes case studies from Rakuten, TELUS, Zapier, and more.",,,2026,,Anthropic,,,,url-date,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0156,Agent Workflow Patterns,agent-workflow-patterns,Tool,🧰,HomeRail,https://github.com/xiaotianfotos/homerail,external,github.com,"TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end.",Gives readers a concrete source in HomeRail when they need to turn a recurring-agent idea into an explicit loop contract.,"Inspectable GitHub source (579 stars; 133 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,626,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L626,,Design,design,Specify a loop contract and operating pattern.,delegation;verification,builder,source-implementation,ok,https://github.com/xiaotianfotos/homerail,GitHub - xiaotianfotos/homerail: Voice-first local agent orchestration runtime for auditable DAG workflows. · GitHub,Voice-first local agent orchestration runtime for auditable DAG workflows. - xiaotianfotos/homerail,,2026-07-07,2026,xiaotianfotos/homerail,GitHub,,,,github-api,xiaotianfotos/homerail,579,133,MIT,2026-07-07T12:11:18Z,2026-07-17T05:43:04Z,,2026-07-17T05:43:07+00:00
+ale-0157,Agent Workflow Patterns,agent-workflow-patterns,Blog,📝,"Old and New Apps, via Modern Coding Agents",https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/,external,terrytao.wordpress.com,"Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is automated away, and the exchange was ""a net wash"" on code quality, he caught one minor bug in the agent's output while the agent found two bugs in his original code. Named-practitioner post; loop content is iterative-refinement rather than unattended loops, so it fits the practitioner-workflow section rather than core loop patterns.","Gives readers a concrete source in Old and New Apps, via Modern Coding Agents when they need to turn a recurring-agent idea into an explicit loop contract.","Contextual source from terrytao.wordpress.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,627,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L627,,Design,design,Specify a loop contract and operating pattern.,verification;escalation,builder,practitioner-analysis,ok,https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/,"Old and new apps, via modern coding agents | What's new","I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0, both for my complex analysis and linear alge…",,2026-07-11,2026,,What's new,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0158,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,"Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable",https://arxiv.org/abs/2607.13285,external,arxiv.org,"Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Distills reusable agent-control patterns that are not tied to a single vendor implementation. Resource-specific angle: Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use.","Gives readers a concrete source in Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable when they need to turn a recurring-agent idea into an explicit loop contract.",Research source arXiv:2607.13285; inspect its method and evaluation before treating results as production evidence.,medium,README.md,628,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L628,2026-07-17,Design,design,Specify a loop contract and operating pattern.,budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13285,"[2607.13285] Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable","The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modified. Before such a change can be made, a developer or coding agent must identify all code locations that implement the target behavior. This is difficult because production harnesses are large, tightly coupled, and behaviorally distributed, while modification requests describe what the system should do and repositories are organized by files and modules. Code search, repository indexing, and long-context processing ease inspection, but still leave this behavior-to-code mapping to be recovered by hand. Behavior localization is therefore a central bottleneck in harness evolution. We introduce the Harness Handbook, a behavior-centric representation synthesized automatically from a harness codebase via static analysis and LLM-assisted structuring, linking each behavior to its corresponding source. We also introduce Behavior-Guided Progressive Disclosure (BGPD), which guides agents from high-level behaviors to relevant implementation details and verifies candidate locations against the current source. On diverse modification requests from two open-source harnesses, Handbook-Assisted planning improves behavior localization and edit-plan quality while using fewer planner tokens, with the largest gains on scattered sites, rarely executed paths, and cross-module interactions. Evolving complex agentic systems thus depends not only on generating edits, but also on determining where those edits should be made.",Ruhan Wang; Yucheng Shi; Zongxia Li; Zhongzhi Li; Yue Yu; Junyao Yang; Kishan Panaganti; Haitao Mi; Dongruo Zhou; Leoweiliang,2026-07-14,2026,arXiv,arXiv,,"29 pages, 6 figures. Project page: https://ruhan-wang.github.io/Harness-Handbook/",cs.AI,arxiv-api,,,,,,,2607.13285,2026-07-17T05:43:07+00:00
+ale-0159,Agent Workflow Patterns,agent-workflow-patterns,Paper,📄,MemoHarness: Agent Harnesses That Learn from Experience,https://arxiv.org/abs/2607.14159,external,arxiv.org,"Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions.",Gives readers a concrete source in MemoHarness: Agent Harnesses That Learn from Experience when they need to turn a recurring-agent idea into an explicit loop contract.,Research source arXiv:2607.14159; inspect its method and evaluation before treating results as production evidence.,medium,README.md,629,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L629,2026-07-17,Design,design,Specify a loop contract and operating pattern.,context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14159,[2607.14159] MemoHarness: Agent Harnesses That Learn from Experience,"An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness into six editable control dimensions, stores per-case diagnoses and distilled global patterns in a dual-layer experience bank, and adapts the learned harness to each test case using retrieved experience without test-time labels, feedback, or additional search. In our evaluation across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness improves over the fixed harnesses we compare against and shows selective transfer to unseen suites and base models. Its additional context can also remain cost-competitive when much of the retrieved experience is cacheable. These results provide evidence that execution experience is a practical substrate for building agent harnesses that are more adaptive than a single static configuration, while leaving broader claims about statistical robustness and component attribution to future work.",Yue Huang; Wenjie Wang; Han Bao; Yuchen Ma; Xiaonan Luo; Yi Nian; Haomin Zhuang; Zheyuan Liu; Yue Zhao; Xiangliang Zhang,2026-07-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.14159,2026-07-17T05:43:07+00:00
+ale-0160,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,SWE-agent,https://github.com/SWE-agent/SWE-agent,external,github.com,Agent-computer interface and autonomous software engineering agent for repository tasks.,Agent-computer interface and autonomous software engineering agent for repository tasks.,Agent-computer interface and autonomous software engineering agent for repository tasks.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Agent-computer interface and autonomous software engineering agent for repository tasks.,Gives readers a concrete source in SWE-agent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (19,832 stars; 2,167 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,638,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L638,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/SWE-agent/SWE-agent,"GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] · GitHub","SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] - GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]",,2024-04-02,2024,SWE-agent/SWE-agent,GitHub,,,,github-api,SWE-agent/SWE-agent,19832,2167,MIT,2024-04-02T04:09:47Z,2026-07-17T04:00:43Z,,2026-07-17T05:43:07+00:00
+ale-0161,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,https://arxiv.org/abs/2405.15793,external,arxiv.org,Paper behind SWE-agent and its interface design.,Paper behind SWE-agent and its interface design.,Paper behind SWE-agent and its interface design.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Paper behind SWE-agent and its interface design.,Gives readers a concrete source in SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2405.15793; inspect its method and evaluation before treating results as production evidence.,medium,README.md,639,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L639,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2405.15793,[2405.15793] SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,"Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that LM agents represent a new category of end users with their own needs and abilities, and would benefit from specially-built interfaces to the software they use. We investigate how interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively, far exceeding the previous state-of-the-art achieved with non-interactive LMs. Finally, we provide insight on how the design of the ACI can impact agents' behavior and performance.",John Yang; Carlos E. Jimenez; Alexander Wettig; Kilian Lieret; Shunyu Yao; Karthik Narasimhan; Ofir Press,2024-05-06,2024,arXiv,arXiv,,"Code, data, and demo available at https://swe-agent.com",cs.SE,arxiv-api,,,,,,,2405.15793,2026-07-17T05:43:07+00:00
+ale-0162,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,mini-SWE-agent,https://mini-swe-agent.com/latest/,external,mini-swe-agent.com,Minimal coding agent that is useful for understanding the core loop without a large framework.,Minimal coding agent that is useful for understanding the core loop without a large framework.,Minimal coding agent that is useful for understanding the core loop without a large framework.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Minimal coding agent that is useful for understanding the core loop without a large framework.,Gives readers a concrete source in mini-SWE-agent when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,640,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L640,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,implementation,ok,https://mini-swe-agent.com/latest/,Overview - mini-SWE-agent documentation,,,,,,mini-swe-agent.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0163,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,OpenHands,https://github.com/All-Hands-AI/OpenHands,external,github.com,Open platform for AI software developers as generalist agents.,Open platform for AI software developers as generalist agents.,Open platform for AI software developers as generalist agents.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Open platform for AI software developers as generalist agents.,Gives readers a concrete source in OpenHands when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (81,043 stars; 10,361 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,641,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L641,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/OpenHands/OpenHands,GitHub - OpenHands/OpenHands: 🙌 OpenHands: AI-Driven Development · GitHub,🙌 OpenHands: AI-Driven Development. Contribute to OpenHands/OpenHands development by creating an account on GitHub.,,2024-03-13,2024,All-Hands-AI/OpenHands,GitHub,,,,github-api,All-Hands-AI/OpenHands,81043,10361,NOASSERTION,2024-03-13T03:33:31Z,2026-07-17T04:50:17Z,,2026-07-17T05:43:07+00:00
+ale-0164,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,OpenHands: An Open Platform for AI Software Developers as Generalist Agents,https://arxiv.org/abs/2407.16741,external,arxiv.org,"Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation.",Gives readers a concrete source in OpenHands: An Open Platform for AI Software Developers as Generalist Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2407.16741; inspect its method and evaluation before treating results as production evidence.,medium,README.md,642,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L642,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.16741,[2407.16741] OpenHands: An Open Platform for AI Software Developers as Generalist Agents,"Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. In this paper, we introduce OpenHands (f.k.a. OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, coordination between multiple agents, and incorporation of evaluation benchmarks. Based on our currently incorporated benchmarks, we perform an evaluation of agents over 15 challenging tasks, including software engineering (e.g., SWE-BENCH) and web browsing (e.g., WEBARENA), among others. Released under the permissive MIT license, OpenHands is a community project spanning academia and industry with more than 2.1K contributions from over 188 contributors.",Xingyao Wang; Boxuan Li; Yufan Song; Frank F. Xu; Xiangru Tang; Mingchen Zhuge; Jiayi Pan; Yueqi Song; Bowen Li; Jaskirat Singh; Hoang H. Tran; Fuqiang Li; Ren Ma; Mingzhang Zheng; Bill Qian; Yanjun Shao; Niklas Muennighoff; Yizhe Zhang; Binyuan Hui; Junyang Lin; Robert Brennan; Hao Peng; Heng Ji; Graham Neubig,2024-07-23,2024,arXiv,arXiv,,Accepted by ICLR 2025; Code: https://github.com/All-Hands-AI/OpenHands,cs.SE,arxiv-api,,,,,,,2407.16741,2026-07-17T05:43:07+00:00
+ale-0165,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Agentless,https://github.com/OpenAutoCoder/Agentless,external,github.com,"Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Workflow-based approach for software issue resolution using localization, repair, and patch validation.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Workflow-based approach for software issue resolution using localization, repair, and patch validation.",Gives readers a concrete source in Agentless when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,083 stars; 235 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,643,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L643,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/OpenAutoCoder/Agentless,GitHub - OpenAutoCoder/Agentless: Agentless🐱: an agentless approach to automatically solve software development problems · GitHub,Agentless🐱: an agentless approach to automatically solve software development problems - OpenAutoCoder/Agentless,,2024-06-30,2024,OpenAutoCoder/Agentless,GitHub,,,,github-api,OpenAutoCoder/Agentless,2083,235,MIT,2024-06-30T23:37:17Z,2026-07-16T06:42:42Z,,2026-07-17T05:43:07+00:00
+ale-0166,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Agentless: Demystifying LLM-based Software Engineering Agents,https://arxiv.org/abs/2407.01489,external,arxiv.org,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Useful contrast case: strong results through structured workflow rather than a fully open-ended agent.,Gives readers a concrete source in Agentless: Demystifying LLM-based Software Engineering Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2407.01489; inspect its method and evaluation before treating results as production evidence.,medium,README.md,644,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L644,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.01489,[2407.01489] Agentless: Demystifying LLM-based Software Engineering Agents,"Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic three-phase process of localization, repair, and patch validation, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (32.00%, 96 correct fixes) and low cost ($0.70) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction.",Chunqiu Steven Xia; Yinlin Deng; Soren Dunn; Lingming Zhang,2024-07-01,2024,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2407.01489,2026-07-17T05:43:07+00:00
+ale-0167,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoCodeRover,https://github.com/AutoCodeRoverSG/auto-code-rover,external,github.com,"Autonomous program improvement system for issue localization, patch generation, and validation.","Autonomous program improvement system for issue localization, patch generation, and validation.","Autonomous program improvement system for issue localization, patch generation, and validation.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Autonomous program improvement system for issue localization, patch generation, and validation.",Gives readers a concrete source in AutoCodeRover when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,096 stars; 334 forks; NOASSERTION license; updated 2026-07-14); popularity is context, not proof of reliability.",medium,README.md,645,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L645,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/AutoCodeRoverSG/auto-code-rover,GitHub - AutoCodeRoverSG/auto-code-rover: A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. · GitHub,A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. - AutoCodeRoverSG/auto-code-rover,,2024-04-08,2024,AutoCodeRoverSG/auto-code-rover,GitHub,,,,github-api,AutoCodeRoverSG/auto-code-rover,3096,334,NOASSERTION,2024-04-08T05:34:14Z,2026-07-14T08:02:04Z,,2026-07-17T05:43:07+00:00
+ale-0168,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,AutoCodeRover: Autonomous Program Improvement,https://arxiv.org/abs/2404.05427,external,arxiv.org,Paper on autonomous code repair loops over real repositories.,Paper on autonomous code repair loops over real repositories.,Paper on autonomous code repair loops over real repositories.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Paper on autonomous code repair loops over real repositories.,Gives readers a concrete source in AutoCodeRover: Autonomous Program Improvement when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2404.05427; inspect its method and evaluation before treating results as production evidence.,medium,README.md,646,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L646,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2404.05427,[2404.05427] AutoCodeRover: Autonomous Program Improvement,"Researchers have made significant progress in automating the software development process in the past decades. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. bug fixing) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving GitHub issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM's understanding of the issue's root cause, and effectively retrieve a context via iterative search. The use of spectrum-based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on SWE-bench-lite (300 real-life GitHub issues) show increased efficacy in solving GitHub issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported SWE-agent. In addition, AutoCodeRover achieved this efficacy with significantly lower cost (on average, $0.43 USD), compared to other baselines. We posit that our workflow enables autonomous software engineering, where, in future, auto-generated code from LLMs can be autonomously improved.",Yuntong Zhang; Haifeng Ruan; Zhiyu Fan; Abhik Roychoudhury,2024-04-08,2024,arXiv,arXiv,,To appear in ISSTA 2024,cs.SE,arxiv-api,,,,,,,2404.05427,2026-07-17T05:43:07+00:00
+ale-0169,Coding-Agent Loop Systems,coding-agent-loop-systems,List,🧭,SWE-bench reading list,https://github.com/SWE-bench/reading-list,external,github.com,Maintained map of software engineering agent systems and related papers.,Maintained map of software engineering agent systems and related papers.,Maintained map of software engineering agent systems and related papers.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Maintained map of software engineering agent systems and related papers.,Gives readers a concrete source in SWE-bench reading list when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (15 stars; 4 forks; updated 2026-06-30); popularity is context, not proof of reliability.",medium,README.md,647,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L647,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,curated-index,ok,https://github.com/SWE-bench/reading-list,GitHub - SWE-bench/reading-list: Academic papers and works related to SWE-bench and SWE-agents · GitHub,Academic papers and works related to SWE-bench and SWE-agents - SWE-bench/reading-list,,2025-06-26,2025,SWE-bench/reading-list,GitHub,,,,github-api,SWE-bench/reading-list,15,4,,2025-06-26T22:03:02Z,2026-06-30T13:06:47Z,,2026-07-17T05:43:07+00:00
+ale-0170,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code,https://arxiv.org/abs/2602.06875,external,arxiv.org,"ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain.",Gives readers a concrete source in TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2602.06875; inspect its method and evaluation before treating results as production evidence.,medium,README.md,648,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L648,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2602.06875,[2602.06875] TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code,"Large Language Models (LLMs) often generate code with subtle but critical bugs, especially for complex tasks. Existing automated repair methods typically rely on superficial pass/fail signals, offering limited visibility into program behavior and hindering precise error localization. In addition, without a way to learn from prior failures, repair processes often fall into repetitive and inefficient cycles. To overcome these challenges, we present TraceCoder, a collaborative multi-agent framework that emulates the observe-analyze-repair process of human experts. The framework first instruments the code with diagnostic probes to capture fine-grained runtime traces, enabling deep insight into its internal execution. It then conducts causal analysis on these traces to accurately identify the root cause of the failure. This process is further enhanced by a novel Historical Lesson Learning Mechanism (HLLM), which distills insights from prior failed repair attempts to inform subsequent correction strategies and prevent recurrence of similar mistakes. To ensure stable convergence, a Rollback Mechanism enforces that each repair iteration constitutes a strict improvement toward the correct solution. Comprehensive experiments across multiple benchmarks show that TraceCoder achieves up to a 34.43\% relative improvement in Pass@1 accuracy over existing advanced baselines. Ablation studies verify the significance of each system component, with the iterative repair process alone contributing a 65.61\% relative gain in accuracy. Furthermore, TraceCoder significantly outperforms leading iterative methods in terms of both accuracy and cost-efficiency.",Jiangping Huang; Wenguang Ye; Weisong Sun; Jian Zhang; Mingyue Zhang; Yang Liu,2026-02-06,2026,arXiv,arXiv,10.1145/3744916.3773187,,cs.SE,arxiv-api,,,,,,,2602.06875,2026-07-17T05:43:07+00:00
+ale-0171,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase,https://arxiv.org/abs/2603.25697,external,arxiv.org,"Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions.",Gives readers a concrete source in The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.25697; inspect its method and evaluation before treating results as production evidence.,medium,README.md,649,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L649,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.25697,[2603.25697] The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase,"Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User x 1000', where an LLM agent exercises that surface as a synthetic power user at 1,000x human cadence; (3) Unbeatable Tests, ground-truth verification the code author cannot fake; and (4) Drift Control, continuous quality measurement with automated pause gates. We validate across two production systems over 285+ iterations, producing 1,094+ merged pull requests with zero regressions detected by the regression oracle (methodology in Section 6.1). We observe emergent properties at scale: multi-iteration self-correction chains, autonomous infrastructure healing, and monotonically improving quality gates. The primitives are not new; our contribution is their composition into a production-tested system with the operational discipline that makes long-running autonomous evolution safe.",Yannick Roy,2026-03-26,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2603.25697,2026-07-17T05:43:07+00:00
+ale-0172,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures,https://arxiv.org/abs/2604.03515,external,arxiv.org,"Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine.",Gives readers a concrete source in Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2604.03515; inspect its method and evaluation before treating results as production evidence.,medium,README.md,650,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L650,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification;state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.03515,[2604.03515] Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures,"LLM-based coding agents can localize bugs, generate patches, and run tests with diminishing human oversight, yet the scaffolding code that surrounds the language model (the control loop, tool definitions, state management, and context strategy) remains poorly understood. Existing surveys classify agents by abstract capabilities (tool use, planning, reflection) that cannot distinguish between architecturally distinct systems, and trajectory studies observe what agents do without examining the scaffold code that determines why. This paper presents a source-code-level architectural taxonomy derived from analysis of 13 open-source coding agent scaffolds at pinned commit hashes. Each agent is characterized across 12 dimensions organized into three layers: control architecture, tool and environment interface, and resource management. The analysis reveals that scaffold architectures resist discrete classification: control strategies range from fixed pipelines to Monte Carlo Tree Search, tool counts range from 0 to 37, and context compaction spans seven distinct strategies. Five loop primitives (ReAct, generate-test-repair, plan-execute, multi-attempt retry, tree search) function as composable building blocks that agents layer in different combinations; 11 of 13 agents compose multiple primitives rather than relying on a single control structure. Dimensions converge where external constraints dominate (tool capability categories, edit formats, execution isolation) and diverge where open design questions remain (context compaction, state management, multi-model routing). All taxonomic claims are grounded in file paths and line numbers, providing a reusable reference for researchers studying agent behavior and practitioners designing new scaffolds.",Benjamin Rombaut,2026-04-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2604.03515,2026-07-17T05:43:07+00:00
+ale-0173,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,A Self-Improving Coding Agent,https://arxiv.org/abs/2504.15228,external,arxiv.org,"An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop.",Gives readers a concrete source in A Self-Improving Coding Agent when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2504.15228; inspect its method and evaluation before treating results as production evidence.,medium,README.md,651,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L651,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2504.15228,[2504.15228] A Self-Improving Coding Agent,"Recent advancements in Large Language Models (LLMs) have spurred interest in deploying LLM agents to undertake tasks in the world. LLMs are often deployed in agent systems: code that orchestrates LLM calls and provides them with tools. We demonstrate that an agent system, equipped with basic coding tools, can autonomously edit itself, and thereby improve its performance on benchmark tasks. We find performance gains from 17% to 53% on a random subset of SWE Bench Verified, with additional performance gains on LiveCodeBench, as well as synthetically generated agent benchmarks. Our work represents an advancement in the automated and open-ended design of agentic systems, and demonstrates a data-efficient, non gradient-based learning mechanism driven by LLM reflection and code updates.",Maxime Robeyns; Martin Szummer; Laurence Aitchison,2025-04-21,2025,arXiv,arXiv,,Submitted as a preprint to NeurIPS 2025,cs.AI,arxiv-api,,,,,,,2504.15228,2026-07-17T05:43:07+00:00
+ale-0174,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,https://arxiv.org/abs/2607.03691,external,arxiv.org,"Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions.",Gives readers a concrete source in Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.03691; inspect its method and evaluation before treating results as production evidence.,medium,README.md,652,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L652,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03691,[2607.03691] Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,"Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these scaffoldings evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after scaffolding updates, yet consistently attribute them to the underlying model rather than the scaffolding itself. In this paper, we address this gap by conducting the first controlled longitudinal study that isolates the scaffolding's contribution. Unlike prior work that fixes the scaffolding and varies the model, we fix the model and vary only the scaffolding, evaluating 35 sequential releases to measure their impact on agent effectiveness and efficiency. We first empirically study the development and release evolution of five major open-source scaffoldings (i.e., Codex, Qwen Code, Gemini, OpenCode, and OpenHands), revealing extreme release velocities exceeding two releases per day and thousands of issues within months. We then perform a controlled deep dive into 35 sequential releases of the Qwen Code CLI, evaluating each against 50 stratified SWE-bench Verified tasks while holding the underlying LLM constant. We trace the resulting quality fluctuations to specific development patterns and architectural components, and illustrate our findings with concrete qualitative evidence linking individual pull requests to measured quality shifts.",Oussama Ben Sghaier; Hao Li; Bram Adams; Ahmed E. Hassan,2026-07-04,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.03691,2026-07-17T05:43:07+00:00
+ale-0175,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,"ToFu: A White-Box, Token-Efficient Agent Harness for Researchers",https://arxiv.org/abs/2607.11423,external,arxiv.org,"MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses.","Gives readers a concrete source in ToFu: A White-Box, Token-Efficient Agent Harness for Researchers when they need to choose an implementation surface for repeatable agent work.",Research source arXiv:2607.11423; inspect its method and evaluation before treating results as production evidence.,medium,README.md,653,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L653,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11423,"[2607.11423] ToFu: A White-Box, Token-Efficient Agent Harness for Researchers","Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent's behavior. We present ToFu, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools. ToFu plays a dual role in research. As a research assistant, it supports practical research workflows with superior token efficiency, lower cost, and multilingual capability compared with existing agentic harnesses. Its release under the MIT License further enables local deployment for privacy-sensitive users. As a research object, ToFu provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design, while retaining strong benchmark performance and an application-level user experience.",Junhao Ruan; Yuan Ge; Bei Li; Yongjing Yin; Yuchun Fan; Xin Chen; Jingang Wang; Chenglong Wang; Jingbo Zhu; Tong Xiao,2026-07-13,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.11423,2026-07-17T05:43:07+00:00
+ale-0176,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,When Does Restricting a Coding Agent to execute_code Help?,https://arxiv.org/abs/2607.10569,external,arxiv.org,"Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition.",Gives readers a concrete source in When Does Restricting a Coding Agent to execute_code Help? when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.10569; inspect its method and evaluation before treating results as production evidence.,medium,README.md,654,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L654,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10569,[2607.10569] When Does Restricting a Coding Agent to execute_code Help? A Regime $\times$ Agent-Design Ablation,"Modern coding agents expose multiple tool surfaces -- IDE primitives, bash, and Model Context Protocol (MCP) code-execution -- and the field has shipped three contradictory claims about which one matters. We run the missing crossed comparison: an integrity-clean three-arm ablation (baseline / bash_only / code_only) on synthetic computation tasks and SWE-bench Mini modification tasks, holding model, harness, and prompts fixed, with two agents (Claude Code, OpenAI Codex CLI) so the comparison spans both regime and agent-design axes. Across the four resulting (regime, agent) cells, restricting the agent to a single execute_code MCP tool is cheaper than -- or statistically tied with -- its cheapest tool-rich rival in three cells (significantly on Artifact/Claude and SWE-bench/Codex; directionally on Artifact/Codex), with pass rates statistically tied within each cell. The lone exception is SWE-bench/Claude, where code_only is directionally costlier (+14.4%, not significant); a conditional-cost analysis localizes that gap to failure-cost on doomed-run trajectories, not a per-edit tax on successful runs. Two implications: the cheapest tool surface is jointly determined by task regime and agent design rather than by either axis alone, and the headline cost signal lives in cache-adjusted cost -- not pass rate, which is invariant across surfaces at the model sizes we evaluate. The benchmark harness, task suite, and analysis code are available at https://github.com/hyang0129/onlycodes.",Hong Yang; Qi Yu; Travis Desell,2026-07-12,2026,arXiv,arXiv,,"9 pages (excluding references), 4 figures, 4 tables. Accepted to the Agentic Software Engineering (SE 3.0) Workshop at KDD 2026 (non-archival)",cs.SE,arxiv-api,,,,,,,2607.10569,2026-07-17T05:43:07+00:00
+ale-0177,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Ralph,https://ghuntley.com/ralph/,external,ghuntley.com,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory.,Gives readers a concrete source in Ralph when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,659,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L659,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://ghuntley.com/ralph/,"Ralph Wiggum as a ""software engineer""","How Ralph Wiggum went from 'The Simpsons' to the biggest name in AI right now - Venture Beat 😎Here's a cool little field report from a Y Combinator hackathon event where they put Ralph Wiggum to the test. ""We Put a Coding Agent in a While Loop and It Shipped",,2025-07-14,2025,,Geoffrey Huntley,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0178,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,everything is a ralph loop,https://ghuntley.com/loop/,external,ghuntley.com,"Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop.",Gives readers a concrete source in everything is a ralph loop when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,660,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L660,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,operational-pattern,ok,https://ghuntley.com/loop/,everything is a ralph loop,"I’ve been thinking about how I build software is so very very different how I used to do it three years ago. No, I’m not talking about acceleration through usage of AI but instead at a more fundamental level of approach, techniques and best practices. Standard software practices",,2026-01-17,2026,,Geoffrey Huntley,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0179,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,how-to-ralph-wiggum,https://github.com/ghuntley/how-to-ralph-wiggum,external,github.com,"Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions.",Gives readers a concrete source in how-to-ralph-wiggum when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,718 stars; 145 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,661,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L661,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/ghuntley/how-to-ralph-wiggum,GitHub - ghuntley/how-to-ralph-wiggum: The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. · GitHub,The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. - ghuntley/how-to-ralph-wiggum,,2026-01-10,2026,ghuntley/how-to-ralph-wiggum,GitHub,,,,github-api,ghuntley/how-to-ralph-wiggum,1718,145,,2026-01-10T04:32:01Z,2026-07-17T03:55:55Z,,2026-07-17T05:43:07+00:00
+ale-0180,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,A Brief History of Ralph,https://www.humanlayer.dev/blog/brief-history-of-ralph,external,www.humanlayer.dev,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Traces how the bare-loop technique spread from a provocation to a production practice among early adopters.,Gives readers a concrete source in A Brief History of Ralph when they need to choose an implementation surface for repeatable agent work.,"Contextual source from www.humanlayer.dev; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,662,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L662,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,practitioner-analysis,ok,https://www.humanlayer.dev/blog/brief-history-of-ralph,A Brief History of Ralph | HumanLayer Blog,The Ralph Wiggum Technique went viral in the last week of 2025. Here's the story of ralph since the first time I met Geoff in June of 2025.,,2026,2026,,humanlayer.dev,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0181,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Ralph Copilot,https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf,external,github.com,"Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`.",Gives readers a concrete source in Ralph Copilot when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (138 stars; 16 forks; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,663,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L663,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf,GitHub - giocaizzi/ralph-copilot at e5b2813cc876c73a8c9d3398c0115da0d15f63cf · GitHub,Copilot implementation of Ralph loop. Contribute to giocaizzi/ralph-copilot development by creating an account on GitHub.,,2026-01-30,2026,giocaizzi/ralph-copilot,GitHub,,,,github-api,giocaizzi/ralph-copilot,138,16,,2026-01-30T11:25:36Z,2026-07-16T14:18:20Z,,2026-07-17T05:43:07+00:00
+ale-0182,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Ralph (snarktank),https://github.com/snarktank/ralph,external,github.com,"Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes.",Gives readers a concrete source in Ralph (snarktank) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (21,102 stars; 2,040 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,664,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L664,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state,builder,source-implementation,ok,https://github.com/snarktank/ralph,GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. · GitHub,Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. - GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.,,2026-01-07,2026,snarktank/ralph,GitHub,,,,github-api,snarktank/ralph,21102,2040,MIT,2026-01-07T14:45:10Z,2026-07-17T04:08:27Z,,2026-07-17T05:43:07+00:00
+ale-0183,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-claude-code,https://github.com/frankbria/ralph-claude-code,external,github.com,"Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop.",Gives readers a concrete source in ralph-claude-code when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (9,544 stars; 727 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,665,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L665,,Build,build,"Choose runtimes, tools, and delegation surfaces.",exit,builder,source-implementation,ok,https://github.com/frankbria/ralph-claude-code,GitHub - frankbria/ralph-claude-code: Autonomous AI development loop for Claude Code with intelligent exit detection · GitHub,Autonomous AI development loop for Claude Code with intelligent exit detection - frankbria/ralph-claude-code,,2025-08-27,2025,frankbria/ralph-claude-code,GitHub,,,,github-api,frankbria/ralph-claude-code,9544,727,MIT,2025-08-27T16:03:45Z,2026-07-17T00:57:55Z,,2026-07-17T05:43:07+00:00
+ale-0184,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-orchestrator,https://github.com/mikeyobrien/ralph-orchestrator,external,github.com,"Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard.",Gives readers a concrete source in ralph-orchestrator when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,007 stars; 281 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,666,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L666,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;escalation;exit,builder,source-implementation,ok,https://github.com/mikeyobrien/ralph-orchestrator,GitHub - mikeyobrien/ralph-orchestrator: An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration · GitHub,An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration - mikeyobrien/ralph-orchestrator,,2025-09-07,2025,mikeyobrien/ralph-orchestrator,GitHub,,,,github-api,mikeyobrien/ralph-orchestrator,3007,281,MIT,2025-09-07T18:17:13Z,2026-07-17T02:05:04Z,,2026-07-17T05:43:07+00:00
+ale-0185,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralphex,https://github.com/umputun/ralphex,external,github.com,"Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan.",Gives readers a concrete source in ralphex when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,381 stars; 110 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,667,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L667,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/umputun/ralphex,GitHub - umputun/ralphex: Extended Ralph loop for autonomous AI-driven plan execution · GitHub,Extended Ralph loop for autonomous AI-driven plan execution - umputun/ralphex,,2026-01-19,2026,umputun/ralphex,GitHub,,,,github-api,umputun/ralphex,1381,110,MIT,2026-01-19T22:44:35Z,2026-07-16T18:57:15Z,,2026-07-17T05:43:07+00:00
+ale-0186,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph (iannuttall),https://github.com/iannuttall/ralph,external,github.com,"File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends.",Gives readers a concrete source in ralph (iannuttall) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (932 stars; 90 forks; updated 2026-07-11); popularity is context, not proof of reliability.",medium,README.md,668,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L668,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;state,builder,source-implementation,ok,https://github.com/iannuttall/ralph,"GitHub - iannuttall/ralph: A minimal, file‑based agent loop for autonomous coding. · GitHub","A minimal, file‑based agent loop for autonomous coding. - iannuttall/ralph",,2026-01-12,2026,iannuttall/ralph,GitHub,,,,github-api,iannuttall/ralph,932,90,,2026-01-12T15:31:06Z,2026-07-11T16:27:54Z,,2026-07-17T05:43:07+00:00
+ale-0187,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ralph-loop-agent,https://github.com/vercel-labs/ralph-loop-agent,external,github.com,"Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger.",Gives readers a concrete source in ralph-loop-agent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (821 stars; 85 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,669,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L669,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;budget;exit,builder,source-implementation,ok,https://github.com/vercel-labs/ralph-loop-agent,GitHub - vercel-labs/ralph-loop-agent: Continuous Autonomy for the AI SDK · GitHub,Continuous Autonomy for the AI SDK. Contribute to vercel-labs/ralph-loop-agent development by creating an account on GitHub.,,2026-01-03,2026,vercel-labs/ralph-loop-agent,GitHub,,,,github-api,vercel-labs/ralph-loop-agent,821,85,Apache-2.0,2026-01-03T21:18:55Z,2026-07-16T19:00:19Z,,2026-07-17T05:43:07+00:00
+ale-0188,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Open Ralph Wiggum,https://github.com/Th0rgal/open-ralph-wiggum,external,github.com,"Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends.",Gives readers a concrete source in Open Ralph Wiggum when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,841 stars; 141 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,670,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L670,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/Th0rgal/open-ralph-wiggum,"GitHub - Th0rgal/open-ralph-wiggum: Type `ralph ""prompt""` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot · GitHub","Type `ralph ""prompt""` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot - Th0rgal/open-ralph-wiggum",,2026-01-06,2026,Th0rgal/open-ralph-wiggum,GitHub,,,,github-api,Th0rgal/open-ralph-wiggum,1841,141,MIT,2026-01-06T08:06:47Z,2026-07-17T05:05:05Z,,2026-07-17T05:43:07+00:00
+ale-0189,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,Compound Engineering,https://every.to/guides/compound-engineering,external,every.to,"Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph.",Gives readers a concrete source in Compound Engineering when they need to choose an implementation surface for repeatable agent work.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,675,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L675,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,operational-pattern,ok,https://every.to/guides/compound-engineering,Compound Engineering - Every,The AI-native engineering philosophy,,,,,every.to,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0190,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Gas Town,https://github.com/steveyegge/gastown,external,github.com,"Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration.",Gives readers a concrete source in Gas Town when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (17,063 stars; 1,569 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,676,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L676,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,builder,source-implementation,ok,https://github.com/gastownhall/gastown,GitHub - gastownhall/gastown: Gas Town - multi-agent workspace manager · GitHub,Gas Town - multi-agent workspace manager. Contribute to gastownhall/gastown development by creating an account on GitHub.,,2025-12-16,2025,steveyegge/gastown,GitHub,,,,github-api,steveyegge/gastown,17063,1569,MIT,2025-12-16T00:33:33Z,2026-07-17T04:12:15Z,,2026-07-17T05:43:07+00:00
+ale-0191,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Amp,https://ampcode.com/,external,ampcode.com,"Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices.",Gives readers a concrete source in Amp when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,677,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L677,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;context;delegation,builder,implementation,ok,https://ampcode.com/,Amp,Amp is a frontier coding agent that lets you wield the full power of leading models.,,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0192,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,karl,https://github.com/kayoslab/karl,external,github.com,"Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases.",Gives readers a concrete source in karl when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (0 stars; 0 forks; MIT license; updated 2026-04-08); popularity is context, not proof of reliability.",medium,README.md,678,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L678,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;budget,builder,source-implementation,ok,https://github.com/kayoslab/karl,GitHub - kayoslab/karl: Autonomous multi-agent development loop · GitHub,Autonomous multi-agent development loop. Contribute to kayoslab/karl development by creating an account on GitHub.,,2026-03-10,2026,kayoslab/karl,GitHub,,,,github-api,kayoslab/karl,0,0,MIT,2026-03-10T09:50:32Z,2026-04-08T07:56:55Z,,2026-07-17T05:43:07+00:00
+ale-0193,Coding-Agent Loop Systems,coding-agent-loop-systems,Pattern,🔁,joelclaw agent-loop skill,https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md,external,github.com,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files.,Gives readers a concrete source in joelclaw agent-loop skill when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (60 stars; 3 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,679,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L679,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,operational-pattern,ok,https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md,joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw · GitHub,"Personal AI operating system — blog, architecture decisions, and the journey from zero to a composable agent system. - joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw",,2026-02-14,2026,joelhooks/joelclaw,GitHub,,,,github-api,joelhooks/joelclaw,60,3,,2026-02-14T22:54:08Z,2026-07-17T04:17:51Z,,2026-07-17T05:43:07+00:00
+ale-0194,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ARIS (Auto-Research-In-Sleep),https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep,external,github.com,"Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate.",Gives readers a concrete source in ARIS (Auto-Research-In-Sleep) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (13,501 stars; 1,218 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,680,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L680,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;verification,builder,source-implementation,ok,https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep,"GitHub - wanshuiyin/Auto-claude-code-research-in-sleep: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. · GitHub","ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. - wanshuiyin/Auto-claude-code-research-in-sleep",,2026-03-10,2026,wanshuiyin/Auto-claude-code-research-in-sleep,GitHub,,,,github-api,wanshuiyin/Auto-claude-code-research-in-sleep,13501,1218,MIT,2026-03-10T07:31:45Z,2026-07-17T05:25:23Z,,2026-07-17T05:43:07+00:00
+ale-0195,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoAgent,https://github.com/kevinrgu/autoagent,external,github.com,"Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.","The work turns loop quality into a measurable task or score. Resource-specific angle: Meta-agent that autonomously edits its own harness (system prompt, tools, orchestration), re-runs the benchmark, and keeps or discards each change by score, with an author-reported top SpreadsheetBench result from a 24-hour unattended run.",Gives readers a concrete source in AutoAgent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (4,543 stars; 498 forks; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,681,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L681,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification,builder,source-implementation,ok,https://github.com/kevinrgu/autoagent,GitHub - kevinrgu/autoagent: autonomous harness engineering · GitHub,autonomous harness engineering. Contribute to kevinrgu/autoagent development by creating an account on GitHub.,,2026-04-02,2026,kevinrgu/autoagent,GitHub,,,,github-api,kevinrgu/autoagent,4543,498,,2026-04-02T20:44:58Z,2026-07-16T09:09:57Z,,2026-07-17T05:43:07+00:00
+ale-0196,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,zeroshot,https://github.com/the-open-engine/zeroshot,external,github.com,"CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.",Gives readers a concrete source in zeroshot when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,647 stars; 140 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,682,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L682,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/the-open-engine/zeroshot,"GitHub - the-open-engine/zeroshot: Your autonomous engineering team in a CLI. The agent loop produces senior-level code that you can actually trust in prod because of non-negotiable feedback from independent reviewers. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI with trivial setup. · GitHub","Your autonomous engineering team in a CLI. The agent loop produces senior-level code that you can actually trust in prod because of non-negotiable feedback from independent reviewers. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI with trivial setup. - the-open-engine/zeroshot",,2025-12-25,2025,the-open-engine/zeroshot,GitHub,,,,github-api,the-open-engine/zeroshot,1647,140,MIT,2025-12-25T17:12:45Z,2026-07-16T20:07:25Z,,2026-07-17T05:43:07+00:00
+ale-0197,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Loki Mode,https://github.com/asklokesh/loki-mode,external,github.com,"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.",Gives readers a concrete source in Loki Mode when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,018 stars; 200 forks; NOASSERTION license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,683,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L683,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state;exit,builder,source-implementation,ok,https://github.com/asklokesh/loki-mode,"GitHub - asklokesh/loki-mode: Multi-agent autonomous SDLC framework. Spec to deployed app. PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. 5 AI providers, 8 quality gates. · GitHub","Multi-agent autonomous SDLC framework. Spec to deployed app. PRD, GitHub issue, OpenAPI/JSON/YAML, or one-line brief. 5 AI providers, 8 quality gates. - asklokesh/loki-mode",,2025-12-26,2025,asklokesh/loki-mode,GitHub,,,,github-api,asklokesh/loki-mode,1018,200,NOASSERTION,2025-12-26T16:26:14Z,2026-07-15T03:59:38Z,,2026-07-17T05:43:07+00:00
+ale-0198,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Looper,https://github.com/ksimback/looper,external,github.com,"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.",Gives readers a concrete source in Looper when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (684 stars; 61 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,684,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L684,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;verification;budget,builder,source-implementation,ok,https://github.com/ksimback/looper,"GitHub - ksimback/looper: Design visual, review-gated agent loops for Claude Code before you run them. · GitHub","Design visual, review-gated agent loops for Claude Code before you run them. - ksimback/looper",,2026-06-18,2026,ksimback/looper,GitHub,,,,github-api,ksimback/looper,684,61,MIT,2026-06-18T19:44:20Z,2026-07-16T17:18:02Z,,2026-07-17T05:43:07+00:00
+ale-0199,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Agent Apprenticeship,https://github.com/Forsy-AI/agent-apprenticeship,external,github.com,"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.",Gives readers a concrete source in Agent Apprenticeship when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,317 stars; 56 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,685,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L685,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/Forsy-AI/agent-apprenticeship,"GitHub - Forsy-AI/agent-apprenticeship: The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents. · GitHub","The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents. - Forsy-AI/agent-apprenticeship",,2026-06-19,2026,Forsy-AI/agent-apprenticeship,GitHub,,,,github-api,Forsy-AI/agent-apprenticeship,1317,56,MIT,2026-06-19T16:19:37Z,2026-07-17T00:34:53Z,,2026-07-17T05:43:07+00:00
+ale-0200,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Scholar Loop,https://github.com/renee-jia/scholar-loop,external,github.com,"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.",Gives readers a concrete source in Scholar Loop when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (461 stars; 35 forks; MIT license; updated 2026-07-08); popularity is context, not proof of reliability.",medium,README.md,686,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L686,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/renee-jia/scholar-loop,"GitHub - renee-jia/scholar-loop: An autonomous AI scientist: a multi-agent loop over literature, experiments, self-critique and write-up, with deterministic guards against reward-hacking and hallucination. · GitHub","An autonomous AI scientist: a multi-agent loop over literature, experiments, self-critique and write-up, with deterministic guards against reward-hacking and hallucination. - renee-jia/scholar-loop",,2026-06-15,2026,renee-jia/scholar-loop,GitHub,,,,github-api,renee-jia/scholar-loop,461,35,MIT,2026-06-15T04:50:58Z,2026-07-08T05:18:54Z,,2026-07-17T05:43:07+00:00
+ale-0201,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,loop-engineering (Cobus Greyling),https://github.com/cobusgreyling/loop-engineering,external,github.com,"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's ""Loop Ready"" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.",Gives readers a concrete source in loop-engineering (Cobus Greyling) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (8,175 stars; 1,071 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,687,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L687,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace;context;state;budget,builder,source-implementation,ok,https://github.com/cobusgreyling/loop-engineering,"GitHub - cobusgreyling/loop-engineering: Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost. · GitHub","Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost. - cobusgreyling/loop-engineering",,2026-06-09,2026,cobusgreyling/loop-engineering,GitHub,,,,github-api,cobusgreyling/loop-engineering,8175,1071,MIT,2026-06-09T06:28:20Z,2026-07-17T05:43:53Z,,2026-07-17T05:43:07+00:00
+ale-0202,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,AutoCVE,https://github.com/larlarua/AutoCVE,external,github.com,"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.",Gives readers a concrete source in AutoCVE when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,289 stars; 83 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,688,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L688,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation;verification;exit,builder,source-implementation,ok,https://github.com/larlarua/AutoCVE,"GitHub - larlarua/AutoCVE: Agent-driven automated CVE discovery platform for source code auditing, vulnerability verification, and report generation. · GitHub","Agent-driven automated CVE discovery platform for source code auditing, vulnerability verification, and report generation. - larlarua/AutoCVE",,2026-06-15,2026,larlarua/AutoCVE,GitHub,,,,github-api,larlarua/AutoCVE,1289,83,AGPL-3.0,2026-06-15T14:50:47Z,2026-07-17T04:42:55Z,,2026-07-17T05:43:07+00:00
+ale-0203,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,LoongFlow (Baidu),https://github.com/baidu-baige/LoongFlow,external,github.com,"Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Baidu's open-source agent framework built explicitly for Loop Engineering: a Plan-Execute-Summary loop with structured experiential memory lets agents plan, execute, reflect, and evolve across software-engineering, math, and ML tasks (Apache-2.0, on PyPI, paper: arXiv 2512.24077).",Gives readers a concrete source in LoongFlow (Baidu) when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (452 stars; 51 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,689,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L689,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/baidu-baige/LoongFlow,"GitHub - baidu-baige/LoongFlow: LoongFlow is an expert-grade Agent framework for Loop Engineering. Through a Plan-Execute-Summary loop and structured experiential memory, it enables AI to continuously think, execute, reflect, and evolve across complex software engineering, mathematical, and machine learning tasks. · GitHub","LoongFlow is an expert-grade Agent framework for Loop Engineering. Through a Plan-Execute-Summary loop and structured experiential memory, it enables AI to continuously think, execute, reflect, and evolve across complex software engineering, mathematical, and machine learning tasks. - baidu-baige/LoongFlow",,2025-12-31,2025,baidu-baige/LoongFlow,GitHub,,,,github-api,baidu-baige/LoongFlow,452,51,Apache-2.0,2025-12-31T05:36:46Z,2026-07-17T02:37:11Z,,2026-07-17T05:43:07+00:00
+ale-0204,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,cc10x,https://github.com/romiluz13/cc10x,external,github.com,"Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Claude Code plugin that routes work through one router, nine specialist agents, sixteen skills, and four workflows, enforcing fail-closed verification and test-honesty gates and writing each workflow's intent, evidence, and verdicts to durable .cc10x/ disk artifacts so resume and review survive context compaction.",Gives readers a concrete source in cc10x when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (153 stars; 24 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,690,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L690,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;verification,builder,source-implementation,ok,https://github.com/romiluz13/cc10x,"GitHub - romiluz13/cc10x: The Loop Engine for Claude Code — engineer the loop, not the prompt. 1 router · 9 agents · 16 skills · 4 workflows. Fail-closed gates, test honesty, anti-anchored review. · GitHub","The Loop Engine for Claude Code — engineer the loop, not the prompt. 1 router · 9 agents · 16 skills · 4 workflows. Fail-closed gates, test honesty, anti-anchored review. - romiluz13/cc10x",,2025-10-22,2025,romiluz13/cc10x,GitHub,,,,github-api,romiluz13/cc10x,153,24,MIT,2025-10-22T11:54:14Z,2026-07-16T06:46:07Z,,2026-07-17T05:43:07+00:00
+ale-0205,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,RigorLoop,https://github.com/ronikobrosly/RigorLoop,external,github.com,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Statistically-grounded loop-engineering framework in which a strategy agent directs concurrent executor agents that iteratively build and refine a solution against gold-standard examples.,Gives readers a concrete source in RigorLoop when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (134 stars; 0 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,691,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L691,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/ronikobrosly/RigorLoop,"GitHub - ronikobrosly/RigorLoop: A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting. · GitHub","A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting. - GitHub - ronikobrosly/RigorLoop: A statistically-sound agentic build framework that employs agentic loops to create code artifacts (whether a script, a skill markdown file, etc). Crucially, it splits verification data into the classic data science-like dev, validation, and final test sets to avoid overfitting.",,2026-07-08,2026,ronikobrosly/RigorLoop,GitHub,,,,github-api,ronikobrosly/RigorLoop,134,0,MIT,2026-07-08T14:38:05Z,2026-07-17T00:53:18Z,,2026-07-17T05:43:07+00:00
+ale-0206,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Open-Inspect,https://github.com/ColeMurray/background-agents,external,github.com,"Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Open-source background coding-agent system inspired by Ramp's Inspect: hosted agents in full dev-environment sandboxes, reachable from a web UI, Slack, GitHub PRs, Linear, or webhooks.",Gives readers a concrete source in Open-Inspect when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,489 stars; 354 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,692,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L692,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,source-implementation,ok,https://github.com/ColeMurray/background-agents,GitHub - ColeMurray/background-agents: An open-source background agents coding system · GitHub,An open-source background agents coding system. Contribute to ColeMurray/background-agents development by creating an account on GitHub.,,2026-01-25,2026,ColeMurray/background-agents,GitHub,,,,github-api,ColeMurray/background-agents,2489,354,MIT,2026-01-25T18:45:47Z,2026-07-17T05:19:45Z,,2026-07-17T05:43:07+00:00
+ale-0207,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,T3MP3ST,https://github.com/elder-plinius/T3MP3ST,external,github.com,"Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.",Gives readers a concrete source in T3MP3ST when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (4,851 stars; 1,017 forks; AGPL-3.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,693,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L693,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;state,builder,source-implementation,ok,https://github.com/elder-plinius/T3MP3ST,GitHub - elder-plinius/T3MP3ST: autonomous red teaming platform; multi-agent offensive-security meta-harness · GitHub,autonomous red teaming platform; multi-agent offensive-security meta-harness - elder-plinius/T3MP3ST,,2026-07-02,2026,elder-plinius/T3MP3ST,GitHub,,,,github-api,elder-plinius/T3MP3ST,4851,1017,AGPL-3.0,2026-07-02T17:53:55Z,2026-07-17T05:42:14Z,,2026-07-17T05:43:07+00:00
+ale-0208,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Loom,https://github.com/valkor-ai/loom,external,github.com,"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.",Gives readers a concrete source in Loom when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (579 stars; 62 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,694,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L694,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification;state,builder,source-implementation,ok,https://github.com/valkor-ai/loom,GitHub - valkor-ai/loom: Loop engineering for agentic software delivery. · GitHub,Loop engineering for agentic software delivery. Contribute to valkor-ai/loom development by creating an account on GitHub.,,2026-06-09,2026,valkor-ai/loom,GitHub,,,,github-api,valkor-ai/loom,579,62,Apache-2.0,2026-06-09T03:46:34Z,2026-07-17T01:09:37Z,,2026-07-17T05:43:07+00:00
+ale-0209,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,Inferoa,https://github.com/agentic-in/inferoa,external,github.com,"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.",Gives readers a concrete source in Inferoa when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (486 stars; 83 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,695,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L695,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/agentic-in/inferoa,GitHub - agentic-in/inferoa: Inference-native Tokenmaxxing Agent Harness for Loop Engineering · GitHub,Inference-native Tokenmaxxing Agent Harness for Loop Engineering - agentic-in/inferoa,,2026-06-08,2026,agentic-in/inferoa,GitHub,,,,github-api,agentic-in/inferoa,486,83,Apache-2.0,2026-06-08T06:29:56Z,2026-07-16T10:37:09Z,,2026-07-17T05:43:07+00:00
+ale-0210,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,PlanWeave,https://github.com/GaosCode/PlanWeave,external,github.com,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.,Gives readers a concrete source in PlanWeave when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (221 stars; 12 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,696,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L696,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/GaosCode/PlanWeave,"GitHub - GaosCode/PlanWeave: PlanWeave is a file-backed loop engineering system for long-running coding agents. It turns fuzzy plans into claimable tasks, routes them through implementation and review agents, records every run, and keeps the loop recoverable. · GitHub","PlanWeave is a file-backed loop engineering system for long-running coding agents. It turns fuzzy plans into claimable tasks, routes them through implementation and review agents, records every run, and keeps the loop recoverable. - GaosCode/PlanWeave",,2026-05-24,2026,GaosCode/PlanWeave,GitHub,,,,github-api,GaosCode/PlanWeave,221,12,MIT,2026-05-24T06:01:56Z,2026-07-17T03:36:41Z,,2026-07-17T05:43:07+00:00
+ale-0211,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,loop.js,https://github.com/loop-js/loop.js,external,github.com,"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.",Gives readers a concrete source in loop.js when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (123 stars; 0 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,697,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L697,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;budget,builder,source-implementation,ok,https://github.com/loop-js/loop.js,"GitHub - loop-js/loop.js: A loop engineering framework — state a Goal; Rounds run until a skeptical, read-only Verify agent settles it. · GitHub","A loop engineering framework — state a Goal; Rounds run until a skeptical, read-only Verify agent settles it. - loop-js/loop.js",,2026-07-10,2026,loop-js/loop.js,GitHub,,,,github-api,loop-js/loop.js,123,0,Apache-2.0,2026-07-10T10:59:54Z,2026-07-17T01:14:01Z,,2026-07-17T05:43:07+00:00
+ale-0212,Coding-Agent Loop Systems,coding-agent-loop-systems,Tool,🧰,ai-trains-ai,https://github.com/Danau5tin/ai-trains-ai,external,github.com,"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.",Gives readers a concrete source in ai-trains-ai when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (181 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,698,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L698,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,source-implementation,ok,https://github.com/Danau5tin/ai-trains-ai,GitHub - Danau5tin/ai-trains-ai: RL-training an AI agent to RL-train AI agents. · GitHub,RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.,,2026-07-07,2026,Danau5tin/ai-trains-ai,GitHub,,,,github-api,Danau5tin/ai-trains-ai,181,14,MIT,2026-07-07T07:45:52Z,2026-07-17T04:48:55Z,,2026-07-17T05:43:07+00:00
+ale-0213,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Factory 2.0: From Coding Agents to Software Factories,https://factory.ai/news/software-factory,external,factory.ai,"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.",Gives readers a concrete source in Factory 2.0: From Coding Agents to Software Factories when they need to choose an implementation surface for repeatable agent work.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,703,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L703,,Build,build,"Choose runtimes, tools, and delegation surfaces.",objective;context;delegation;state,builder,practitioner-analysis,ok,https://factory.ai/news/software-factory,Factory 2.0: From coding agents to software factories | Factory.ai Factory.ai Logo Arrow Right Icon,"In 2023, we launched Factory with the mission to bring autonomy to software engineering. While others were using models...",Factory,2026-06-15,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0214,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Superpowers 6,https://blog.fsck.com/2026/06/15/Superpowers-6/,external,blog.fsck.com,"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.",Gives readers a concrete source in Superpowers 6 when they need to choose an implementation surface for repeatable agent work.,"Contextual source from blog.fsck.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,704,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L704,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;budget,builder,practitioner-analysis,ok,https://blog.fsck.com/2026/06/15/Superpowers-6/,Superpowers 6 — Massively Parallel Procrastination,"I'm Jesse. I make stuff. Software, hardware. Very occasionally, trouble.",,,2026,,Massively Parallel Procrastination,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0215,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Introducing Devin Security Swarm,https://cognition.com/blog/introducing-devin-security-swarm,external,cognition.com,"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.",Gives readers a concrete source in Introducing Devin Security Swarm when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,705,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L705,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;intake;workspace;verification,builder,practitioner-analysis,ok,https://cognition.com/blog/introducing-devin-security-swarm,Introducing Devin Security Swarm | Cognition,"Devin Security Swarm finds vulnerabilities across the codebase, validates exploitability at runtime, and ships remediation PRs.",,2026-07-01,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0216,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,Towards Self-Driving Codebases,https://cursor.com/blog/self-driving-codebases,external,cursor.com,"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.",Gives readers a concrete source in Towards Self-Driving Codebases when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,706,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L706,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,builder,practitioner-analysis,ok,https://cursor.com/blog/self-driving-codebases,Towards self-driving codebases · Cursor,We're making a part of our multi-agent research harness available to try today in preview.,Wilson Lin,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0217,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,��,Factory: Incident Response Automation,https://factory.ai/news/incident-response,external,factory.ai,"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.",Gives readers a concrete source in Factory: Incident Response Automation when they need to choose an implementation surface for repeatable agent work.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,707,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L707,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;state,builder,practitioner-analysis,ok,https://factory.ai/news/incident-response,Incident Response | Factory.ai Factory.ai Logo Arrow Right Icon,"On-call alerts have always been stomach-dropping moments. Someone's dinner, weekend, or launch review gets hijacked for ...",Factory,2026-07-10,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0218,Coding-Agent Loop Systems,coding-agent-loop-systems,Blog,📝,A Week-Long Autonomous Voxel Manhattan Build,https://x.com/mattshumer_/status/2075268746315268138,external,x.com,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.,"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.",Gives readers a concrete source in A Week-Long Autonomous Voxel Manhattan Build when they need to choose an implementation surface for repeatable agent work.,"Contextual source from x.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,708,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L708,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,practitioner-analysis,ok,https://x.com/mattshumer_/status/2075268746315268138,"Matt Shumer on X: ""GPT-5.6-Sol one-shotted this voxel-based Manhattan. Just look at the precision... it's insane. It ran for almost a week, completely autonomously, to get the job done. https://t.co/LZgthaBnqL"" / X","GPT-5.6-Sol one-shotted this voxel-based Manhattan. Just look at the precision... it's insane. It ran for almost a week, completely autonomously, to get the job done.",,2026-07-09,2026,,X (formerly Twitter),,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0219,Coding-Agent Loop Systems,coding-agent-loop-systems,Paper,📄,Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,https://arxiv.org/abs/2607.13091,external,arxiv.org,"Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.","Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Converts accepted review feedback into versioned behavioral rules that future coding sessions can apply; across 11 reported production sessions, error classes covered by a rule did not recur, a promising but deliberately small-scope result.",Gives readers a concrete source in Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.13091; inspect its method and evaluation before treating results as production evidence.,medium,README.md,709,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L709,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13091,[2607.13091] Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,"LLM-based coding agents repeat the same classes of mistakes across sessions because they lack a mechanism to retain corrections from human review feedback. We present a closed-loop framework in which every accepted review comment is codified as a persistent behavioral rule, progressively expanding the set of error classes the agent can self-detect. The framework combines an accumulating rule set in a version-controlled instruction file, a self-review checklist executed before code submission, and automated validation that ensures rule set integrity as it grows. In deployment across a 35+ service microservices platform, the rule set grew from 5 to 18 behavioral rules, 15+ language-specific standards, and a 15-item self-review checklist, all derived from real review feedback. We present empirical results from 11 recorded working sessions spanning code generation, PR review, incident investigation, and cross service refactoring. We observe that accumulated rules shift review effort from low-level correctness toward design-level validation, achieve a measured 0% recurrence rate for ruled-against error classes, and transfer across heterogeneous agent interfaces. We compare our approach against related work in experiential LLM learning (Reflexion, ExpeL, Voyager) and automated code review (CodeReviewer, SWE-bench agents), showing that our framework achieves persistent cross-session learning without weight updates, operates on production codebases rather than synthetic benchmarks, and addresses an orthogonal dimension (behavioral consistency over time) that existing benchmarks do not measure. The result is a coding agent that improves with every review cycle, accumulating the engineering wisdom of its human collaborators without changing a single model weight.",Aditya Aggarwal; Nahid Farhady Ghalaty,2026-07-13,2026,arXiv,arXiv,,Already presented and accepted in - 32nd ICE IEEE/ITMC Conference (ICE 2026),cs.SE,arxiv-api,,,,,,,2607.13091,2026-07-17T05:43:07+00:00
+ale-0220,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Why Agentic Systems Must Produce Deterministic Outputs to Scale,https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale,external,streamzero.com,"Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Argues for deterministic boundaries, contracts, and execution gates around probabilistic agent reasoning.",Gives readers a concrete source in Why Agentic Systems Must Produce Deterministic Outputs to Scale when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from streamzero.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,718,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L718,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://streamzero.com/blog/posts/deep-dives-tools-technologies-architectures/agentic-patterns/why-agentic-systems-must-produce-deterministic-outputs-to-scale,Why Agentic Systems Must Produce Deterministic Outputs to Scale,"Agentic systems are gaining traction, but their inherent non-determinism poses a significant challenge for production environments. This document argues that deterministic outputs are essential for scaling agentic systems, enabling validation, security, and compliance in critical applications.",,,,,streamzero.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0221,Verification And Feedback Gates,verification-and-feedback-gates,Pattern,🔁,Stop Babysitting Your Coding Agent. Give It Backpressure.,https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent,external,generativeprogrammer.com,"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.",Gives readers a concrete source in Stop Babysitting Your Coding Agent. Give It Backpressure. when they need to measure progress and gate completion with repeatable evidence.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,719,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L719,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,operational-pattern,ok,https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent,Stop Babysitting Your Coding Agent. Give It Backpressure.,Backpressure is feedback that reaches the agent before the agent reaches the human.,Bilgin Ibryam,,,,generativeprogrammer.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0222,Verification And Feedback Gates,verification-and-feedback-gates,Pattern,🔁,How to Build a Self-Verification Loop in Claude Code,https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p,external,dev.to,"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.",Gives readers a concrete source in How to Build a Self-Verification Loop in Claude Code when they need to measure progress and gate completion with repeatable evidence.,Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.,medium,README.md,720,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L720,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,operational-pattern,ok,https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p,"How to Build a Self-Verification Loop in Claude Code (3 Layers, 20 Minutes) - DEV Community Navigation menu Search Search Close More... Copy link Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode Enter fullscreen mode Exit fullscreen mode","Claude Code's Stop hook blocks the agent from finishing until verification passes. Combine it with... Tagged with ai, programming, productivity, claude.",,,,,DEV Community,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0223,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Agentic Code Review,https://addyosmani.com/blog/agentic-code-review/,external,addyosmani.com,"Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.",Gives readers a concrete source in Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,721,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L721,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://addyosmani.com/blog/agentic-code-review/,AddyOsmani.com - Agentic Code Review,"Coding agents are extraordinarily good now, and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code...",Addy Osmani,,,,addyosmani.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0224,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts,https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/,external,simonwillison.net,"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.",Gives readers a concrete source in Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,722,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L722,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,evaluator,practitioner-analysis,ok,https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/,Research: Using DSPy to evaluate and improve Datasette Agent's SQL system prompts,"Leveraging the DSPy framework, this project evaluates and refines the core production system prompts used by Datasette Agent’s read-only SQL question answerer. The methodology involves a harness where DSPy agents …",Simon Willison,,2026,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0225,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Agentic coding notes,https://danluu.com/ai-coding/,external,danluu.com,"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","The work turns loop quality into a measurable task or score. Resource-specific angle: Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.",Gives readers a concrete source in Agentic coding notes when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from danluu.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,723,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L723,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,evaluator,practitioner-analysis,ok,https://danluu.com/ai-coding/,"Agentic test processes, LLM benchmarks, and other notes on agentic coding from Galapagos Island",,,,,,danluu.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0226,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Understanding Is the New Bottleneck,https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html,external,www.geoffreylitt.com,"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.",Gives readers a concrete source in Understanding Is the New Bottleneck when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.geoffreylitt.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,724,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L724,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,evaluator,practitioner-analysis,ok,https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html,Understanding is the new bottleneck,"Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.",,,2026,,geoffreylitt.com,,,,url-date,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0227,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Verifying Agentic Development at Scale,https://cognition.com/blog/testing-development,external,cognition.com,"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.",Gives readers a concrete source in Verifying Agentic Development at Scale when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,725,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L725,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://cognition.com/blog/testing-development,Verifying Agentic Development at Scale | Cognition,What we’ve learned building end-to-end testing capabilities in Devin’s virtual machine,,2026-05-29,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0228,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Loop Engineering Without Verification Is Just Automation,https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/,external,www.sonarsource.com,"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.",Gives readers a concrete source in Loop Engineering Without Verification Is Just Automation when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.sonarsource.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,726,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L726,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/,Loop engineering without verification is just automation | Sonar,Explore how LLM reviewers and deterministic checks work together to keep coding agent loops from shipping unfinished code.,,,,,sonarsource.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0229,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Closing the Verification Loop: Observability-Driven Harnesses,https://www.datadoghq.com/blog/ai/harness-first-agents/,external,www.datadoghq.com,"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.",Gives readers a concrete source in Closing the Verification Loop: Observability-Driven Harnesses when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.datadoghq.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,727,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L727,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.datadoghq.com/blog/ai/harness-first-agents/,Closing the verification loop: Observability-driven harnesses for building with agents | Datadog security-platform rum ci dashboard host-map apm security-platform rum ci dashboard host-map apm security-platform rum ci dashboard Icon/world,"Learn how Datadog verifies AI-generated systems at scale using deterministic testing, formal methods, and observability-driven feedback loops.","Alp Keles, Jai Menon, Sesh Nalla, Vyom Shah",2026-03-09,2026,,Datadog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0230,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,How to build a better agent harness with traces and evals,https://arize.com/blog/improve-ai-agents-traces-evals-harness/,external,arize.com,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Trace-evaluate-debug-refine loop for improving agent behavior from real runs.,Gives readers a concrete source in How to build a better agent harness with traces and evals when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from arize.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,732,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L732,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://arize.com/blog/improve-ai-agents-traces-evals-harness/,How to build a better agent harness with traces and evals - Arize AI,"Agents are easy to prototype and hard to improve. A repeatable loop of traces, evals, failed-span inspection, and targeted harness changes makes agent behavior easier to debug and improve.",Aaron Winston,2026-05-29,2026,,Arize AI,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0231,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Better Harness: A Recipe for Harness Hill-Climbing with Evals,https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals,external,www.langchain.com,LangChain's recipe for using evals as the learning signal for harness improvement.,LangChain's recipe for using evals as the learning signal for harness improvement.,LangChain's recipe for using evals as the learning signal for harness improvement.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: LangChain's recipe for using evals as the learning signal for harness improvement.,Gives readers a concrete source in Better Harness: A Recipe for Harness Hill-Climbing with Evals when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,733,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L733,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals,Better Harness: A Recipe for Harness Hill-Climbing with Evals,"We can build better agents by building better harnesses. But to autonomously build a “better” harness, we need a strong learning signal to “hill-climb” on. We share how we use evals as that signal, plus design decisions that help our agent generalize instead of overfit. Better-Harness is a system for iteratively sourcing and improving your harness with evals.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0232,Verification And Feedback Gates,verification-and-feedback-gates,Blog,📝,Improving Deep Agents with harness engineering,https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering,external,www.langchain.com,"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","The agent workflow includes explicit self-checking or gated completion. Resource-specific angle: Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.",Gives readers a concrete source in Improving Deep Agents with harness engineering when they need to measure progress and gate completion with repeatable evidence.,"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,734,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L734,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,evaluator,practitioner-analysis,ok,https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering,Improving Deep Agents with harness engineering,"Harness engineering improved LangChain's coding agent from Top 30 to Top 5 on Terminal Bench using self-verification, tracing, and context optimization.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0233,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,https://arxiv.org/abs/2604.25850,external,arxiv.org,"Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Closed loop that turns each harness edit into a falsifiable contract verified against trajectory outcomes, so the harness evolves from observability rather than trial and error.",Gives readers a concrete source in Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.25850; inspect its method and evaluation before treating results as production evidence.,medium,README.md,735,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L735,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.25850,[2604.25850] Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,"Harnesses are now central to coding-agent performance, mediating how models interact with tools and execution environments. Yet harness engineering remains a manual craft, because automating it faces a heterogeneous action space across editable components, voluminous trajectories that bury actionable signal, and edits whose effect is hard to attribute. We introduce Agentic Harness Engineering (AHE), a closed loop that addresses these challenges through three matched observability pillars: (1) component observability gives every editable harness component a file-level representation so the action space is explicit and revertible; (2) experience observability distills millions of raw trajectory tokens into a layered, drill-down evidence corpus that an evolving agent can actually consume; and (3) decision observability pairs every edit with a self-declared prediction, later verified against the next round's task-level outcomes. Together, these pillars turn every edit into a falsifiable contract, so harness evolution proceeds autonomously without collapsing into trial-and-error. Empirically, ten AHE iterations lift pass@1 on Terminal-Bench 2 from 69.7% to 77.0%, surpassing the human-designed harness Codex-CLI (71.9%) and the self-evolving baselines ACE and TF-GRPO. The frozen harness transfers without re-evolution: on SWE-bench-verified it tops aggregate success at 12% fewer tokens than the seed, and on Terminal-Bench 2 it yields +5.1 to +10.1pp cross-family gains across three alternate model families, indicating the evolved components encode general engineering experience rather than benchmark-specific tuning. Ablations localize the gain to tools, middleware, and long-term memory rather than the system prompt, suggesting factual harness structure transfers while prose-level strategy does not.",Jiahang Lin; Shichun Liu; Chengjun Pan; Lizhi Lin; Shihan Dou; Zhiheng Xi; Xuanjing Huang; Hang Yan; Zhenhua Han; Tao Gui; Yu-Gang Jiang,2026-04-28,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2604.25850,2026-07-17T05:43:07+00:00
+ale-0234,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Meta-Harness: End-to-End Optimization of Model Harnesses,https://arxiv.org/abs/2603.28052,external,arxiv.org,"Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.",Gives readers a concrete source in Meta-Harness: End-to-End Optimization of Model Harnesses when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2603.28052; inspect its method and evaluation before treating results as production evidence.,medium,README.md,736,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L736,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.28052,[2603.28052] Meta-Harness: End-to-End Optimization of Model Harnesses,"The performance of large language model (LLM) systems depends not only on model weights, but also on their harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing text optimizers are poorly matched to this setting because they compress feedback too aggressively. We introduce Meta-Harness, an outer-loop system that searches over harness code for LLM applications. It uses an agentic proposer that accesses the source code, scores, and execution traces of all prior candidates through a filesystem. On online text classification, Meta-Harness improves over a state-of-the-art context management system by 7.7 points while using 4x fewer context tokens. On retrieval-augmented math reasoning, a single discovered harness improves accuracy on 200 IMO-level problems by 4.7 points on average across five held-out models. On agentic coding, discovered harnesses surpass the best hand-engineered baselines on TerminalBench-2. Together, these results show that richer access to prior experience can enable automated harness engineering.",Yoonho Lee; Roshen Nair; Qizheng Zhang; Kangwook Lee; Omar Khattab; Chelsea Finn,2026-03-30,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.28052,2026-07-17T05:43:07+00:00
+ale-0235,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,HALO (Hierarchical Agent Loop Optimizer),https://github.com/context-labs/halo,external,github.com,"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.",Gives readers a concrete source in HALO (Hierarchical Agent Loop Optimizer) when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (1,103 stars; 80 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,737,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L737,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/context-labs/halo,GitHub - context-labs/HALO: Hierarchal Agent Loop Optimizer · GitHub,Hierarchal Agent Loop Optimizer. Contribute to context-labs/HALO development by creating an account on GitHub.,,2026-04-21,2026,context-labs/halo,GitHub,,,,github-api,context-labs/halo,1103,80,,2026-04-21T18:20:46Z,2026-07-17T03:36:53Z,,2026-07-17T05:43:07+00:00
+ale-0236,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions",https://arxiv.org/abs/2607.03935,external,arxiv.org,"Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Agentic RL framework in which one model both solves tasks and edits its own harness, including repairing faulty evaluation code, co-evolving weights, harness, and solutions so a trained Qwen3-8B matches a much larger baseline.","Gives readers a concrete source in Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2607.03935; inspect its method and evaluation before treating results as production evidence.,medium,README.md,738,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L738,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03935,"[2607.03935] Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions","Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-learning framework in which a single model can generate task solutions or edit selected harness components in a multi-turn action space. HASE enables a single Qwen3-8B model to match the text-classification performance of a GPT-OSS-120B model that uses Claude Code as the harness proposer. In alpha factor mining, HASE outperforms the reported GPT-OSS-120B baseline. HASE also repairs imperfect evaluation components and converges to state-of-the-art performance in circle-packing algorithm discovery. These results show that HASE improves the harness and the solution through one unified agentic process.",Haochen Luo; Yi Huang; Sichun Luo; Fengyuan Liu; Lei Li; Zefa Hu; Junlan Feng; Qi Liu,2026-07-04,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.03935,2026-07-17T05:43:07+00:00
+ale-0237,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,auto-harness,https://github.com/neosigmaai/auto-harness,external,github.com,"Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Bring-your-own-agent framework for self-improving agentic systems that mines failures from runs, optimizes the harness in response, and gates every change behind regression checks.",Gives readers a concrete source in auto-harness when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (525 stars; 59 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,739,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L739,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/neosigmaai/auto-harness,"GitHub - neosigmaai/auto-harness: Bring your own agent and build a self-improving agentic system. Automatically mine failures, optimize the agent harness, and gate against regressions. · GitHub","Bring your own agent and build a self-improving agentic system. Automatically mine failures, optimize the agent harness, and gate against regressions. - neosigmaai/auto-harness",,2026-04-03,2026,neosigmaai/auto-harness,GitHub,,,,github-api,neosigmaai/auto-harness,525,59,MIT,2026-04-03T21:18:14Z,2026-07-16T14:02:38Z,,2026-07-17T05:43:07+00:00
+ale-0238,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,OpenAI agent evals,https://developers.openai.com/api/docs/guides/agent-evals,external,developers.openai.com,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation guidance for moving from traces to repeatable grading of agent workflows.,Gives readers a concrete source in OpenAI agent evals when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from developers.openai.com; use it for current product or standard behavior.,high,README.md,744,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L744,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,official-documentation,ok,https://developers.openai.com/api/docs/guides/agent-evals,Evaluate agent workflows | OpenAI API,"Learn how to evaluate agent workflows with traces, graders, datasets, and evaluation runs on the OpenAI platform.",,,,,OpenAI Developers,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0239,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Promptfoo OpenAI Agents provider,https://www.promptfoo.dev/docs/providers/openai-agents/,external,www.promptfoo.dev,"Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Testing and assertions for multi-turn agent workflows, tools, state, handoffs, sandboxes, and traces.",Gives readers a concrete source in Promptfoo OpenAI Agents provider when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,745,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L745,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;delegation;verification;state,builder;evaluator,implementation,ok,https://www.promptfoo.dev/docs/providers/openai-agents/,OpenAI Agents | Promptfoo,"Test OpenAI Agents with tools, handoffs, sessions, sandbox workflows, and tracing in promptfoo.",,,,,promptfoo.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0240,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Inspect AI,https://github.com/UKGovernmentBEIS/inspect_ai,external,github.com,"UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: UK AISI evaluation framework with solvers, scorers, sandboxing, tool use, MCP, and log viewing.",Gives readers a concrete source in Inspect AI when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (2,365 stars; 608 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,746,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L746,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,builder;evaluator,source-implementation,ok,https://github.com/UKGovernmentBEIS/inspect_ai,GitHub - UKGovernmentBEIS/inspect_ai: Inspect: A framework for large language model evaluations · GitHub,Inspect: A framework for large language model evaluations - UKGovernmentBEIS/inspect_ai,,2023-11-14,2023,UKGovernmentBEIS/inspect_ai,GitHub,,,,github-api,UKGovernmentBEIS/inspect_ai,2365,608,MIT,2023-11-14T14:53:11Z,2026-07-17T03:58:31Z,,2026-07-17T05:43:07+00:00
+ale-0241,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,OpenTelemetry Semantic Conventions for Generative AI Systems,https://opentelemetry.io/docs/specs/semconv/gen-ai/,external,opentelemetry.io,"Portable tracing conventions for model calls, tool calls, and agent workflows.","Portable tracing conventions for model calls, tool calls, and agent workflows.","Portable tracing conventions for model calls, tool calls, and agent workflows.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Portable tracing conventions for model calls, tool calls, and agent workflows.",Gives readers a concrete source in OpenTelemetry Semantic Conventions for Generative AI Systems when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from opentelemetry.io; use it for current product or standard behavior.,high,README.md,747,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L747,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,builder;evaluator,official-documentation,ok,https://opentelemetry.io/docs/specs/semconv/gen-ai/,Moved: Generative AI semantic conventions | OpenTelemetry The OpenTelemetry Logo,Important GenAI semantic conventions have moved to the OpenTelemetry GenAI semantic conventions repository. This page has moved and is no longer maintained in this repository.,,,,,OpenTelemetry,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0242,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,AgentOps,https://github.com/AgentOps-AI/agentops,external,github.com,"Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Monitoring, replay, cost tracking, benchmarking, and tracing for agent sessions.",Gives readers a concrete source in AgentOps when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (5,716 stars; 609 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,748,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L748,,Verify,verify,"Gate progress with tests, evals, and evidence.",state;budget,builder;evaluator,source-implementation,ok,https://github.com/AgentOps-AI/agentops,"GitHub - AgentOps-AI/agentops: Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI · GitHub","Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI - AgentOps-AI/agentops",,2023-08-15,2023,AgentOps-AI/agentops,GitHub,,,,github-api,AgentOps-AI/agentops,5716,609,MIT,2023-08-15T23:26:23Z,2026-07-16T18:46:29Z,,2026-07-17T05:43:07+00:00
+ale-0243,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Langfuse,https://github.com/langfuse/langfuse,external,github.com,"Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Open-source LLM engineering platform with tracing, evaluations, and metrics that loops can read back as feedback signals.",Gives readers a concrete source in Langfuse when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (31,302 stars; 3,299 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,749,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L749,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/langfuse/langfuse,"GitHub - langfuse/langfuse: 🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 · GitHub","🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 - GitHub - langfuse/langfuse: 🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23",,2023-05-18,2023,langfuse/langfuse,GitHub,,,,github-api,langfuse/langfuse,31302,3299,NOASSERTION,2023-05-18T17:47:09Z,2026-07-17T05:18:44Z,,2026-07-17T05:43:07+00:00
+ale-0244,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,LangSmith,https://www.langchain.com/langsmith,external,www.langchain.com,"Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Tracing, evaluation, and monitoring platform for inspecting and grading agent runs across iterations.",Gives readers a concrete source in LangSmith when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,750,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L750,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://www.langchain.com/langsmith/observability,LangSmith: Agent & LLM Observability Platform,"Complete AI agent and LLM observability platform with tracing and real-time monitoring. Debug agents, find failures fast, and track costs and latency.",,,,,LangChain,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0245,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Arize Phoenix,https://github.com/Arize-ai/phoenix,external,github.com,"Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Open-source AI observability for tracing, evaluating, and debugging agent behavior from real runs.",Gives readers a concrete source in Arize Phoenix when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (10,595 stars; 988 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,751,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L751,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/Arize-ai/phoenix,GitHub - Arize-ai/phoenix: AI Observability & Evaluation · GitHub,AI Observability & Evaluation. Contribute to Arize-ai/phoenix development by creating an account on GitHub.,,2022-11-09,2022,Arize-ai/phoenix,GitHub,,,,github-api,Arize-ai/phoenix,10595,988,NOASSERTION,2022-11-09T23:44:35Z,2026-07-17T05:13:05Z,,2026-07-17T05:43:07+00:00
+ale-0246,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Braintrust,https://www.braintrust.dev/,external,www.braintrust.dev,"Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation and observability platform with experiments, datasets, and CI integration for gating agent changes.",Gives readers a concrete source in Braintrust when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,752,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L752,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://www.braintrust.dev/,Braintrust - The AI observability platform for building quality AI products,"Ship quality AI at scale. Braintrust is the AI observability platform for tracing production, running evals, and catching regressions before they reach users.",,,,,Braintrust,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0247,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Weave,https://docs.wandb.ai/weave,external,docs.wandb.ai,"Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Weights & Biases toolkit for tracing, evaluating, and monitoring agent applications over time.",Gives readers a concrete source in Weave when they need to measure progress and gate completion with repeatable evidence.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,753,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L753,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,implementation,ok,https://docs.wandb.ai/weave,W&B Weave - Weights & Biases Documentation,"Track, test, and improve language model apps with W&B Weave",,,,,Weights & Biases Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0248,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,agentops (boshu2),https://github.com/boshu2/agentops,external,github.com,"Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Independent verification layer for coding agents where a change only counts as done after a different model or a real test checks it, with the verdict recorded in the repo via a tamper-evident ledger.",Gives readers a concrete source in agentops (boshu2) when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (411 stars; 40 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,754,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L754,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,source-implementation,ok,https://github.com/boshu2/agentops,GitHub - boshu2/agentops: The operating loop a coding agent follows — and skills to orchestrate multi-agent systems. · GitHub,The operating loop a coding agent follows — and skills to orchestrate multi-agent systems. - boshu2/agentops,,2025-11-05,2025,boshu2/agentops,GitHub,,,,github-api,boshu2/agentops,411,40,Apache-2.0,2025-11-05T19:18:56Z,2026-07-17T02:15:43Z,,2026-07-17T05:43:07+00:00
+ale-0249,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,SkillSpec,https://github.com/modiqo/skillspec,external,github.com,"CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: CLI that makes agent skills followable, testable, and provable by converting prose skills into structured contracts, scoring follow-through risk, and generating execution traces of which steps ran, were skipped, and what evidence exists.",Gives readers a concrete source in SkillSpec when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (975 stars; 60 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,755,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L755,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/modiqo/skillspec,"GitHub - modiqo/skillspec: SkillSpec makes agent skills followable, testable, and provable with Doctor risk reports, guided imports, structured contracts, and alignment proof. · GitHub","SkillSpec makes agent skills followable, testable, and provable with Doctor risk reports, guided imports, structured contracts, and alignment proof. - modiqo/skillspec",,2026-06-19,2026,modiqo/skillspec,GitHub,,,,github-api,modiqo/skillspec,975,60,Apache-2.0,2026-06-19T23:42:55Z,2026-07-17T05:08:46Z,,2026-07-17T05:43:07+00:00
+ale-0250,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Shepherd,https://github.com/shepherd-agents/shepherd,external,github.com,"Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Python runtime that records agent execution as reversible, Git-like traces so meta-agents or humans can observe, fork, replay, and revert any run before results touch files, with copy-on-write forking, roughly 95% cache reuse on replay, and syscall-level permission enforcement.",Gives readers a concrete source in Shepherd when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (1,434 stars; 104 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,756,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L756,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;state,builder;evaluator,source-implementation,ok,https://github.com/shepherd-agents/shepherd,"GitHub - shepherd-agents/shepherd: A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents · GitHub","A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents - shepherd-agents/shepherd",,2026-06-24,2026,shepherd-agents/shepherd,GitHub,,,,github-api,shepherd-agents/shepherd,1434,104,MIT,2026-06-24T17:26:46Z,2026-07-16T21:43:18Z,,2026-07-17T05:43:07+00:00
+ale-0251,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,grill-for-unknowns,https://github.com/nicobailon/grill-for-unknowns,external,github.com,"Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Portable SKILL.md agent skill that gates long-running subagent and coding-agent launches behind plan interrogation: it inspects the real territory (docs, source, tests, config) first, sorts what's known into facts, decisions, domain language, and unknowns across known/unknown quadrants, then emits a launch packet with assumptions, verification steps, and rollback risks before dispatch.",Gives readers a concrete source in grill-for-unknowns when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (178 stars; 6 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,757,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L757,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,builder;evaluator,source-implementation,ok,https://github.com/nicobailon/grill-for-unknowns,"GitHub - nicobailon/grill-for-unknowns: Agent skill for finding unknowns, grilling plans, and reaching shared understanding before implementation · GitHub","Agent skill for finding unknowns, grilling plans, and reaching shared understanding before implementation - nicobailon/grill-for-unknowns",,2026-07-09,2026,nicobailon/grill-for-unknowns,GitHub,,,,github-api,nicobailon/grill-for-unknowns,178,6,MIT,2026-07-09T18:55:30Z,2026-07-16T16:09:21Z,,2026-07-17T05:43:07+00:00
+ale-0252,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Fable Harness,https://github.com/Miguok/fable-harness,external,github.com,"Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Drop-in behavior protocol kit (hooks, a skill, and sub-agents auto-injected into every Claude Code session) enforcing a verify-first process: gather evidence before answering and verify changes before declaring done.",Gives readers a concrete source in Fable Harness when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (189 stars; 33 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,758,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L758,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,builder;evaluator,source-implementation,ok,https://github.com/Miguok/fable-harness,"GitHub - Miguok/fable-harness: Make Claude Code work like a disciplined engineer: OODA, multi-party adversarial review, tiered model routing, fail-then-pass — token-efficient by design (route heavy work to smaller models, isolate sub-agent context). Distilled from Fable to reinforce the Opus harness. · GitHub","Make Claude Code work like a disciplined engineer: OODA, multi-party adversarial review, tiered model routing, fail-then-pass — token-efficient by design (route heavy work to smaller models, isolate sub-agent context). Distilled from Fable to reinforce the Opus harness. - Miguok/fable-harness",,2026-07-05,2026,Miguok/fable-harness,GitHub,,,,github-api,Miguok/fable-harness,189,33,MIT,2026-07-05T05:57:40Z,2026-07-16T03:21:33Z,,2026-07-17T05:43:07+00:00
+ale-0253,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Mindwalk,https://github.com/cosmtrek/mindwalk,external,github.com,"Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Local visualization tool that replays Claude Code and Codex session logs as light moving across a 3D map of the repository, making long agent runs inspectable after the fact.",Gives readers a concrete source in Mindwalk when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (761 stars; 45 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,759,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L759,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,builder;evaluator,source-implementation,ok,https://github.com/cosmtrek/mindwalk,GitHub - cosmtrek/mindwalk: A visualization tool that replays coding-agent sessions on a 3D map of your codebase. · GitHub,A visualization tool that replays coding-agent sessions on a 3D map of your codebase. - cosmtrek/mindwalk,,2026-07-09,2026,cosmtrek/mindwalk,GitHub,,,,github-api,cosmtrek/mindwalk,761,45,MIT,2026-07-09T11:41:46Z,2026-07-17T04:55:08Z,,2026-07-17T05:43:07+00:00
+ale-0254,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Waggle,https://github.com/modiqo/waggle,external,github.com,"MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: MCP-native reference layer for agent handoffs: instead of pasting full context between agents, it passes a compact attributed, resolvable reference token that the receiving agent expands on demand.",Gives readers a concrete source in Waggle when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (791 stars; 141 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,760,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L760,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",context;delegation;budget,builder;evaluator,source-implementation,ok,https://github.com/modiqo/waggle,"GitHub - modiqo/waggle: Attributed, resolvable artifact references for agent handoffs — a ~30-byte token instead of pasted context. MCP-native; the reference layer for the agent-harness world. · GitHub","Attributed, resolvable artifact references for agent handoffs — a ~30-byte token instead of pasted context. MCP-native; the reference layer for the agent-harness world. - modiqo/waggle",,2026-07-08,2026,modiqo/waggle,GitHub,,,,github-api,modiqo/waggle,791,141,Apache-2.0,2026-07-08T04:33:09Z,2026-07-17T05:16:51Z,,2026-07-17T05:43:07+00:00
+ale-0255,Verification And Feedback Gates,verification-and-feedback-gates,Tool,🧰,Jacquard,https://github.com/jbwinters/jacquard-lang,external,github.com,"Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Research language designed around the machine-writes, human-verifies contract, using effect-typed signatures so an agent's generated code carries checkable declarations of what it is allowed to touch.",Gives readers a concrete source in Jacquard when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (93 stars; 2 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,761,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L761,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,builder;evaluator,source-implementation,ok,https://github.com/jbwinters/jacquard-lang,GitHub - jbwinters/jacquard-lang: Jacquard is a small programming language designed for a regime in which most code is written by machine-learning models and reviewed by people. · GitHub,Jacquard is a small programming language designed for a regime in which most code is written by machine-learning models and reviewed by people. - jbwinters/jacquard-lang,,2026-07-06,2026,jbwinters/jacquard-lang,GitHub,,,,github-api,jbwinters/jacquard-lang,93,2,Apache-2.0,2026-07-06T23:14:47Z,2026-07-17T05:07:22Z,,2026-07-17T05:43:07+00:00
+ale-0256,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Verification of Software Systems,https://arxiv.org/abs/2511.17330,external,arxiv.org,"Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Pairs a coding agent with a theorem prover (AutoRocq) in a generate-and-validate loop, turning formal proof into the exit gate for trusted automatic programming.",Gives readers a concrete source in Agentic Verification of Software Systems when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2511.17330; inspect its method and evaluation before treating results as production evidence.,medium,README.md,766,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L766,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2511.17330,[2511.17330] Agentic Verification of Software Systems,"Automatically generated code is gaining traction recently, owing to the prevalence of Large Language Models (LLMs). Further, the AlphaProof initiative has demonstrated the possibility of using AI for general mathematical reasoning. Reasoning about computer programs (software) can be accomplished via general mathematical reasoning; however, it tends to be more structured and richer in contexts. This forms an attractive proposition, since then AI agents can be used to reason about voluminous code that gets generated by AI. In this work, we present a first LLM agent, AutoRocq, for conducting program verification. Unlike past works, which rely on extensive training of LLMs on proof examples, our agent learns on-the-fly and improves the proof via an iterative refinement loop. The iterative improvement of the proof is achieved by the proof agent communicating with the Rocq (formerly Coq) theorem prover to get additional context and feedback. The final result of the iteration is a proof derivation checked by the Rocq theorem prover. In this way, our proof construction involves autonomous collaboration between the proof agent and the theorem prover. This autonomy facilitates the search for proofs and decision-making in deciding on the structure of the proof tree. Experimental evaluation on SV-COMP benchmarks and on Linux kernel modules shows promising efficacy in achieving automated program verification. As automation in code generation becomes more widespread, we posit that our proof agent can be potentially integrated with AI coding agents to achieve a generate and validate loop, thus moving closer to the vision of trusted automatic programming.",Haoxin Tu; Huan Zhao; Yahui Song; Mehtab Zafar; Ruijie Meng; Abhik Roychoudhury,2025-11-21,2025,arXiv,arXiv,10.1145/3808164,Camera-ready version appeared in the Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2026),cs.SE,arxiv-api,,,,,,,2511.17330,2026-07-17T05:43:07+00:00
+ale-0257,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance",https://arxiv.org/abs/2603.18096,external,arxiv.org,"Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","Gives readers a concrete source in A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2603.18096; inspect its method and evaluation before treating results as production evidence.,medium,README.md,767,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L767,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.18096,"[2603.18096] A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance","In Agentic AI, Large Language Models (LLMs) are increasingly used in the orchestration layer to coordinate multiple agents and to interact with external services, retrieval components, and shared memory. In this setting, failures are not limited to incorrect final outputs. They also arise from long-horizon interaction, stochastic decisions, and external side effects (such as API calls, database writes, and message sends). Common failures include non-termination, role drift, propagation of unsupported claims, and attacks via untrusted context or external channels. This paper presents an assurance framework for such Agentic AI systems. Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts. Contracts provide machine-checkable verdicts, localize the first violating step, and support deterministic replay. The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations. It also supports structured fault injection at service, retrieval, and memory boundaries to assess containment under realistic operational faults and degraded conditions. Finally, governance is treated as a runtime component, enforcing per-agent capability limits and action mediation (allow, rewrite, block) at the language-to-action boundary. To support comparative evaluations across stochastic seeds, models, and orchestration configurations, the paper defines trace-based metrics for task success, termination reliability, contract compliance, factuality indicators, containment rate, and governance outcome distributions. More broadly, the framework is intended as a common abstraction to support testing and evaluation of multi-agent LLM systems, and to facilitate reproducible comparison across orchestration designs and configurations.",Ciprian Paduraru; Petru-Liviu Bouruc; Alin Stefanescu,2026-03-18,2026,arXiv,arXiv,,,cs.MA,arxiv-api,,,,,,,2603.18096,2026-07-17T05:43:07+00:00
+ale-0258,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Self-Evolving Agents with Anytime-Valid Certificates,https://arxiv.org/abs/2607.00871,external,arxiv.org,"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.",Gives readers a concrete source in Self-Evolving Agents with Anytime-Valid Certificates when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.00871; inspect its method and evaluation before treating results as production evidence.,medium,README.md,768,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L768,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00871,[2607.00871] Self-Evolving Agents with Anytime-Valid Certificates,"Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present \textbf{SEA}, an architecture that confines self-modification to a small steering adapter and a versioned harness around a \emph{frozen} base model and admits each modification only through an anytime-valid gate that emits an auditable certificate against a fixed error budget. Five loop controllers compose published guarantees; because such gates can only \emph{select} among behaviors the frozen base already produces, five verifier-in-the-loop mechanisms -- best-of-$N$, micro-step search, self-authored reproduction oracles, search-layer control, and self-repair -- supply the dense, grader-free signal the gates require, computed from the issue text alone. On a $52$-instance SWE-bench Verified subset across four base models, base capability is the dominant, confound-free effect, and on two strong base models a deliberate no-op-composite control isolates the suite's contribution at $+4$ and $+5$ (\textsc{Glm}~5.2 $24\to28$; \textsc{Gpt} $29\to34$, the $65\%$ best), with event logs confirming that its mechanisms fire and prevent regressions. Results are single-run on expensive evaluations; confirming run-to-run variance and adapting the per-task algorithm mix are future work.",Biswa Sengupta,2026-07-01,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.00871,2026-07-17T05:43:07+00:00
+ale-0259,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Delayed Verification Destabilizes Multi-Agent LLM Belief,https://arxiv.org/abs/2606.27409,external,arxiv.org,"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.",Gives readers a concrete source in Delayed Verification Destabilizes Multi-Agent LLM Belief when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2606.27409; inspect its method and evaluation before treating results as production evidence.,medium,README.md,769,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L769,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.27409,[2606.27409] Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement,"Multi-agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can propagate through the agent network. We model this process as delayed consensus on a graph with grounded corrector nodes. Spectral decomposition by the grounded Laplacian yields a closed-form stability threshold for the verification dose: correction that is too strong or too delayed can turn consensus into oscillation. The most unstable regime occurs when the communication and verification delays coincide; for delay two, the threshold is the inverse golden ratio. The same framework gives a supermodular placement objective and a greedy (1-1/e)-approximation rule for assigning a limited corrector budget to influential nodes. Experiments across five open models confirm the predicted dose-delay oscillations. By contrast, grounded factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed-belief tasks while grounded verification remains stabilizing",Igor Itkin,2026-06-25,2026,arXiv,arXiv,,"20 pages, 5 figures, 1 table. Code and data: https://github.com/YehudaItkin/delayed-verification-llm",cs.MA,arxiv-api,,,,,,,2606.27409,2026-07-17T05:43:07+00:00
+ale-0260,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory,https://arxiv.org/abs/2606.06523,external,arxiv.org,"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.",Gives readers a concrete source in Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2606.06523; inspect its method and evaluation before treating results as production evidence.,medium,README.md,770,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L770,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.06523,[2606.06523] Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory,"Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic capabilities, most agent systems still lack formal methods for specifying, verifying, and debugging their workflow and execution trajectories. This challenge mirrors a long-standing problem in mathematics, where the ambiguity of natural languages (NLs) motivates the development of formal languages (FLs). Inspired by this paradigm, we propose **Lean4Agent**, to the best of our knowledge, the first framework that uses Lean4, a dependent-type FL to model and verify agent behavior. **Lean4Agent** launches **FormalAgentLib**, an extensible Lean4 library for formally modeling and verifying agent workflows' semantic consistency under explicit assumptions, and enabling localization of execution-time failures revealed by trajectories. Building on **FormalAgentLib**, we further develop **LeanEvolve**, which applies results in **FormalAgentLib** to revise workflows to enhance its capability. Extensive experiments on a hard problem subset of SWE-Bench-Verified and a subset of ELAIP-Bench across 5 leading LLMs indicate that the verification-passing workflows outperform the failing ones by an average of **11.94%**, and **LeanEvolve** further improves SWE performance by **7.47%** on average. Furthermore, **Lean4Agent** establishes a foundation for a new field of using expressive dependent-type FL to formally model and verify agent behavior.",Ruida Wang; Jerry Huang; Pengcheng Wang; Xuanqing Liu; Luyang Kong; Tong Zhang,2026-06-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.06523,2026-07-17T05:43:07+00:00
+ale-0261,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Regimes: An Auditable, Held-Out-Gated Improvement Loop",https://arxiv.org/abs/2606.10241,external,arxiv.org,"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","Gives readers a concrete source in Regimes: An Auditable, Held-Out-Gated Improvement Loop when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2606.10241; inspect its method and evaluation before treating results as production evidence.,medium,README.md,771,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L771,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.10241,"[2606.10241] Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph","Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history. We show that an event-sourced agent runtime removes that friction and turns controlled improvement into a first-class workflow. When the agent's state is a deterministic projection of an append-only event log, failures are recorded, a run replays exactly from its log, candidate patches scope to typed pipeline seams, gates are auditable, and every promotion or discard is itself an event. We demonstrate this with Regimes, a loop on the ActiveGraph runtime that diagnoses failed evaluations, proposes a repair at a pipeline point, and promotes it only after static checks, sandbox execution, in-sample evaluation, and held-out validation. The loop is target-agnostic: the same control flow runs against different tasks through a common interface. On LongMemEval-S the dominant failure is not retrieval but reconciliation: the evidence is already in the assembled context, yet the reader answers incorrectly. Across five seeded held-out splits, Regimes discovers reader-prompt repairs that improve final held-out accuracy by +0.05 to +0.10 in four splits and +0.01 in one over-promotion split; two splits are individually significant (seed 5 unadjusted for its sequential promotion structure), and the pooled count is descriptive only, since the splits share one 500-question pool. The durable contributions are ActiveGraph as an auditable substrate that makes controlled improvement loops tractable, the held-out-gated loop it supports, the failure-regime taxonomy routing each failure to a pipeline location (whose marginal value over an unrouted baseline is the primary open question), and the prompt-as-discovery-probe hypothesis.",Yohei Nakajima,2026-06-08,2026,arXiv,arXiv,,"30 pages, 5 figures. Code and committed runs: https://github.com/yoheinakajima/regimes",cs.AI,arxiv-api,,,,,,,2606.10241,2026-07-17T05:43:07+00:00
+ale-0262,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents,https://arxiv.org/abs/2605.22608,external,arxiv.org,"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.",Gives readers a concrete source in Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2605.22608; inspect its method and evaluation before treating results as production evidence.,medium,README.md,772,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L772,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.22608,[2605.22608] Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents,"Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-driven, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.",Asaf Yehudai; Lilach Eden; Michal Shmueli-Scheuer,2026-05-21,2026,arXiv,arXiv,,ACL,cs.CL,arxiv-api,,,,,,,2605.22608,2026-07-17T05:43:07+00:00
+ale-0263,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference,https://arxiv.org/abs/2607.02882,external,arxiv.org,"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.",Gives readers a concrete source in Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.02882; inspect its method and evaluation before treating results as production evidence.,medium,README.md,773,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L773,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.02882,[2607.02882] Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference,"Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncertainty of LLM outputs, complex inter-node dependencies, and heterogeneous tool interactions. Existing agentic workflow optimization and agent enhancement methods primarily rely on trajectory-level feedback. Without explicitly identifying the underlying failure root causes, their resulting repair plans are often insufficiently targeted. We propose FlowFixer, a diagnosis-driven automated repair framework for agentic workflows. FlowFixer first transforms workflow executions into unified symbolic traces and performs symbolic inference to derive executable behavioral specifications that capture node correctness, temporal dependencies, and causal relationships. Based on specification verification, it conducts failure attribution and root cause analysis, and then generates targeted repair patches. To reduce verification costs, FlowFixer further employs a multi-dimensional pre-execution assessment to filter infeasible repairs before dynamic verification. We evaluate FlowFixer on workflow failures collected from three popular development platforms: Dify, Coze and n8n. Results show that FlowFixer achieves a repair success rate of 71.3%, outperforming state-of-the-art baselines by 11.9% to 27.6%. It also improves failure attribution accuracy by 4.8% to 33.1% and root cause analysis accuracy by 15.3% to 38.8%. This work offers a new perspective on reliable diagnosis and repair of agentic workflows through symbolic modeling and inference.",Xuyan Ma; Yawen Wang; Junjie Wang; Xiaofei Xie; Boyu Wu; Mingyang Li; Dandan Wang; Qing Wang,2026-07-03,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.02882,2026-07-17T05:43:07+00:00
+ale-0264,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use,https://arxiv.org/abs/2607.01874,external,arxiv.org,"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.",Gives readers a concrete source in SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.01874; inspect its method and evaluation before treating results as production evidence.,medium,README.md,774,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L774,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.01874,[2607.01874] SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use,"Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both evaluation and training, since an agent may pass through trial and error while selecting distractor skills, skipping required steps, composing workflows incorrectly or omitting final checks. We introduce SkillCoach, a self-evolving rubric framework for evaluating and enhancing agentic skill-use. SkillCoach derives skill-grounded process rubrics from real rollouts and evaluates trajectories along four dimensions: skill selection, skill following, skill composition, and skill-grounded reflection. It keeps the external verifier as a separate outcome signal, allowing process quality to be distinguished from accidental task success. The evolved rubrics further serve as process supervision for selecting high-quality training trajectories. Experiments show that evolved rubrics substantially improve evaluation quality, expose failures hidden by final accuracy, and provide stronger supervision signals than outcome-only filtering for enhancing agentic skill-use.",Jiayin Zhu; Kelong Mao; Yudong Guo; Dengbo He; Sulong Xu; Simiu Gu; Yutao Yue,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.01874,2026-07-17T05:43:07+00:00
+ale-0265,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests,https://arxiv.org/abs/2607.00990,external,arxiv.org,"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.",Gives readers a concrete source in SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.00990; inspect its method and evaluation before treating results as production evidence.,medium,README.md,775,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L775,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00990,[2607.00990] SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests,"Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block for such agents and have been shown useful for patch validation. However, it remains unclear whether BRTs can also help the more central stage of patch generation. We first conduct a preliminary study and find that directly using advanced BRT generators to guide patch generation is not beneficial: fail-to-fail BRTs can mislead agents, while even fail-to-pass BRTs bring limited or negative gains. Our analysis reveals two reasons: fail-to-pass BRTs may cover only one manifestation of the reported issue, leading to partial patches, whereas fail-to-fail BRTs are unreliable as direct patch-generation targets. Motivated by these insights, we propose SWE-Doctor, a software issue resolution agent that guides patch generation with runtime diagnoses derived from multi-faceted BRT executions. SWE-Doctor first generates multi-faceted BRTs for different behavioral requirements stated in the issue, then executes and debugs these BRTs to construct runtime-grounded diagnosis records, and finally uses the diagnoses together with localization information inferred during BRT generation to guide patch generation and reduce partial patches. We evaluate SWE-Doctor on Python bug-fixing issues from the widely adopted SWE-bench Verified and SWE-bench Pro across five LLM backends. SWE-Doctor consistently outperforms existing agents across all 10 LLM-benchmark combinations, achieving average resolution rates of 75.7% on SWE-bench Verified and 59.4% on SWE-bench Pro. In particular, on the more challenging SWE-bench Pro, SWE-Doctor improves the average resolution rate by 8.0-8.9 percentage points over the baseline agents.",Yaoqi Guo; Yang Liu; Jie M. Zhang; Yun Ma; Yiling Lou; Zhenpeng Chen,2026-07-01,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.00990,2026-07-17T05:43:07+00:00
+ale-0266,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation,https://arxiv.org/abs/2607.06273,external,arxiv.org,"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.",Gives readers a concrete source in AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06273; inspect its method and evaluation before treating results as production evidence.,medium,README.md,776,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L776,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06273,[2607.06273] AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation,"Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent repair must recover dynamic trajectories whose early decisions can propagate into later errors and external state changes. Existing automatic remedies address only part of this problem: blind retry adds no diagnosis, outcome feedback says whether a run failed but not where or why, and self-reflection often lacks grounded evidence to prevent the same failure from recurring. We present AgentTether, a run-time repair framework that automates post-run diagnosis and guided recovery without modifying the underlying agent or environment. AgentTether abstracts each run into Transition Units, links them through a dependency-aware Critical Transition Graph, and localizes failure-critical subtrajectories by combining an offline normal-behavior model with a run-local graph detector. It then converts the localized cause into behavior-scoped guidance backed by cross-iteration Repair Memory, and can optionally apply guarded run-time intervention to keep the correction active during re-execution. The same design can be deployed as an offline diagnostic-and-guidance tool or as an online repair layer. We evaluate AgentTether on 261 tau-bench tasks across three domains with Qwen3.7-max, and test cross-model transfer on Banking with GPT-5.4. On the hardest Banking domain, AgentTether repairs 59.04% (49/83) of initially failed Qwen3.7-max tasks and 65.12% (56/86) of initially failed GPT-5.4 tasks. Overall, AgentTether improves repair effectiveness while reducing agent turns and end-to-end approach tokens, suggesting a practical reliability layer that can wrap existing agent deployments, reduce wasted re-execution, and improve recovery without retraining the agent.",Chenyu Zhao; Shenglin Zhang; Wenwei Gu; Yongqian Sun; Dan Pei; Chetan Bansal; Saravan Rajmohan; Minghua Ma,2026-07-07,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.06273,2026-07-17T05:43:07+00:00
+ale-0267,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review,https://arxiv.org/abs/2607.06065,external,arxiv.org,"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.",Gives readers a concrete source in SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06065; inspect its method and evaluation before treating results as production evidence.,medium,README.md,777,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L777,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06065,[2607.06065] SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review,"Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce \textbf{SWE-Review}, a framework for closing this loop with agentic code review. Given an issue and an AI-generated PR, a reviewer agent explores the repository, decides whether the PR should be accepted, and provides structured feedback for revision. We evaluate this setting with our proposed \textbf{SWE-Review-Bench} to measure both review correctness and downstream revision usefulness. We further curate \textbf{SWE-Review-Traj} dataset to study broader applications of agentic review and fill the data-scarcity gap for open reviewer training. Experiments show that agentic review continuously improves PRs through a generate-review-revise loop, outperforms single-turn fixed-context review in both decision accuracy and resolve rate after revision, transfers beyond review to improve issue-resolution models, and enables effective and efficient test-time scaling. These results position agentic code review as a practical mechanism for moving AI coding agents from one-shot PR generation toward closed-loop issue resolution.",Ruoyu Wang; Jierun Chen; Shaowei Wang; Chaofan Tao; Sidi Yang; Yuxin Jiang; Kim-Hui Yap; Lifeng Shang; Xiaohui Li; Haoli Bai,2026-07-07,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.06065,2026-07-17T05:43:07+00:00
+ale-0268,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,"Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode",https://arxiv.org/abs/2607.07405,external,arxiv.org,"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","Gives readers a concrete source in Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode when they need to measure progress and gate completion with repeatable evidence.",Research source arXiv:2607.07405; inspect its method and evaluation before treating results as production evidence.,medium,README.md,778,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L778,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07405,"[2607.07405] Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents","Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive environments, a tool may execute any well-formed call even when the corresponding state transition is forbidden by domain policy. The result is a silent wrong state (a booking cancelled, a passenger count changed, a claim acted on without verification) that neither the tool nor the agent's self-report exposes. We study this failure mode in the $\tau^2$-bench airline domain. On a budget agent, 78% of observed failures are silent wrong-state failures with no tool error, and the aggregate failure rate is reproducible across disjoint seeds, not sampling noise. We then evaluate a lightweight intervention: deterministic, read-only pre-execution gates that inspect the proposed call and current state before allowing a write. A four-gate suite raises full-benchmark success from 29.6% to 42.0% on gpt-4o-mini (+12.4pp; paired task-level bootstrap P=0.0012), and the lift reproduces on a disjoint 15-seed set (+12.3pp; P=0.0008). The effect is concentrated where the gates fire: on the 26/50 firing tasks, success rises by +19.2pp, while movement on the 24 non-firing tasks does not exclude zero. Two negative controls (a self-enforcing retail domain and BFCL) bound the mechanism: gates help when tools are policy-permissive and add little where tools already self-enforce. As suggestive evidence, not a central claim, the same failure mode persists at the frontier: gpt-5.2 at default reasoning still attempts policy-violating writes, and the same suite improves success from 61.2% to 71.6% (+10.4pp; P=0.020; n=5, no replication). The contribution is a bounded evaluation and reliability result: deterministic gates do not guarantee task success, but they can deterministically prevent a known class of silent policy-violating writes at the action boundary.",Vikas Reddy; Sumanth Reddy Challaram; Abhishek Basu,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07405,2026-07-17T05:43:07+00:00
+ale-0269,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Harnessing Code Agents for Automatic Software Verification,https://arxiv.org/abs/2607.06341,external,arxiv.org,"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.",Gives readers a concrete source in Harnessing Code Agents for Automatic Software Verification when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.06341; inspect its method and evaluation before treating results as production evidence.,medium,README.md,779,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L779,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.06341,[2607.06341] Harnessing Code Agents for Automatic Software Verification,"Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large language models (LLMs) promise to generate these proofs automatically, yet existing approaches wire a fixed, human-designed proof strategy into the system and constrain the model to follow it (retrieving premises and predicting tactics one step at a time, or splitting goals by divide-and-conquer), and still prove only a fraction of their target theorems. We show that imposing such a strategy is unnecessary and limiting. Handing the whole lemma to a general LLM code agent (for example, Claude Code), free to choose its own approach, and wrapping it in a verification harness is both simpler and more effective, achieving full coverage: every targeted lemma proved, with no failures and no Coq expert intervention. The agent writes the proofs under feedback and hard constraints from the harness that keep each one sound (accepted only when the prover's kernel closes it), complete (no obligation left unproved or silently dropped), and terminating (no divergent tactics). We evaluate this harness plus code agent along three dimensions. (1) Core logic: on Iris, the state-of-the-art separation logic for concurrent and memory-manipulating programs, Aria proves all 4,257 lemmas of the four core modules and the 217 lemmas verifying Rust's standard libraries built on it, fully automatically. (2) Comparison with prior LLM provers: on reglang, where prior provers manage barely one in eight, Aria proves all 318. (3) Generality: on iris-lean, the unfinished Lean 4 port of Iris, it proves 72 not-yet-ported lemmas, showing the approach is not specific to Coq. A state-of-the-art model (Claude Opus 4.7) can write proofs for verified software development fully and automatically.",Shuangxiang Kan; Shuanglong Kan; Sebastian Ertel,2026-07-07,2026,arXiv,arXiv,,,cs.FL,arxiv-api,,,,,,,2607.06341,2026-07-17T05:43:07+00:00
+ale-0270,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,LLM-as-a-Verifier: A General-Purpose Verification Framework,https://arxiv.org/abs/2607.05391,external,arxiv.org,"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.",Gives readers a concrete source in LLM-as-a-Verifier: A General-Purpose Verification Framework when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05391; inspect its method and evaluation before treating results as production evidence.,medium,README.md,780,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L780,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05391,[2607.05391] LLM-as-a-Verifier: A General-Purpose Verification Framework,"Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training. Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores. This probabilistic formulation enables verification to scale along multiple dimensions: (1) score granularity, (2) repeated evaluation, and (3) criteria decomposition. In particular, we show that scaling the scoring granularity leads to better separation between positive and negative solutions, resulting in more calibrated comparisons. Moreover, scaling repeated evaluation and criteria decomposition consistently lead to additional gains in verification accuracy through variance and complexity reduction. We further introduce a cost-efficient ranking algorithm for selecting the best solution among candidates using the verifier's continuous scores. LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%). Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress. We build an extension for Claude Code, enabling developers to monitor and improve their own agentic systems. Finally, we show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.",Jacky Kwok; Shulu Li; Pranav Atreya; Yuejiang Liu; Yixing Jiang; Chelsea Finn; Marco Pavone; Ion Stoica; Azalia Mirhoseini,2026-07-06,2026,arXiv,arXiv,,Code: https://github.com/llm-as-a-verifier/llm-as-a-verifier Website: https://llm-as-a-verifier.com,cs.AI,arxiv-api,,,,,,,2607.05391,2026-07-17T05:43:07+00:00
+ale-0271,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents,https://arxiv.org/abs/2607.08028,external,arxiv.org,"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.",Gives readers a concrete source in From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.08028; inspect its method and evaluation before treating results as production evidence.,medium,README.md,781,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L781,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08028,[2607.08028] From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents,"Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context. Productization adds requirements for source boundaries, entity routing, answer contracts, and reproducible traces. We present a harness-engineering approach that reconstructs this pattern into a traceable, auditable LLM-agent architecture: deterministic behavior moves into code, manifests, schemas, and validation artifacts around a replaceable composition boundary, while source-backed claims remain the authority for runtime answers. We instantiate it on a public-data slice of five Korean corporate groups (25 listed companies) and evaluate three research questions. (1) The harness preserves its source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts across the fixed validation scenarios; a fault-injection control confirms the validators flag deliberately broken contracts. (2) The checks the harness enforces held under model substitution: across three hosted models, they passed on all 270 composition-boundary runs; failures were confined to the model-composed side and were caught and recorded. (3) The code-owned guarantees are load-bearing, not reproducible by prompting alone: holding the model fixed and varying only the enforcement layer, prompt instructions alone let recommendation-language and internal-trace-leakage violations reach the reader, which the harness blocks entirely. A bolt-on external guardrail prevents such violations too but over-refuses, dropping utility to 88/120 where the harness preserves full utility (120/120); in this ablation, only code-owned enforcement preserves both safety and utility. The result is a reusable engineering pattern for turning exploratory prototypes into auditable applications with versioned source, control, and validation artifacts.",Joongho Ahn; Moonsoo Kim,2026-07-09,2026,arXiv,arXiv,,"32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: https://github.com/hammerbaki/enterprise-llm-agent-harness (archived at https://doi.org/10.5281/zenodo.21269426)",cs.AI,arxiv-api,,,,,,,2607.08028,2026-07-17T05:43:07+00:00
+ale-0272,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization,https://arxiv.org/abs/2607.07702,external,arxiv.org,"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.",Gives readers a concrete source in From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07702; inspect its method and evaluation before treating results as production evidence.,medium,README.md,782,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L782,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07702,[2607.07702] From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization,"The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution traces are difficult to use directly for optimization: large trace collections are often redundant and heterogeneous, making optimization inefficient and prone to overfitting to low-value failures; meanwhile, each individual trajectory also contains many irrelevant steps, while naive context reduction methods such as truncation or sliding windows can discard causally important evidence and produce misleading optimization signals. To resolve this dilemma, we introduce STRACE (Structural TRajectory Analysis and Causal Extraction), a framework that constructs high signal-noise optimization contexts for more precise and effective optimization. At the batch level, STRACE mines failure patterns to filter redundant traces and retain representative failures; within each selected trace, it performs causal localization over a textual dependency graph to remove non-causal steps and identify the true root-cause module for optimization. Empirical results demonstrate that STRACE significantly outperforms standard context-filtering baselines. Notably, on a challenging formal verification task (VeruSAGE-Bench), it successfully optimizes human-expert designed agents, delivering $1.4\times$ success-rate improvement (42.5% to 58.5%). The code is available at https://github.com/moomight/STRACE .",Ying Chang; Jiahang Xu; Xuan Feng; Chenyuan Yang; Peng Cheng; Yuqing Yang,2026-07-08,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.07702,2026-07-17T05:43:07+00:00
+ale-0273,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems,https://arxiv.org/abs/2607.07989,external,arxiv.org,"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.",Gives readers a concrete source in Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07989; inspect its method and evaluation before treating results as production evidence.,medium,README.md,783,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L783,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07989,[2607.07989] Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems,"Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures. When an execution fails, identifying which agent is responsible and at what point the trajectory first becomes irreversibly misdirected is difficult due to long-horizon interactions and tightly coupled agent behaviors. In this paper, we study the problem of failure localization in LLM-based multi-agent systems and present AgentLocate, a framework that attributes failures to both a specific agent and the earliest decisive step. AgentLocate combines an LLM-based judging mechanism with multi-perspective verification by independent evaluators, whose assessments are aggregated using a confidence-aware strategy. The resulting feedback is further used to adapt the judge through lightweight fine-tuning, improving attribution quality. We evaluate AgentLocate on two complementary benchmarks covering diverse tasks, agent configurations, and trajectory lengths. Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.",Yufei Xia; Anjun Gao; Yueyang Quan; Zhuqing Liu; Minghong Fang,2026-07-08,2026,arXiv,arXiv,,To appear in COLM 2026,cs.CR,arxiv-api,,,,,,,2607.07989,2026-07-17T05:43:07+00:00
+ale-0274,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse,https://arxiv.org/abs/2607.07980,external,arxiv.org,"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.",Gives readers a concrete source in 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07980; inspect its method and evaluation before treating results as production evidence.,medium,README.md,784,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L784,,Verify,verify,"Gate progress with tests, evals, and evidence.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07980,[2607.07980] 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse,"Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built. Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable. A motivating observational analysis of public GITHUB activity finds that agent-authored pull requests are reviewed less often, merged several times faster, and discussed less than human-authored ones, yet the direction of these trends flips under different but equally defensible analysis choices, so the traces establish what is changing without explaining why. To recover the mechanisms, we synthesize practitioner discourse at scale into an explanatory theory: we collect 38,709 grey-literature documents (engineering blogs and Reddit threads), filter to those substantively about code review, and code a stratified random sample of 3,100 with an LLM-assisted pipeline, from which we build a causal model of 26 constructs and 67 relationships (64 directed, 3 contested). Its organizing claim is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process. The theory makes the competing positions explicit and turns ""AI is changing code review"" into falsifiable propositions with named constructs and moderators. As a secondary contribution, we offer the underlying LLM-assisted, grey-literature theory-building method as a scalable template for software-engineering research, with a public implementation.",Shyam Agarwal; Courtney Miller; Christian Kästner; Bogdan Vasilescu,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07980,2026-07-17T05:43:07+00:00
+ale-0275,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring,https://arxiv.org/abs/2607.08066,external,arxiv.org,"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.",Gives readers a concrete source in Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.08066; inspect its method and evaluation before treating results as production evidence.,medium,README.md,785,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L785,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08066,[2607.08066] Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring,"Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misaligned or deceptive behavior. While effective in standard scenarios, recent work highlights that LLMs remain vulnerable to persuasion-based jailbreaks, where natural-language arguments override model constraints. We stress-test whether this vulnerability extends to monitoring LLMs: can an adversarial agent persuade its CoT monitor to approve proposed actions that violate the monitor's policy? We design an evaluation framework with 40 tasks and analyze thousands of agent-monitor interactions, where agents are instructed to argue for policy-violating proposals. We find that in such adversarial settings, monitor access to the agent's CoT reasoning increases rather than decreases approval of harmful actions on average by 9.5%, as the scratchpad provides an additional persuasion channel. To address this, we introduce a fact-checking monitoring framework. We find that a fact-checker and monitor pairing from different model families, for example a Claude 3.7 Sonnet monitor paired with a GPT-4.1 fact-checker, reduces approval of policy-violating actions by up to 45%, compared to only 6%, when using the same model for both fact-checking and monitoring roles. Our results demonstrate that CoT monitoring alone may be insufficient against adversarial persuasion, and that model-diverse fact-checking provides a robust mitigation.",Jennifer Za; Julija Bainiaksina; Nikita Ostrovsky; Tanush Chopra; Victoria Krakovna,2026-07-09,2026,arXiv,arXiv,,"25 pages, 10 figures",cs.AI,arxiv-api,,,,,,,2607.08066,2026-07-17T05:43:07+00:00
+ale-0276,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Physics-Audited Agentic Discovery in Scientific Machine Learning,https://arxiv.org/abs/2607.07379,external,arxiv.org,"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.",Gives readers a concrete source in Physics-Audited Agentic Discovery in Scientific Machine Learning when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07379; inspect its method and evaluation before treating results as production evidence.,medium,README.md,786,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L786,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07379,[2607.07379] Physics-Audited Agentic Discovery in Scientific Machine Learning,"In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A low error, however, does not establish that the predicted fields satisfy the physics that matter for mechanics, such as boundary conditions, superposition, stiffness scaling, or causality. We introduce Physics-Audited Agentic SciML (PA-SciML), a verification-first workflow for agentic SciML discovery. The workflow fixes a scoring evaluator before search, derives reviewable machine-checkable physics requirements, checks each trained candidate on its outputs, and separately searches prescribed input ranges or measured load-history spans for high-violation cases without reference solution fields. A surrogate is reported as verified only under the stated checks. When enabled, the workflow also adds advisory numerical probes before training and tests one modeling change at a time to record which isolated edits are associated with score gains before reuse. In the reported computational-solid-mechanics numerical examples, the static elasticity run selects a surrogate with lower validation error than the error-only baseline while both selected models pass the common linear-elastic checks. In the transient elastodynamics run, an error-only baseline with similar mean error fails a stricter causality check by responding to future parts of the loading history, while the selected surrogate passes the stated checks. The main distinction is per-candidate physics evidence on predicted fields, not a richer aggregate score.",Diab W. Abueidda; Bilal Ahmed; Panos Pantidis; Mostafa E. Mobasher,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07379,2026-07-17T05:43:07+00:00
+ale-0277,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair,https://arxiv.org/abs/2607.07882,external,arxiv.org,"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","The resource is directly reusable as a starting artifact. Resource-specific angle: TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.",Gives readers a concrete source in Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.07882; inspect its method and evaluation before treating results as production evidence.,medium,README.md,787,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L787,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07882,[2607.07882] Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair,"Bug reports serve as task specifications for repository-level automated program repair (APR) agents, but they often describe only the observed failure and omit repair-relevant information such as the failure-inducing behavior, behavioral requirement, and implementation scope. As a result, a repair agent may inspect irrelevant code, infer an incorrect requirement, or generate a patch that addresses the reported symptom without restoring the intended repository behavior. We present TrajSpec, a trajectory-guided approach for repository-supported bug report specification refinement. Given an original report and a pre-fix repository, TrajSpec runs a trajectory-collection agent and uses the resulting unverified trajectory as a source of trajectory-derived specification evidence. It organizes this evidence into a three-level representation consisting of a high-level interpretation of the issue, diagnostic findings supporting that interpretation, and concrete repository observations. TrajSpec then generates a draft refined report and applies repository-based review to remove unsupported claims, revise uncertain claims, and add repository-supported details. We evaluate TrajSpec on all 300 SWE-Bench Lite instances using Mini-SWE-Agent V2. TrajSpec's refined reports improve Pass@1 from 41.00% to 59.67% with GPT-5-mini and from 54.67% to 64.33% with MiniMax M2.5. On a stratified sample of 100 instances, TrajSpec's refined reports also improve Pass@1 from 41.00% to 71.00% with Agentless and from 47.00% to 72.00% with AutoCodeRover. Ablation results show that removing repository-based review or the hierarchical evidence representation reduces Pass@1 from 59.67% to 48.00% and 47.67%, respectively. Overall, TrajSpec provides actionable repository-supported context that consistently improves repair performance.",S M Farah Al Fahim; Md Nakhla Rafi; Md Ahasanuzzaman; Zeyang Ma; Dong Jae Kim; Shaowei Wang; Tse-Hsun; Chen,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07882,2026-07-17T05:43:07+00:00
+ale-0278,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Failure as a Process: An Anatomy of CLI Coding Agent Trajectories,https://arxiv.org/abs/2607.09510,external,arxiv.org,"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.",Gives readers a concrete source in Failure as a Process: An Anatomy of CLI Coding Agent Trajectories when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.09510; inspect its method and evaluation before treating results as production evidence.,medium,README.md,788,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L788,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09510,[2607.09510] Failure as a Process: An Anatomy of CLI Coding Agent Trajectories,"Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern. Existing empirical studies investigate why coding agents fail, yet they largely treat failure as a final outcome rather than a temporal process, providing limited insight into how failures emerge, evolve, and become unrecoverable. We present the first large-scale empirical study of CLI coding-agent failure trajectories, introducing a process-oriented framework that analyzes failure through its onset, evolution, and recovery across execution trajectories. We first collect 3,843 execution trajectories generated by seven frontier models across three coding-agent scaffolds (OpenHands, MiniSWE, and Terminus2) on Terminal-Bench, then carefully filter them to obtain 1,794 complete and valid trajectories for manual annotation (over 63,000 execution steps), from which we derive 14 findings spanning failure occurrence, root causes, recovery, and cross-system consistency. Our findings show that coding-agent failures are predominantly driven by epistemic errors, typically begin within the first few execution steps, and often remain hidden until recovery is no longer possible, suggesting that improving coding-agent reliability requires earlier validation and intervention rather than relying solely on final-outcome evaluation.",Xiangxin Zhao; Han Li; Shuaiting Li; Tianyi Zhao; Earl T. Barr; Federica Sarro; He Ye,2026-07-10,2026,arXiv,arXiv,,"12 pages, 6 figures",cs.SE,arxiv-api,,,,,,,2607.09510,2026-07-17T05:43:07+00:00
+ale-0279,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Agentic Proof and Property-Based Testing via Property-Templates,https://arxiv.org/abs/2607.09072,external,arxiv.org,"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.",Gives readers a concrete source in Agentic Proof and Property-Based Testing via Property-Templates when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.09072; inspect its method and evaluation before treating results as production evidence.,medium,README.md,789,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L789,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09072,[2607.09072] Agentic Proof and Property-Based Testing via Property-Templates in Data-Intensive Computing,"As the cost of code generation becomes cheaper with AI, the new bottleneck in software engineering has shifted to intent specification and validation. Overcoming this durability crisis of AI-driven coding requires more than traditional fuzzing: each candidate property must be proven correct over a model and shown to hold on the real implementation, making formal proof and systematic property-based testing (PBT) complementary. However, validating properties this way at scale requires solving two subproblems: verifying candidate properties and operationalizing PBT without AI hallucination. We hypothesize that recurring property patterns, cast as property templates--abstract, parameterized forms with holes--address both at once. This paper investigates recurring property patterns in Apache Spark. In data-intensive scalable computing systems, correctness properties arise from the principles of data partition, computation decomposition, and dataflow computation. For instance, aggregation decomposition relates a global function executed on the entire dataset to a local function followed by a recombiner. We design an agentic, dual-track validation framework that uses property templates to formally verify correctness in the Lean 4 theorem prover and instantiate PBT templates as executable PySpark tests. Our evaluation shows that property templates increase agentic proof engineering success by up to 2.6x (1.6x on average) and reduce proof hallucinations by 59%. Template-guided PBT synthesis reduces intent misalignments from 22 to 1 and cuts synthesis cost by up to 5.7x (3.8x on average). Template-guided synthesis further exceeds a state-of-the-art Spark fuzzer and approaches unguided LLM-based PBT on code coverage. Finally, comparing the two tracks is informative: when a proof succeeds yet a PBT finds a counterexample, the mismatch identifies a gap between the formal model and implementation.",Seongmin Lee; Yaoxuan Wu; Miryung Kim,2026-07-10,2026,arXiv,arXiv,,"12 pages, 7 figures, 4 tables; supplementary material included as ancillary file",cs.SE,arxiv-api,,,,,,,2607.09072,2026-07-17T05:43:07+00:00
+ale-0280,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP,https://arxiv.org/abs/2607.11098,external,arxiv.org,"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.",Gives readers a concrete source in AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.11098; inspect its method and evaluation before treating results as production evidence.,medium,README.md,790,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L790,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11098,[2607.11098] AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP,"Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deployment. We present AgentCheck, an open-source web workbench that turns an MCP server into an intervention surface. AgentCheck runs an agent against its real tools and records every tool response, then re-runs the agent with the response perturbed by a fault (12 types) injector. Matching tool calls are replayed from cache, and later tool calls go live after the agent diverges. This yields a reproduce-intervene-confirm loop: the developer toggles a mitigation, re-runs against the identical fault, and sees if the failure goes away. Scoring has two parts: deterministic pass/fail rules, plus an LLM judge for interpretive labels, validated against human annotations. Across five agents, the best passes 105/120 scenarios and the weakest only 77. The failures are usually silent, confident use of incorrect tool outputs rather than crashes. On the weakest agent, a retry mitigation raises success on timeout error faults from as few as 30% of cases to 100%, whereas stale-data faults remain near 3-4 of 10 regardless of the mitigation. AgentCheck makes these failure modes reproducible, comparable, and verifiable before deployment.",Aritra Mazumder; Nusrat jahan Lia,2026-07-13,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.11098,2026-07-17T05:43:07+00:00
+ale-0281,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Latent Programming Horizons in Coding Agents,https://arxiv.org/abs/2607.05188,external,arxiv.org,"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.",Gives readers a concrete source in Latent Programming Horizons in Coding Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05188; inspect its method and evaluation before treating results as production evidence.,medium,README.md,791,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L791,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05188,[2607.05188] Latent Programming Horizons in Coding Agents,"A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally represents about the program it is working on. We show that the residual streams of language models under coding agents linearly encode properties of the evolving program: a logistic-regression probe on hidden states is able to decode whether the current code parses, passes its test suite, reduces the number of failing tests, and introduces regressions, reaching AUC up to 0.83 for correctness across two models and two benchmarks. Our second finding is more surprising: these representations run ahead of the agent's own edits. Probes trained to predict the outcome of future edits (before they are materialized and written on disk) achieve performance above chance up to roughly 25 steps in advance. We call this the agent's latent programming horizon. As a proof of external validity, we show that the probes transfer across benchmarks without retraining. Our positive results open calls for more research in mechanistic interpretability of coding agents.",André Silva; Han Tu; Martin Monperrus,2026-07-06,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.05188,2026-07-17T05:43:07+00:00
+ale-0282,Verification And Feedback Gates,verification-and-feedback-gates,Docs,📚,Why evaluate agents,https://adk.dev/evaluate/,external,adk.dev,"Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK guide to evaluating final responses and trajectories, defining test cases, selecting criteria, and running repeatable agent evaluations locally or in CI.",Gives readers a concrete source in Why evaluate agents when they need to measure progress and gate completion with repeatable evidence.,Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,792,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L792,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,official-documentation,ok,https://adk.dev/evaluate/,Why evaluate agents - Agent Development Kit (ADK) Agent Development Kit (ADK),Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0283,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Structured Feedback Improves Repair in an LLM Agent Loop,https://arxiv.org/abs/2607.14167,external,arxiv.org,"In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.","The contribution is machine-readable and validation-friendly. Resource-specific angle: In 50 paired TextWorld tasks under a four-call budget, feedback containing the failure location, observed value, and admissible alternatives raises repair success from 14/50 to 36/50 for one model and 8/50 to 29/50 for another; ablations identify alternatives, not JSON syntax, as the main driver.",Gives readers a concrete source in Structured Feedback Improves Repair in an LLM Agent Loop when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14167; inspect its method and evaluation before treating results as production evidence.,medium,README.md,793,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L793,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14167,[2607.14167] Structured Feedback Improves Repair in an LLM Agent Loop,"LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified. We introduce VeriHarness, a code-controlled agent loop in which models generate candidates while external validators control acceptance, budgets, and traces. We use it to compare raw diagnostics with feedback that identifies the failure location, observed value, and admissible alternatives. Across 50 paired TextWorld games under a four-call cap, feedback containing all three fields raises terminal success from 14/50 to 36/50 for Qwen2.5-Coder-14B (+44 percentage points) and from 8/50 to 29/50 for Llama-3.1-8B (+42 points). Ablations locate most of the gain in the admissible alternatives: feedback containing only the location and observed value remains near the raw diagnostic baseline. Presenting the complete repair information in prose instead of a keyed JSON record yields nearly the same success, providing no evidence that JSON syntax itself improves repair. The ordering persists across the tested call budgets and one sampled-decoding setting.",Jaideep Ray; Ankit Goyal,2026-07-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.14167,2026-07-17T05:43:07+00:00
+ale-0284,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,Copy-on-Write Scoring: Application-Specific Agent Evaluations,https://arxiv.org/abs/2607.14336,external,arxiv.org,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Uses PostgreSQL copy-on-write isolation to let an agent modify a realistic application state while a scorer evaluates the resulting operations safely; the Plane case study also exposes tool-surface defects that simpler task checks miss.,Gives readers a concrete source in Copy-on-Write Scoring: Application-Specific Agent Evaluations when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14336; inspect its method and evaluation before treating results as production evidence.,medium,README.md,794,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L794,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14336,[2607.14336] Copy-on-Write Scoring: Application-Specific Agent Evaluations,"Trustworthy deployment of LLM-based agents in software systems requires evaluating how they perform on application-specific workflows, with enough granularity to localize where they succeed and fail. Yet existing agent evaluation mechanisms are limited: benchmarks have low construct validity for application-specific workflows and environments, and replica evaluation environments are expensive and prone to drift. We propose Copy-on-Write (CoW) Scoring, a framework that evaluates agent operations directly within application environments using a PostgreSQL-level Copy-on-Write mechanism to isolate agent writes. CoW Scoring produces session- and operation-level scores that highlight where agents' database write operations succeed and fail in a given application environment, enabling inexpensive evaluation and iteration on agent harnesses and tool surfaces. We demonstrate the framework on Plane, an open-source project-management platform, where analysis surfaced specific issues in the tool surface, and corresponding fixes produced measurable improvements on affected models. Python library: https://github.com/trail-ml/agent-cow-python",Joanna Roy; Sven Hoelzel,2026-07-15,2026,arXiv,arXiv,,"15 pages, 11 figures, accepted at ICML 2026 Second Workshop on Agents in the Wild: Safety, Security, and Beyond",cs.SE,arxiv-api,,,,,,,2607.14336,2026-07-17T05:43:07+00:00
+ale-0285,Verification And Feedback Gates,verification-and-feedback-gates,Paper,📄,The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,https://arxiv.org/abs/2607.14340,external,arxiv.org,"Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Places formal proof obligations inside a coding-agent repair loop and reports 49,280 discharged obligations with 20-40x less supervision; the paper also states that proofs must be paired with known-answer tests, interoperability checks, and human specification review.",Gives readers a concrete source in The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.14340; inspect its method and evaluation before treating results as production evidence.,medium,README.md,795,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L795,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14340,[2607.14340] The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,"AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3, IKEv2, X.509, and a Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an agent can be trusted to establish is bounded by the strength of its feedback.",Tobias Philipp,2026-07-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.14340,2026-07-17T05:43:07+00:00
+ale-0286,Securing Unattended Loops,securing-unattended-loops,Critique,⚠️,The lethal trifecta for AI agents,https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/,external,simonwillison.net,"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.",Gives readers a concrete source in The lethal trifecta for AI agents when they need to bound risk before recurring or unattended execution.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,803,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L803,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,operator;security,risk-analysis,ok,https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/,"The lethal trifecta for AI agents: private data, untrusted content, and external communication",If you are a user of LLM systems that use tools (you can call them “AI agents” if you like) it is critically important that you understand the risk of …,Simon Willison,,2025,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0287,Securing Unattended Loops,securing-unattended-loops,Critique,⚠️,Prompt injection series,https://simonwillison.net/series/prompt-injection/,external,simonwillison.net,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.,Gives readers a concrete source in Prompt injection series when they need to bound risk before recurring or unattended execution.,"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,804,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L804,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake,operator;security,risk-analysis,ok,https://simonwillison.net/series/prompt-injection/,Simon Willison: Prompt injection,,Simon Willison,,,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0288,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Agentic AI - Threats and Mitigations,https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/,external,genai.owasp.org,"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.",Gives readers a concrete source in Agentic AI - Threats and Mitigations when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,805,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L805,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;workspace;context;delegation,builder;operator;security,technical-documentation,ok,https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/,Agentic AI - OWASP Lists Threats and Mitigations,"Explore key threats and mitigation strategies for agentic AI, focusing on security measures to address vulnerabilities in AI applications and their potential risks.",OWASPGenAIProject Editor,,,,OWASP Gen AI Security Project,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0289,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Designing AI agents to resist prompt injection,https://openai.com/index/designing-agents-to-resist-prompt-injection/,external,openai.com,"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","Primary-source operational guidance rather than commentary. Resource-specific angle: OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.",Gives readers a concrete source in Designing AI agents to resist prompt injection when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,806,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L806,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,builder;operator;security,technical-documentation,restricted,https://openai.com/index/designing-agents-to-resist-prompt-injection/,,,,,,,OpenAI,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0290,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,sandbox-runtime,https://github.com/anthropic-experimental/sandbox-runtime,external,github.com,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Anthropic's OS-level filesystem and network sandboxing for arbitrary processes without requiring a container.,Gives readers a concrete source in sandbox-runtime when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (4,685 stars; 363 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,807,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L807,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/anthropic-experimental/sandbox-runtime,"GitHub - anthropic-experimental/sandbox-runtime: A lightweight sandboxing tool for enforcing filesystem and network restrictions on arbitrary processes at the OS level, without requiring a container. · GitHub","A lightweight sandboxing tool for enforcing filesystem and network restrictions on arbitrary processes at the OS level, without requiring a container. - anthropic-experimental/sandbox-runtime",,2025-10-20,2025,anthropic-experimental/sandbox-runtime,GitHub,,,,github-api,anthropic-experimental/sandbox-runtime,4685,363,Apache-2.0,2025-10-20T02:52:10Z,2026-07-17T05:03:55Z,,2026-07-17T05:43:07+00:00
+ale-0291,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,E2B,https://github.com/e2b-dev/E2B,external,github.com,"Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Open-source isolated cloud sandboxes for running untrusted, AI-generated code inside agent loops.",Gives readers a concrete source in E2B when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (13,014 stars; 967 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,808,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L808,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/e2b-dev/E2B,"GitHub - e2b-dev/E2B: Open-source, secure environment with real-world tools for enterprise-grade agents. · GitHub","Open-source, secure environment with real-world tools for enterprise-grade agents. - e2b-dev/E2B",,2023-03-04,2023,e2b-dev/E2B,GitHub,,,,github-api,e2b-dev/E2B,13014,967,Apache-2.0,2023-03-04T13:41:18Z,2026-07-17T04:55:28Z,,2026-07-17T05:43:07+00:00
+ale-0292,Securing Unattended Loops,securing-unattended-loops,Docs,📚,Modal Sandboxes,https://modal.com/docs/guide/sandboxes,external,modal.com,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Secure sandboxed execution for agent-driven code with resource limits and network controls.,Execution isolation and permission boundaries are part of the design. Resource-specific angle: Secure sandboxed execution for agent-driven code with resource limits and network controls.,Gives readers a concrete source in Modal Sandboxes when they need to bound risk before recurring or unattended execution.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,809,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L809,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,technical-documentation,ok,https://modal.com/docs/guide/sandboxes,Sandboxes | Modal Docs,"This page is a high-level guide to Sandboxes, secure containers for executing untrusted user or agent code on Modal.",,,,,Modal,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0293,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Daytona,https://www.daytona.io/,external,www.daytona.io,"Infrastructure for running AI-generated code in fast, isolated sandboxes.","Infrastructure for running AI-generated code in fast, isolated sandboxes.","Infrastructure for running AI-generated code in fast, isolated sandboxes.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Infrastructure for running AI-generated code in fast, isolated sandboxes.",Gives readers a concrete source in Daytona when they need to bound risk before recurring or unattended execution.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,810,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L810,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,implementation,ok,https://www.daytona.io/,Daytona - Secure Infrastructure for Running AI-Generated Code,"Deploy Al code with confidence using Daytona's lightning-fast infrastructure. 90ms environment creation, stateful operations, and enterprise-grade security.",,,,,daytona.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0294,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,peerd,https://github.com/NotASithLord/peerd,external,github.com,"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.",Gives readers a concrete source in peerd when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (359 stars; 35 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,811,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L811,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;delegation,builder;operator;security,source-implementation,ok,https://github.com/NotASithLord/peerd,"GitHub - NotASithLord/peerd: The first AI agent harness native to the browser. A browser extension that runs a full agent loop where you already work: it drives your tabs, spins up sandboxed compute (JS notebooks, WASM Linux VMs, client-side apps), and shares what it builds peer-to-peer. BYOK, no backend, no telemetry. · GitHub","The first AI agent harness native to the browser. A browser extension that runs a full agent loop where you already work: it drives your tabs, spins up sandboxed compute (JS notebooks, WASM Linux VMs, client-side apps), and shares what it builds peer-to-peer. BYOK, no backend, no telemetry. - NotASithLord/peerd",,2026-06-22,2026,NotASithLord/peerd,GitHub,,,,github-api,NotASithLord/peerd,359,35,Apache-2.0,2026-06-22T05:55:13Z,2026-07-16T23:48:05Z,,2026-07-17T05:43:07+00:00
+ale-0295,Securing Unattended Loops,securing-unattended-loops,Paper,📄,When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,https://arxiv.org/abs/2607.05189,external,arxiv.org,"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.",Gives readers a concrete source in When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05189; inspect its method and evaluation before treating results as production evidence.,medium,README.md,812,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L812,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05189,[2607.05189] When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,"Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution. This integration also creates a new path to compromise: untrusted external content can be silently written into persistent memory and later reused as trusted state. We study this threat as stealth memory injection, in which a remote black-box adversary delivers a single email payload that must induce the agent to write poisoned memory, stay hidden in the agent's response to the user, and affect future behavior. We introduce WhisperBench, a 108-case benchmark spanning five risk categories and both fact and preference poisoning. Built on a real IMAP/SMTP workflow and an authentic email agent skill, it enables full-cycle evaluation of stealth memory injection attacks. To enable this black-box attack under single-email delivery and without runtime feedback, we propose MemGhost, a one-shot payload generation framework. MemGhost uses an environment proxy to emulate persistent-agent execution and an objective proxy to convert memory adoption and conversational stealth into dense rubric-based rewards, then trains the attacker policy with supervised fine-tuning and reinforcement learning. Across 56 held-out test cases, MemGhost achieves 87.5% end-to-end success on OpenClaw with GPT-5.4 and 71.4% on Claude Code SDK with Sonnet 4.6. It also transfers across personal-agent architectures (NanoClaw and Hermes Agent) and memory backends (filesystem and vector-based Mem0), and remains effective against input-level, model-level, and system-level defenses. These results suggest that persistent memory can turn ordinary external processing into a practical pathway for long-term agent compromise.",Yechao Zhang; Shiqian Zhao; Jiawen Zhang; Jie Zhang; Gelei Deng; Xiaogeng Liu; Chaowei Xiao; Tianwei Zhang,2026-07-06,2026,arXiv,arXiv,,"25 pages, 8 figures. Preprint",cs.CR,arxiv-api,,,,,,,2607.05189,2026-07-17T05:43:07+00:00
+ale-0296,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,https://arxiv.org/abs/2607.05029,external,arxiv.org,"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.",Gives readers a concrete source in Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05029; inspect its method and evaluation before treating results as production evidence.,medium,README.md,813,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L813,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05029,[2607.05029] Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,"Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context. While this has improved the agent's functionality and continuity across tasks, it has also introduced a new attack surface: the agent's own reasoning history. In this paper, we introduce the Forged Amplifying Rationale Memory Attack (FARMA), which poisons an agent's remembered reasoning rather than its factual knowledge. It inserts forged reasoning traces using evasive language that bypasses keyword-based defenses, then amplifies them through self-referential reinforcement that defeats consensus-based defenses. To address FARMA, we introduce SENTINEL, a layered defense pipeline to detect forged reasoning entries. Its central component is the Reasoning Guard that structurally analyzes candidate entries for forgery using five weighted signals. We evaluate FARMA and SENTINEL across multiple agents and different LLM models with 50 trials and show that FARMA achieves an attack success rate of up to 100% under baseline conditions and is capable of defeating defense mechanisms like keyword filter and A-MemGuard. Our evaluation also shows that SENTINEL reduces FARMA's attack success rate to as low as 0% with no false positives observed across 326 benign agent traces. Our work demonstrates the need to protect not only an agent's retrieved content but also the integrity of its reasoning history.",Neeraj Karamchandani; Piyush Nagasubramaniam; Sencun Zhu; Dinghao Wu,2026-07-06,2026,arXiv,arXiv,,"Preprint. 10 pages, 2 figures, 4 tables",cs.CR,arxiv-api,,,,,,,2607.05029,2026-07-17T05:43:07+00:00
+ale-0297,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Distributed Attacks in Persistent-State AI Control,https://arxiv.org/abs/2607.02514,external,arxiv.org,"Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Extends AI-control evaluation to coding agents shipping code that persists across sessions, showing a misaligned agent can spread an attack across successive PRs to evade per-transcript monitors, and adds a stateful link-tracker monitor that cuts evasion from 93% to 47%.",Gives readers a concrete source in Distributed Attacks in Persistent-State AI Control when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.02514; inspect its method and evaluation before treating results as production evidence.,medium,README.md,814,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L814,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.02514,[2607.02514] Distributed Attacks in Persistent-State AI Control,"As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence creates a new attack surface: a misaligned or prompt-injected agent can distribute attacks across pull requests (PRs) and time its payload for the PR with the best natural cover. To study the resulting dynamics, we introduce Iterative VibeCoding, a setting for AI control, the study of safely deploying capable but potentially untrusted AI. In Iterative VibeCoding, a coding agent builds software over a sequence of PRs in a persistent codebase while pursuing a covert side task. Our benchmark includes two task families: CLI tools and Flask web services, across 20 total task variations. We use Claude Sonnet 4.5 as the attack agent and GPT-4o as the monitor. We compare gradual attacks, which distribute the side task across PRs, against non-gradual attacks concentrated in a single PR. No single monitor is robust to both: which strategy evades best (success while evading the monitor) depends on the monitor type, so a defender cannot close off both gradual and non-gradual attacks with any one monitor. High evasion (>= 65%) generalizes across model attack agent backends (Sonnet 4.5, Gemini 3.1 Pro, Kimi K2.5), confirming this is a property of the persistent-state attack surface rather than a single model's capability. Evasion also remains high across state-of-the-art monitor models and the gap between gradual and non-gradual evasion widens for more capable models. We introduce a stateful link-tracker monitor that tracks suspicious buildup across PRs. On both task families, it detects gradual attacks substantially better than diff monitors that merely see more accumulated history. Combining this stronger monitor with trajectory monitors in a four-monitor ensemble reduces gradual-attack evasion from 93% under the weakest standard diff monitor to 47%.",Josh Hills; Ida Caspary; Asa Cooper Stickland,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02514,2026-07-17T05:43:07+00:00
+ale-0298,Securing Unattended Loops,securing-unattended-loops,Paper,📄,ElephantAgent: Contextual State Continuity in Agentic Systems,https://arxiv.org/abs/2607.01919,external,arxiv.org,"Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Verification protocol that recomputes state digests before each query and logs authorized changes to a trusted-hardware ledger, so an agent's persistent memory and tool descriptions cannot be covertly poisoned between runs and can be rolled back to the last verified state.",Gives readers a concrete source in ElephantAgent: Contextual State Continuity in Agentic Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.01919; inspect its method and evaluation before treating results as production evidence.,medium,README.md,815,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L815,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.01919,[2607.01919] ElephantAgent: Contextual State Continuity in Agentic Systems,"Agentic systems enhance their capabilities by invoking external tools and maintaining persistent memory. However, these external dependencies introduce novel attack surfaces. Recent tool and memory poisoning attacks show that maliciously crafted tool descriptors and poisoned memory can covertly bias agent behavior. These threats reflect a deeper issue: the lack of verifiable continuity in the agent's contextual state for planning and execution. We present ElephantAgent, a protocol that enforces Contextual State Continuity to defend against contextual state poisoning. Inspired by prior state-continuity mechanisms (e.g., Nimble), ElephantAgent extends this protection to the evolving contextual state of agentic systems. We define the contextual state as the bounded, security-critical subset of the agent's entire context (e.g., tool state and memory). Before processing each query, ElephantAgent recomputes the digest of the local contextual state and verifies it against the latest authorized digest. Using replicated trusted hardware, ElephantAgent maintains a linearizable ledger of authorized contextual state transitions and detects out-of-band state tampering. To handle in-band semantic abuse, ElephantAgent additionally provides Historical Traceability, enabling conditional post-hoc audit and recovery to a known-good prior state.",Jiankai Jin; Xiangzheng Zhang; Zhao Liu; Wenzhuo Xu; Dongdong Yang; Deyue Zhang; Quanchen Zou,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.01919,2026-07-17T05:43:07+00:00
+ale-0299,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Cloudflare security-audit-skill,https://github.com/cloudflare/security-audit-skill,external,github.com,"Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.","The contribution is machine-readable and validation-friendly. Resource-specific angle: Cloudflare's open-sourced six-phase audit pipeline in which separate validation agents try to disprove each finding and fresh agents independently verify every claim against source code, emitting schema-validated findings that accumulate across repeated runs.",Gives readers a concrete source in Cloudflare security-audit-skill when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (2,547 stars; 190 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,816,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L816,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,builder;operator;security,source-implementation,ok,https://github.com/cloudflare/security-audit-skill,"GitHub - cloudflare/security-audit-skill: A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings · GitHub","A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings - cloudflare/security-audit-skill",,2026-06-18,2026,cloudflare/security-audit-skill,GitHub,,,,github-api,cloudflare/security-audit-skill,2547,190,MIT,2026-06-18T14:08:44Z,2026-07-17T05:00:47Z,,2026-07-17T05:43:07+00:00
+ale-0300,Securing Unattended Loops,securing-unattended-loops,Paper,📄,The Balkanization of Execution-Security Research for AI Coding Agents,https://arxiv.org/abs/2607.05743,external,arxiv.org,"Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Systematizes 39 papers on the execution layer around coding agents, spanning sandbox isolation, capability control, TOCTOU races, MCP threats, and egress control, surfacing five cross-cutting gaps and four verified CVEs in production agent harnesses.",Gives readers a concrete source in The Balkanization of Execution-Security Research for AI Coding Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.05743; inspect its method and evaluation before treating results as production evidence.,medium,README.md,817,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L817,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.05743,"[2607.05743] The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities","AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe. That literature is scattered. Papers on sandbox isolation, capability and access control, policy enforcement, time-of-check-to-time-of-use (TOCTOU) races, Model Context Protocol (MCP) threats, identity delegation, execution provenance, network egress control, and static analysis of agent-generated code are published independently and rarely cite one another. We systematize 39 papers published between 2023 and 2026 into 17 categories, each verified directly against its source. The same verification protocol also confirms four disclosed, patched CVEs directly affecting production agent harnesses. Reading across categories surfaces five cross-cutting gaps that no single paper addresses. (1) Isolation architectures and capability models are almost never evaluated against one another on a shared benchmark. (2) Policy-enforcement studies report failure rates from 69% to 98% of real denylists, yet no isolation paper re-evaluates its own defense under that adversarial setting. (3) TOCTOU and MCP threats are analyzed as separate literatures despite both being instances of the same state-validation problem. (4) Every enforcement mechanism assumes an honest policy author, leaving policy-authoring error itself unaddressed. (5) Benign but out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting are addressed by no access-control or capability paper in the corpus. Existing broader surveys of agentic AI security discuss sandboxing only as one item among many defenses, leaving execution security without a dedicated systematization. This paper is written to fill that gap. We conclude with a research agenda directed at the five gaps.",Mohammadreza Rashidi,2026-07-07,2026,arXiv,arXiv,,"18 pages, 15 figures, 6 tables. Systematizes 39 execution-security papers (2023-2026) into 17 verified categories. Machine-readable corpus and verification script released as a supplementary artifact",cs.CR,arxiv-api,,,,,,,2607.05743,2026-07-17T05:43:07+00:00
+ale-0301,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Context-to-Execution Integrity for LLM Agents,https://arxiv.org/abs/2607.06000,external,arxiv.org,"Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.",Gives readers a concrete source in Context-to-Execution Integrity for LLM Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.06000; inspect its method and evaluation before treating results as production evidence.,medium,README.md,818,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L818,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.06000,[2607.06000] Context-to-Execution Integrity for LLM Agents,"Language-model agents read attacker-writable context to solve tasks. Tool execution needs a separate authority check for protected sink fields, sink-interpreted payloads, and the invocation event. Context-to-Execution Integrity (CXI) is an execution-boundary system for this setting. Policies mark protected sink fields, typed releases carry narrow validated values from writable context to specific destinations, opaque data slots keep evidence as data, and a deterministic gate admits a call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest. We evaluate CXI on open-weight field-projection runs, AgentDojo live episodes, a code-agent exact-effect benchmark, manifest-bound ledger faults, proposal-pressure controls, and hosted/API compatibility traces. AgentDojo covers 720 live episodes and 1,739 LLM calls; the code-agent benchmark covers 400 repository episodes with exact-effect authorization and lease-bound execution, yielding 231 safe task completions and zero observed field, effect, or invocation escapes. The accounting reports parser outcomes, authorization outcomes, and task-quality outcomes together with the admission-integrity result. Across the evaluated sinks, CXI admits execution only when field, effect, and invocation authority bind to the same action manifest.",Igor Santos-Grueiro,2026-07-07,2026,arXiv,arXiv,,20 pages,cs.CR,arxiv-api,,,,,,,2607.06000,2026-07-17T05:43:07+00:00
+ale-0302,Securing Unattended Loops,securing-unattended-loops,Paper,📄,When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents,https://arxiv.org/abs/2607.06595,external,arxiv.org,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.,Gives readers a concrete source in When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.06595; inspect its method and evaluation before treating results as production evidence.,medium,README.md,819,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L819,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.06595,[2607.06595] When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents,"Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight. When augmented with long-term memory, an agent can recall specific details relevant to the current task, reducing the need for large context windows. Currently, long-term memory agents tend to fall into two distinct domains: conversational and action-planning agents. Personal assistant agents sit at the convergence of these two domains and handle sensitive information while interacting with untrusted information sources, creating previously unaccounted security vulnerabilities. In this work, we introduce the novel attack vector, GhostWriter, which exploits current memory subsystems in tool-using personal agents to poison their memory store. GhostWriter operates in two phases: injection, where an adversary sends a hidden attack payload to the target agent; and activation, in which the poisoned memory is retrieved. We show that GhostWriter achieves near-universal injection rates of approximately 98% and a high average activation rate of approximately 60% against state-of-the-art agents. This attack is possible due to the lack of security-focused memory governance. In response, we propose Agentic Memory Sentry (AM-Sentry), which leverages two mitigation techniques: a memory-saving policy and a memory-retrieval screen. Our experiments show that AM-Sentry dramatically reduces GhostWriter's success rate while preserving agent utility.",George Torres; Sharad Shrestha; Satyajayant Misra,2026-07-06,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.06595,2026-07-17T05:43:07+00:00
+ale-0303,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents,https://arxiv.org/abs/2607.08395,external,arxiv.org,"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.",Gives readers a concrete source in Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08395; inspect its method and evaluation before treating results as production evidence.,medium,README.md,820,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L820,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;state;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08395,[2607.08395] Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents,"Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assistants, unsafe content in these agents can propagate through persistent state, reusable skills, and tool-mediated interactions, creating a substantially larger semantic attack surface. We observe that most security-critical interactions in such agents are transmitted through natural-language token flows, including memory updates, tool arguments, retrieved files, and inter-component communications. This observation enables a new security formulation: unsafe behavior can be intercepted as risky semantic flows before reaching privileged runtime sinks. Based on this insight, we propose TokenWall, a runtime defense framework that acts as a semantic firewall over agent token flows. TokenWall performs boundary-aware semantic auditing over these flows, constructing structured source-sink audit records, applying lightweight local inspection before execution, and selectively escalating ambiguous high-risk cases to stronger arbitration modules. Unlike prior approaches that rely on sparse auditing or remote large-model oversight, TokenWall enables full-coverage pre-execution mediation while reducing remote arbitration and latency. Experiments on CIK-Bench show that TokenWall reduces attack success rate to 12.5% while maintaining a 97.4% benign executable pass rate without human confirmation. TokenWall further introduces only 0.69 seconds of additional latency on benign cases, demonstrating that semantic runtime containment can achieve a practical security-utility trade-off for persistent AI agents.",Puji Wang; Yingchen Zhang; Ruqing Zhang; Jiafeng Guo; Xueqi Cheng,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08395,2026-07-17T05:43:07+00:00
+ale-0304,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Prismata: Confining Cross-Site Prompt Injection in Web Agents,https://arxiv.org/abs/2607.08147,external,arxiv.org,"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.",Gives readers a concrete source in Prismata: Confining Cross-Site Prompt Injection in Web Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08147; inspect its method and evaluation before treating results as production evidence.,medium,README.md,821,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L821,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08147,[2607.08147] Prismata: Confining Cross-Site Prompt Injection in Web Agents,"Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untrusted content is dangerous, even on benign pages. Agents resurface this risk by interpreting natural language as instructions, allowing third-party and user-generated content to hijack the agent via prompt injection. The core challenge is that deriving a task-specific security policy requires reasoning over page structure that is entangled with the attacker's content. We present Prismata, a defense enforcing contextual least privilege for web agents, constraining both what the agent sees and what it can do. Prismata's dynamic trust derivation produces permission labels for page content, with structural confinement guarantees, inspired by classical integrity models, that bound any labeling errors so that labels can only decrease in privilege and mislabelings are bounded. Prismata's mechanical confinement enforces these labels by redacting content and restricting agent capabilities. Importantly, these mechanisms require no developer annotations, so Prismata supports the long tail of websites. Across recent published web agent attacks, including adaptive variants, Prismata substantially reduces attack success while preserving benign task utility.",Corban Villa; Alp Eren Ozdarendeli; Sijun Tan; Raluca Ada Popa,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08147,2026-07-17T05:43:07+00:00
+ale-0305,Securing Unattended Loops,securing-unattended-loops,Paper,📄,TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories,https://arxiv.org/abs/2607.08400,external,arxiv.org,"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.",Gives readers a concrete source in TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08400; inspect its method and evaluation before treating results as production evidence.,medium,README.md,822,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L822,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08400,[2607.08400] TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories,"LLM agents reach users through resellers, who may rebrand a developer's agent or substitute a cheaper model. When provenance is disputed, attribution rests on the trajectory log (the record of tool calls, observations, and executed actions, not the model's reasoning), which the reseller stores and processes to meter usage. A watermark must therefore survive an adversary with full read/write access to the very evidence it is detected from; existing agent watermarks do not, as their attribution is read straight off that log. We present TRACE, to our knowledge the first agent watermark that is distortion-free in its action choices, self-synchronizing under deletion, and unconditionally invariant under rewriting. Deletion desynchronizes a position-derived key and rewriting alters content, so a deletion-robust key must come from content and a rewrite-robust key from position, and no single key serves both. A trajectory, however, has room for two watermarks. TRACE superposes a selection channel that sets which action is chosen, keyed on local content with a distortion-free sampler, so the agent's distribution is provably unchanged and detection resynchronizes after deletions, and a tally channel that sets how many records each decision group holds, keyed on the log's skeleton alone, which no rewriting can touch. We prove this behavioral watermark's signal is bought with decision entropy, each decision paying at least half its entropy and deterministic decisions nothing, and that erasing both channels forces the reseller to corrupt the trajectories it resells. On ToolBench and ALFWorld, TRACE matches the unwatermarked agent's success rate while its selection channel reaches detection scores near z = 100 on long-horizon trajectories, stays detectable under 70% step deletion, and keeps a tally channel exactly unchanged under LLM rewriting of any strength.",Zheng Gao; Xiaoyu Li; Xiaoyan Feng; Jiaojiao Jiang; Yang Song; Yulei Sui; Zhenchang Xing; Liming Zhu,2026-07-09,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.08400,2026-07-17T05:43:07+00:00
+ale-0306,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents,https://arxiv.org/abs/2607.07474,external,arxiv.org,"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.",Gives readers a concrete source in Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07474; inspect its method and evaluation before treating results as production evidence.,medium,README.md,823,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L823,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07474,[2607.07474] Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents,"Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not. We argue that this binary attack-success rate discards the information a defender most needs, namely how harmful the resulting action was. We introduce an action-graded harm rubric that scores an agent's tool-call trajectory on a seven-level ordinal scale (L0 to L6) according to whether the executed action was reversible, whether it crossed scope to reach another party, and whether it expanded privilege. We compute the scale two ways: a deterministic oracle that reads the trajectory and the attacker's stated goal, and a panel of three frontier language-model judges that read a tag-free account of the same trajectory. Across four victim models and two defenses on the AgentDojo workspace suite, severity grading exposes three cases the binary metric hides, including a defense that reports a zero attack-success rate while still permitting an externally visible cross-scope leak through an unfiltered tool. The judge panel reproduces the oracle with high ordinal agreement (Krippendorff's alpha = 0.91) but shares systematic blind spots that we characterize, most notably a failure to recognize escalation chains. Unlike prior work that provides harm taxonomies, harmful-task completion tests, execution-level safety benchmarks, or severity-aware simulation, our contribution is a reusable, trace-grounded severity instrument applied to the actual actions recorded in existing red-team logs. All code, prompts, and per-episode logs are released.",Harry Owiredu-Ashley,2026-07-08,2026,arXiv,arXiv,,"8 pages, 6 figures. Code and artifacts: https://github.com/Harry-Ashley/action-graded-severity",cs.CR,arxiv-api,,,,,,,2607.07474,2026-07-17T05:43:07+00:00
+ale-0307,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting,https://arxiv.org/abs/2607.07433,external,arxiv.org,"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.",Gives readers a concrete source in Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07433; inspect its method and evaluation before treating results as production evidence.,medium,README.md,824,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L824,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07433,[2607.07433] Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting,"The growing adoption of agentic LLM applications has introduced a new threat previously named as promptware. While prior work has established that adversaries can exploit direct channels to LLM applications to apply promptware under weak threat models, many applications do not provide any direct channels that could be exploited for prompt injection beyond the Internet. This raises a question: can attackers exploit LLM applications at scale without any direct channels in practical threat models? In this work, we show that the inherent tendency of LLMs to hallucinate resource identifiers can be exploited to amplify untargeted promptware attacks that pull adversarial prompts at scale and could be exploited to establish a botnet. We introduce adversarial hallucination squatting, a technique in which attackers identify trending resources (e.g., popular repositories, popular skills, etc.), compute the LLM distribution of hallucinations on the trending resource names, and preemptively register them to host adversarial prompts. By leveraging the predictability and transferability of hallucinations across foundational LLMs and to application layers, adversaries can significantly amplify the reach of untargeted promptware under weak threat models and establish a botnet by exploiting LLM applications to install a bot on the device that pulled the compromised hallucinated resource from the Inter. We empirically demonstrate that hallucinated resource generation occurs at high rates, up to 85% in repository cloning scenarios and up to 100% in skill installation, and that these hallucinations transfer between foundational models and different prompts. We demonstrate the practicality of adversarial hallucination squatting against various production LLM applications with integrated terminals in their set of tools, achieving remote tool execution and remote code execution.",Aya Spira; Stav Cohen; Elad Feldman; Ron Bitton; Avishai Wool; Ben Nassi,2026-07-08,2026,arXiv,arXiv,,Website: https://sites.google.com/view/agentic-botnets/home,cs.CR,arxiv-api,,,,,,,2607.07433,2026-07-17T05:43:07+00:00
+ale-0308,Securing Unattended Loops,securing-unattended-loops,Blog,📝,GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos,https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/,external,noma.security,"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.",Gives readers a concrete source in GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos when they need to bound risk before recurring or unattended execution.,"Contextual source from noma.security; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,825,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L825,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake,operator;security,practitioner-analysis,ok,https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/,GitLost: How We Tricked GitHub’s AI Agent into Leaking Private Repos - Noma Security,"TL;DR: Noma Labs discovered a critical prompt injection vulnerability within GitHub’s new Agentic Workflows, allowing an unauthenticated attacker to silently pull data from private repositories by posting a crafted GitHub Issue in a public repository belonging to the same organization as the private repositories. Noma Labs named the vulnerability GitLost. Introduction GitHub recently launched […]",,,,,noma.security,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0309,Securing Unattended Loops,securing-unattended-loops,Benchmark,🧪,ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents,https://arxiv.org/abs/2607.07774,external,arxiv.org,"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.",Gives readers a concrete source in ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents when they need to bound risk before recurring or unattended execution.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,826,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L826,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;budget,researcher;evaluator;operator;security,benchmark,ok,https://arxiv.org/abs/2607.07774,[2607.07774] ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents,"As LLM agents take on offensive security work, a single out-of-scope tool call can breach a client's engagement boundary, disrupt production, or void a bug-bounty finding. Unlike a fixed safety policy, the boundary that matters is declared in the user's request and must be inferred from intent. That challenge is sharpened by the adversarial nature of offensive security: the same tool call is in or out of scope depending not on the action itself but on the target it touches and the context in which it runs, which no fixed policy can enumerate in advance. We study pre-execution gating: a cheap, trusted LLM judge inspects each call proposed by a strong, swappable agent, and accepts or rejects it before it runs. We introduce ScopeJudge, a benchmark of 4,897 tool calls (7.7% scope violations) from agent trajectories on tasks engineered to tempt agents out of scope and labeled at the call level by professional penetration testers, with substantial inter-grader agreement (Fleiss kappa = 0.64) that sets an expert agreement reference point of F1 = 0.78. We evaluate eight judge models under five transcript strategies, varying how much context the judge sees, from the static policy alone to the full raw transcript, and chart the resulting cost-accuracy Pareto frontier. We find that a static policy is structurally insufficient for scope enforcement: blind to the user's request, judge recall collapses to near zero, confirming that scope lives in the request and that request-conditioned monitoring is necessary. Because a missed violation costs more than a spurious rejection, we report precision, recall, and F1 separately and recommend two operating points: a cost-sensitive configuration and a recall-first one for high-stakes deployments. We release the ScopeJudge dataset to support real-time monitoring and scalable oversight of autonomous security agents.",Shane Caldwell; Max Harley; Ads Dawson; Michael Kouremetis; Vincent Abruzzo; Will Pearce,2026-07-08,2026,arXiv,arXiv,,"22 pages, 4 figures, 4 tables",cs.CR,arxiv-api,,,,,,,2607.07774,2026-07-17T05:43:07+00:00
+ale-0310,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors,https://arxiv.org/abs/2607.07368,external,arxiv.org,"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.",Gives readers a concrete source in Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07368; inspect its method and evaluation before treating results as production evidence.,medium,README.md,827,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L827,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;delegation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07368,[2607.07368] Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors,"AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent. AI Control usually studies a single agent in one trajectory, but real deployments run many agents over shared infrastructure, and the most severe risks (model-weight exfiltration, training-run poisoning) plausibly need several agents acting in concert. We initiate the empirical study of multi-agent AI control, formalising distributed attacks in which several agents jointly aim for a malicious goal. We develop FakeLab: a synthetic AI-lab codebase (9 services, 86 benign tasks, 4 attack objectives). We evaluate single agent monitoring against distributed attacks, varying the number of agents, their coordination, model capabilities and precise monitoring configuration. Our central finding is the fragmentation effect: as more agents coordinate to attack, per-agent monitoring becomes less likely to catch any of the attackers. Secondly, this is not driven by the ratio of benign to malicious code -- we argue this is likely dependent on model capability. Third, an explicit planner amplifies the fragmentation effect, raises the rate of completing the attack up to sevenfold, and mildly sharpens executors' poor monitor-theory-of-mind. Finally: a weak ""trusted"" monitor misses most attacks, while a stronger one cuts undetected success by more than an order of magnitude but remains imperfect. We are happy to share FakeLab, our control setting, with safety researchers on request.",Oliver Makins; Orazio Angelini; Zohreh Shams; Mary Phuong,2026-07-08,2026,arXiv,arXiv,,Submitted to NeurIPS; 81 pages; 32 figures and 24 tables,cs.LG,arxiv-api,,,,,,,2607.07368,2026-07-17T05:43:07+00:00
+ale-0311,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions,https://arxiv.org/abs/2607.07461,external,arxiv.org,"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.",Gives readers a concrete source in Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07461; inspect its method and evaluation before treating results as production evidence.,medium,README.md,828,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L828,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07461,[2607.07461] Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions,"Large language models (LLMs) are increasingly deployed as autonomous agents that interact with external tools and services via the Model Context Protocol (MCP), a standardized interface for dynamic tool invocation. While MCP simplifies integration, it also expands the attack surface and enables generic exploits across multiple servers. Despite prior work on malicious MCP servers, the vulnerability landscape of MCP servers remains underexplored. In this work, we systematically analyze MCP server vulnerabilities, focusing on metadata characteristics, vulnerable code patterns, and community responses. Our study reveals that taint-style vulnerabilities constitute a substantial fraction of MCP server vulnerabilities, require significant code modifications to remediate, and are met with slow community responses. Motivated by these findings, we propose SPELLSMITH, presenting a novel textbased avenue for shielding taint-style vulnerabilities in MCP servers. In particular, SPELLSMITH analyzes the high-risk capabilities exposed by an MCP server and combines them with tool descriptions and parameter semantics to identify potential taint-style vulnerability risks, thereby constructing a tool-level risk profile. Then, SPELLSMITH leverages the Description property of the protocol to embed behavioral guidance (Description Enhancement Module) and exploits LLMs' self-reflection capabilities (Self-Reflection Module) to iteratively evaluate and refine outputs. By strengthening LLM internal decision-making, SPELLSMITH provides an active and unified mitigation strategy that generalizes across multiple vulnerabilities, reducing reliance on context-specific code-level fixes. Our experiments demonstrate that SPELLSMITH effectively mitigates taint-style vulnerability exploitation in MCP servers, highlighting its practical applicability and advantages over traditional code-level mitigations.",Yang Shi; Jiaheng Fu; Yihe Huang; Ruixiang Wu; Chengyao Sun; Kaifeng Huang,2026-07-08,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.07461,2026-07-17T05:43:07+00:00
+ale-0312,Securing Unattended Loops,securing-unattended-loops,Blog,📝,Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits,https://factory.ai/news/droid-shield-2-0,external,factory.ai,"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.",Gives readers a concrete source in Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits when they need to bound risk before recurring or unattended execution.,"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,829,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L829,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,operator;security,practitioner-analysis,ok,https://factory.ai/news/droid-shield-2-0,Droid Shield 2.0: learned secret detection | Factory.ai Factory.ai Logo Arrow Right Icon,"Droid Shield 2.0: learned secret detection Factory's Droids write, refactor, and commit code autonomously at a volume th...",Factory,2026-07-01,2026,,Factory,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0313,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,destructive_command_guard,https://github.com/Dicklesworthstone/destructive_command_guard,external,github.com,"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.",Gives readers a concrete source in destructive_command_guard when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (5,033 stars; 189 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,830,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L830,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,builder;operator;security,source-implementation,ok,https://github.com/Dicklesworthstone/destructive_command_guard,GitHub - Dicklesworthstone/destructive_command_guard: The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents. · GitHub,The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents. - Dicklesworthstone/destructive_command_guard,,2026-01-07,2026,Dicklesworthstone/destructive_command_guard,GitHub,,,,github-api,Dicklesworthstone/destructive_command_guard,5033,189,NOASSERTION,2026-01-07T22:27:34Z,2026-07-17T05:40:21Z,,2026-07-17T05:43:07+00:00
+ale-0314,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution,https://ainowinstitute.org/publications/friendly-fire-exploit-brief,external,ainowinstitute.org,"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.",Gives readers a concrete source in Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution when they need to bound risk before recurring or unattended execution.,Research source; inspect its method and evaluation before treating results as production evidence.,medium,README.md,831,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L831,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-paper,ok,https://ainowinstitute.org/publications/friendly-fire-exploit-brief,Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution - AI Now Institute,"AI Now’s latest research demonstrates a critical attack vector on popular AI agents, built by Anthropic and OpenAI, when used for defensive purposes that actually turn the agent against its user.",Boyan Milanov,2026-07-08,2026,,AI Now Institute,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0315,Securing Unattended Loops,securing-unattended-loops,Blog,📝,How We Contain Claude Across Products,https://www.anthropic.com/engineering/how-we-contain-claude,external,www.anthropic.com,"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.",Gives readers a concrete source in How We Contain Claude Across Products when they need to bound risk before recurring or unattended execution.,"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,832,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L832,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,operator;security,practitioner-analysis,ok,https://www.anthropic.com/engineering/how-we-contain-claude,How we contain Claude across products \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0316,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability,https://arxiv.org/abs/2607.11086,external,arxiv.org,"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.",Gives readers a concrete source in Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.11086; inspect its method and evaluation before treating results as production evidence.,medium,README.md,833,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L833,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.11086,[2607.11086] Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability,"The Model Context Protocol (MCP) has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical. In practice, due to the absence of large-scale runtime MCP servers, such understanding largely relies on security scanners applied to a small number of cases, yet the reliability of these assessments remains unclear. In this study, we revisit how MCP security is measured. We present MCPZoo, the largest collection of MCP servers for dynamic analysis to date. MCPZoo is constructed through a multi-agent framework for transforming in-the-wild static repositories into dynamic services. The framework emulates how human experts build, diagnose, and iteratively repair deployment and runtime defects by combining environment inference with feedback-driven refinement. To ensure practical interactivity at runtime, the servers are validated via real protocol interactions. As a result, MCPZoo contains 64,611 unique MCP servers (113,927 in total), with more than 37,288 supporting dynamic analysis. Leveraging MCPZoo, we conduct the first ecosystem-scale measurement of MCP servers and the scanners that analyze them. While existing scanners report that 96.89% of servers are risky, we find that these signals are unreliable. In particular, manual validation shows that less than 50% of sampled alerts are true positives, and scanner outputs exhibit clear inconsistency across scanners. Overall, MCPZoo enables large-scale, reproducible measurement of MCP server security and exposes limitations of current scanning practices. We further release a public query interface to support practical risk assessment of MCP servers.",Pei Chen; Baichao An; Mengying Wu; Binwang Wan; Geng Hong; Jinsong Chen; Xudong Pan; Jiarun Dai; Min Yang,2026-07-13,2026,arXiv,arXiv,,"18 pages, 11 figures, and 10 tables. This article substantially extends the preliminary 3-page MCPZoo dataset release arXiv:2512.15144. Includes appendices",cs.CR,arxiv-api,,,,,,,2607.11086,2026-07-17T05:43:07+00:00
+ale-0317,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming,https://arxiv.org/abs/2607.11698,external,arxiv.org,"Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Uses an autonomous research loop to red-team production agents, having one agent iteratively discover, reproduce, and refine attacks against another, turning red-teaming itself into a recurring verified loop.",Gives readers a concrete source in Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.11698; inspect its method and evaluation before treating results as production evidence.,medium,README.md,834,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L834,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.11698,[2607.11698] Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming,"Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pace with evolving models and tools. Existing approaches mainly optimize attack success and preserve artifacts such as benchmarks, payloads, or attack programs, which record where attacks succeed but not the enabling conditions behind unsafe agent behavior. We study automated red-teaming for production LLM agents using one agentic research environment to discover reusable vulnerability knowledge about another. We present AHA, a falsifiable discovery loop that proposes a vulnerability hypothesis, constructs a falsifier, instantiates a valid attack, executes it in a sandboxed harness, reflects on the trajectory, and promotes confirmed findings into a Vulnerability Concept Graph (VCG). Each concept links an attacker-facing surface to an unsafe trajectory through a claim, enabling condition, falsifier, transfer prediction, and supporting evidence. Across Claude Code and Codex on three scenarios covering direct and indirect attacks, the discovered concepts reveal a reusable vulnerability core across models and agents. A frozen VCG requires no further search and outperforms the strongest frozen discovery baseline by 14.2 percentage points under the same single-shot protocol, while transferring across scenarios and attack channels. The resulting VCG provides an auditable artifact for production safety teams to inspect vulnerabilities, validate patches, and accumulate reusable safety knowledge. Our code is available at https://github.com/henrymao2004/Auto-research-red-teaming-in-sleep.",Xutao Mao; Xiang Zheng; Cong Wang,2026-07-13,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.11698,2026-07-17T05:43:07+00:00
+ale-0318,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents",https://arxiv.org/abs/2607.10487,external,arxiv.org,"Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Proposes binding an agent's authority to the moment of commit rather than the moment of request, so a permission granted mid-run cannot be replayed later to cause irreversible effects in unattended execution.","Gives readers a concrete source in Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.10487; inspect its method and evaluation before treating results as production evidence.,medium,README.md,835,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L835,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.10487,"[2607.10487] Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents","LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result. We study the commit boundary at which earlier authority evidence no longer authorizes a durable effect. We call this property commit-time authorization: a durable effect is authorized only if the witness that licensed its derived state remains fresh, causally prior, bound to the same effect, and eligible at commit time. We build a controlled-invalidation suite spanning browser, tool/API, and multi-agent workflows. The suite preserves the user goal and payload shape while invalidating the authority relation before durability. In the primary 54-task matrix, endpoint success remains high: 262/270 runs reach the visible result. Only 55/270 are authorized completions; among the 216 invalidating rows, 207 commit after the authorizing path has failed. All 54 clean controls remain authorized, and a separate 54-run authority-preserving check produces no unauthorized commits. We then evaluate mitigation families. Prompt caution and single-condition checks are insufficient because different hazards break different boundary conditions. Defenses work when they refresh, rebind, replan, or refuse at the durability boundary. CommitGuard, a fail-closed boundary monitor, blocks stale durable-effect attempts on protected commit surfaces when runtimes emit witness, dependency, binding, and eligibility signals. The result is a reporting and runtime-design lesson: endpoint success is a utility metric; authorized commit is a security property.",Igor Santos-Grueiro,2026-07-11,2026,arXiv,arXiv,,20 pages,cs.CR,arxiv-api,,,,,,,2607.10487,2026-07-17T05:43:07+00:00
+ale-0319,Securing Unattended Loops,securing-unattended-loops,Paper,📄,ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm,https://arxiv.org/abs/2607.10455,external,arxiv.org,"Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Automated auditing framework that probes CLI coding agents for real-world harmful behavior and grades alignment, giving unattended-agent operators a repeatable safety check rather than manual spot review.",Gives readers a concrete source in ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.10455; inspect its method and evaluation before treating results as production evidence.,medium,README.md,836,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L836,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.10455,[2607.10455] ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm,"Autonomous CLI agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight. Does greater autonomy invite greater risk? We introduce ANCHOR, an automated auditing framework that stress-tests CLI agents on illegal tasks grounded in public US court cases. ANCHOR deploys an auditor agent fine-tuned on dark personality data using supervised and reinforcement fine tuning. This auditor roleplays persistent malicious users who decompose tasks, reframe requests upon refusal, and adapt strategies across multi-turn interactions. Evaluating frontier CLI agents, we find that while they often refuse illegal tasks when prompted directly, compliance reaches 100\% under persistent malicious interaction. When agents comply, they frequently exceed user requests, autonomously building infrastructure for large-scale harm, including catastrophic risk scenarios such as large-scale financial fraud and bioweapon development. These findings demonstrate that current alignment techniques are insufficient for autonomous agents and underscore the need for safety evaluations against persistent, adaptive malicious users. We release ANCHOR at https://github.com/garified/anchor",Kefan Song; Yanjun Qi,2026-07-11,2026,"International Conference on Machine Learning, 2026",arXiv,,"Accepted at ICML 2026. 19 pages, 14 figures, 5 tables",cs.AI,arxiv-api,,,,,,,2607.10455,2026-07-17T05:43:07+00:00
+ale-0320,Securing Unattended Loops,securing-unattended-loops,Tool,🧰,Clawk,https://github.com/clawkwork/clawk,external,github.com,"Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.","Execution isolation and permission boundaries are part of the design. Resource-specific angle: Runs coding agents inside disposable, network-restricted Linux VMs so an unattended or untrusted agent's blast radius is confined to a throwaway sandbox.",Gives readers a concrete source in Clawk when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (662 stars; 16 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,837,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L837,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,builder;operator;security,source-implementation,ok,https://github.com/clawkwork/clawk,"GitHub - clawkwork/clawk: Give coding agents a disposable Linux VM, not your laptop · GitHub","Give coding agents a disposable Linux VM, not your laptop - clawkwork/clawk",,2026-07-06,2026,clawkwork/clawk,GitHub,,,,github-api,clawkwork/clawk,662,16,Apache-2.0,2026-07-06T21:30:38Z,2026-07-17T05:17:27Z,,2026-07-17T05:43:07+00:00
+ale-0321,Securing Unattended Loops,securing-unattended-loops,Blog,📝,Auto-Review of Agent Actions Without Synchronous Human Oversight,https://alignment.openai.com/auto-review/,external,alignment.openai.com,"OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: OpenAI's alignment team on reviewing agent actions asynchronously with automated reviewers when a human cannot watch every step, so oversight scales with agent throughput instead of gating it.",Gives readers a concrete source in Auto-Review of Agent Actions Without Synchronous Human Oversight when they need to bound risk before recurring or unattended execution.,"Contextual source from alignment.openai.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,838,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L838,2026-07-15,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation,operator;security,practitioner-analysis,ok,https://alignment.openai.com/auto-review/,"Auto-review of agent actions without synchronous human oversight Illustrative deployment snapshot showing 10,000 actions, most inside the sandbox, and out-of-sandbox actions reviewed by Auto-review.","Auto-review offers a safer default for deploying coding agents, using a separate agent to approve or deny boundary-crossing actions.",Maja Trębacz; Sam Arnesen; Ollie Matthews; Dylan Hurd; Won Park; Owen Lin; Joe Gershenson,2026-04-30,2026,OpenAI,OpenAI,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0322,Securing Unattended Loops,securing-unattended-loops,Paper,📄,SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing,https://arxiv.org/abs/2607.13594,external,arxiv.org,"Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Routes each proposed action among execute, ask a human, and refuse, with one threshold controlling the deployment's risk posture; experiments report stronger overall accuracy and safety recall than the compared baselines.",Gives readers a concrete source in SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13594; inspect its method and evaluation before treating results as production evidence.,medium,README.md,839,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L839,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13594,[2607.13594] SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing,"LLM agents act on real-world environments through tool calls, and a single misjudged action can cause irreversible harm. The standard safeguard is a guard model that labels each proposed action as safe or unsafe, but this binary view conflates two distinct decisions: whether the action is harmful in itself, and whether it is appropriate given the user's context. It also operates at the granularity of action categories rather than individual instances, producing routine interruptions that erode autonomy and train users to wave through the most consequential alerts. We reframe the problem as a per-instance three-way routing decision over {EXECUTE, ASK, REFUSE} and instantiate it with Safety Sentry, a lightweight guard model whose inference reduces to a single decoding call. A single decoding-time threshold lets one fixed checkpoint be re-positioned across deployments of differing risk tolerance without retraining. Safety Sentry outperforms a broad set of open-weight and frontier closed-source baselines on overall accuracy and safety-related recall, while controlling both directional error rates simultaneously.",Tianyu Chen; Chujia Hu; Wenjie Wang,2026-07-15,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.13594,2026-07-17T05:43:07+00:00
+ale-0323,Securing Unattended Loops,securing-unattended-loops,Paper,📄,CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems,https://arxiv.org/abs/2607.13716,external,arxiv.org,"Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Normalizes heterogeneous tool calls into a canonical runtime action object that can be verified and attested before execution; evaluation spans 96 seeds and 384 variants covering approval binding, tampering, and runtime portability.",Gives readers a concrete source in CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13716; inspect its method and evaluation before treating results as production evidence.,medium,README.md,840,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L840,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13716,[2607.13716] CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems,"Agentic AI systems increasingly act through heterogeneous runtimes: local coding hooks, SDK tools, browser automation, managed-agent traces, API gateways, and workflow engines. A single operational act such as publishing code, changing identity state, moving money, or exporting data may therefore be represented by many incompatible runtime records. This makes a basic governance question difficult to answer: what action was actually approved, what evidence binds the approval to execution, and can an independent verifier reproduce the same action identity later? This paper presents Canonical Action Verification and Attestation (CAVA), a runtime-semantics layer for converting heterogeneous agent activity into canonical runtime action objects. CAVA is positioned below Proof-Carrying Agent Actions (PCAA): PCAA defines the deployer-owned route-review-prove governance process, while CAVA defines the stable action object that process governs. The paper formalizes canonical action identity, semantic pattern detection, approval binding, receipt integrity, runtime-portable projection, and optional attestation substrates. We study a reference implementation through a 96-seed, 384-variant benchmark covering semantic equivalence, semantic separation, wrapper bypass, false-positive control, approval binding, receipt reproducibility, attestation tamper detection, runtime portability, semantic pattern detection, policy degradation, and Azure deployment drills. The contribution is a systems formulation of action-level canonicalization and policy-addressable semantic patterns as a necessary substrate for deployer-side AI governance.",Zexun Wang,2026-07-15,2026,arXiv,arXiv,,"35 pages. Working paper on canonical action verification, runtime governance, semantic pattern detection, and approval-bound action receipts",cs.AI,arxiv-api,,,,,,,2607.13716,2026-07-17T05:43:07+00:00
+ale-0324,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement",https://arxiv.org/abs/2607.13718,external,arxiv.org,"Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Surveys 21 permission proposals and compares five commercial agents, producing a taxonomy that connects what users see in permission interfaces to how authority is represented and enforced at runtime.","Gives readers a concrete source in How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.13718; inspect its method and evaluation before treating results as production evidence.,medium,README.md,841,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L841,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13718,"[2607.13718] How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement","As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can cause agents to leak private information to third parties. As autonomous systems, agents also present the more active danger of performing sensitive tasks, such as bank transactions, without the user's intent or authorization. Recognizing this challenge, the agentic security community has developed numerous proposals for secure agentic systems. Much of this work has focused on product-level approaches, where agentic system developers determine and apply the same security policies and permissions to all users. Yet different users have different needs and preferences, necessitating support for user-level permissions policies in agentic AI systems. To understand how user-level permissions are handled in AI agent systems, we survey 21 proposals for agent permissions systems. From this review, we construct a taxonomy of how different systems specify user-level permissions policies, both at the user interface and internally; derive internal policies from user input; and enforce those policies at run-time. We then analyze five prominent commercial agents and compare their permissions handling to agentic permissions systems in the literature. We identify several high-level themes across the literature and commerical agents, as well as multiple gaps where future work is needed.",Alexandra E. Michael; Franziska Roesner,2026-07-15,2026,arXiv,arXiv,,"15 pages, 4 figures",cs.CR,arxiv-api,,,,,,,2607.13718,2026-07-17T05:43:07+00:00
+ale-0325,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,https://arxiv.org/abs/2607.14166,external,arxiv.org,"Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Tests six open-source frameworks and finds their stop or approval controls do not behave as execution barriers; 215 of 1,200 live runs performed a side effect during an approval pause, while the proposed admission gate blocks all measured violations with roughly 1 ms overhead.",Gives readers a concrete source in Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14166; inspect its method and evaluation before treating results as production evidence.,medium,README.md,842,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L842,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14166,[2607.14166] Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,"Production LLM-agent frameworks expose control primitives -- human-in-the-loop approval gates, run cancellation, and execution timeouts -- whose names and documentation imply barrier semantics: while a run is paused, cancelled, or timed out, no gated side effect executes. We show this implied contract holds on none of the six widely used open-source frameworks we test. Model-free differential probes isolate a recurring sibling leak -- an approval gate suspends its own branch while a sibling branch's effect executes during the pause, so a later rejection cannot prevent it -- in every framework shipping a pre-execution gate (five of six), plus three further gaps: replay double-execution, cancellation orphans, and timeout zombies. The hazard is reachable, not merely constructible: under an a-priori-fixed protocol, frontier models emit the leak-triggering plan shape at pooled rates up to 14%, and when live models drive the unmodified frameworks under an approval pause, 215 of 1,200 runs execute their effect during the pause, across three schedulers and two language runtimes. To repair the measured gaps we present SOUNDGATE, an environment-external effect gate in Rust through which every side effect must be admitted, enforcing hold-until-decided, reject-cancels, dedup-on-replay, and fence-on-cancel -- one property per violation class -- under a stated complete-mediation contract discharged for network egress by kernel-enforced routes. We verify the properties over a model of the admission core (Verus; TLA+/TLC, exhaustive to 7.5e7 states; TLAPS), model-check the deployed Rust with Loom, and bridge model to code by differential conformance over 1.2e7 operations -- refinement evidence, not a mechanized proof. SOUNDGATE blocks every measured violation end-to-end on all six frameworks while releasing legitimate effects, at about 1 ms admission per write and 12k-26k durable admissions per second.",Sajjad Khan,2026-07-15,2026,arXiv,arXiv,,"31 pages, 3 figures, 11 tables",cs.SE,arxiv-api,,,,,,,2607.14166,2026-07-17T05:43:07+00:00
+ale-0326,Securing Unattended Loops,securing-unattended-loops,Paper,📄,Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,https://arxiv.org/abs/2607.14611,external,arxiv.org,"Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Evaluates planted memory payloads in Claude Code and Codex across four models, showing that malicious state can affect both current and future sessions and can persist differently across harnesses.",Gives readers a concrete source in Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14611; inspect its method and evaluation before treating results as production evidence.,medium,README.md,843,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L843,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",context;state,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14611,[2607.14611] Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,"A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases. While this makes agents more useful and self-improving, it also creates a new attack surface for prompt injections in which malicious instructions can be embedded within persistent files and influence future behavior. In this work, we study prompt injection attacks in memory-based agentic systems using a sandboxed synthetic workspace. We evaluate two agentic systems, Anthropic Claude Code and OpenAI Codex, across four models: Claude Haiku 4.5, Claude Opus 4.7, GPT-5.2, and GPT-5.5. Our results show that although it is difficult to make an agent overwrite its own memory files using untrusted external content, payloads already planted in those files can successfully attack current and future sessions. Attack success and payload persistence vary substantially across systems, models, adversarial goals, and multi-session attack sequences. These findings show that persistent memory changes the threat model for prompt injection and motivate defenses that protect memory updates without removing useful agent adaptation.",Soham Gadgil; David Alexander; Sai Sunku; Franziska Roesner,2026-07-16,2026,arXiv,arXiv,,Preprint,cs.CR,arxiv-api,,,,,,,2607.14611,2026-07-17T05:43:07+00:00
+ale-0327,Securing Unattended Loops,securing-unattended-loops,Paper,📄,"Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents",https://arxiv.org/abs/2607.15143,external,arxiv.org,"Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Demonstrates five setup-instruction attack classes in 12 scenarios across production coding harnesses, including README and dependency attacks; a deterministic pre-install check closes most of the measured gap.","Gives readers a concrete source in Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.15143; inspect its method and evaluation before treating results as production evidence.,medium,README.md,844,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L844,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.15143,"[2607.15143] Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents","AI coding agents set up projects by reading documentation and installing the dependencies it lists, without verifying their names, sources, or known vulnerabilities. By editing only a README, requirements file, or Makefile, an attacker can redirect the agent to an untrusted registry, a known-vulnerable version, or a wrong-but-plausible name: documentation becomes a vector for code execution. We present the first systematic evaluation of package-install-time supply-chain attacks delivered through ordinary project-setup documentation across production coding-agent harnesses, probing frontier models on twelve scenarios in five attack classes, grounded in documented incidents. The same model catches an attack through one harness and installs it through another: install-time security rests on the harness-model combination, not the model alone. Agents catch blatant typosquats reliably, but plausible separator-confusion names (azurecore for azure-core) slip through, and how often depends on the harness-model pairing. Source-based attacks like registry redirection are missed almost everywhere. The source blind spot recurs on npm and Cargo, where nearly every model installs the untrusted dependency; name detection carries over less consistently across ecosystems. Security-oriented prompts recover part of the gap but only for the dimension they name; a deterministic pre-install check that verifies names, sources, and versions before any code runs closes most of it.",Aadesh Bagmar; Pushkar Saraf,2026-07-16,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.15143,2026-07-17T05:43:07+00:00
+ale-0328,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Effective Context Engineering for AI Agents,https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents,external,www.anthropic.com,Anthropic guide to context as managed runtime state rather than a prompt dump.,Anthropic guide to context as managed runtime state rather than a prompt dump.,Anthropic guide to context as managed runtime state rather than a prompt dump.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Anthropic guide to context as managed runtime state rather than a prompt dump.,"Gives readers a concrete source in Effective Context Engineering for AI Agents when they need to carry context, state, and receipts across runs and failures.","Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,852,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L852,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,technical-documentation,ok,https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents,Effective context engineering for AI agents \ Anthropic,"Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.",,,,,Anthropic,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0329,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs,https://ninadpathak.com/blog/agent-harnesses/,external,ninadpathak.com,"Covers execution loops, state, checkpointing, observers, and replayability.","Covers execution loops, state, checkpointing, observers, and replayability.","Covers execution loops, state, checkpointing, observers, and replayability.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Covers execution loops, state, checkpointing, observers, and replayability.","Gives readers a concrete source in Agent Harnesses: the Infrastructure Layer Your LLM Agent Actually Needs when they need to carry context, state, and receipts across runs and failures.","Contextual source from ninadpathak.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,853,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L853,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,practitioner-analysis,ok,https://ninadpathak.com/blog/agent-harnesses/,Agent Harnesses: the Infrastructure Layer Your Llm Agent Actually Needs | Ninad Pathak,"Every production AI agent needs a harness. Here is what one contains, why frameworks often are not enough, and how to build the layer that actually determines reliability.",,,,,ninadpathak.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0330,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,The Agent Loop Is the New OS,https://www.harness.io/blog/agent-loop-new-os,external,www.harness.io,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Frames the agent loop as an OS-like boundary with context as RAM and tools as I/O.,"Gives readers a concrete source in The Agent Loop Is the New OS when they need to carry context, state, and receipts across runs and failures.","Contextual source from www.harness.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,854,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L854,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context,builder,practitioner-analysis,ok,https://www.harness.io/blog/agent-loop-new-os,The Agent Loop Is the New OS | Harness Blog | Harness Share in X Share in Facebook Share in LinkedIn Search in ChatGpt Github icon LinkedIn icon Facebook icon Instagram icon Twitter icon,"The Harness MCP server treats the AI agent loop as an operating system, mapping the LLM to the CPU and the Context Window to RAM. Learn how this design uses 10 generic, composable tools to abstract complexity and keep the context window clean for higher-quality, cost-efficient AI agent reasoning. | Blog",,2026,2026,,Harness.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0331,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Harness engineering for coding agent users,https://martinfowler.com/articles/harness-engineering.html,external,martinfowler.com,"Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Martin Fowler article on feedforward, feedback, and outer harnesses for coding agents.","Gives readers a concrete source in Harness engineering for coding agent users when they need to carry context, state, and receipts across runs and failures.","Contextual source from martinfowler.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,855,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L855,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://martinfowler.com/articles/harness-engineering.html,Harness engineering for coding agent users,"A mental model for building trust in coding agents through feedforward guides, feedback sensors, and iterative harness engineering.",,,,,martinfowler.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0332,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Context Engineering,https://simonwillison.net/2025/Jun/27/context-engineering/,external,simonwillison.net,"Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Simon Willison's framing of context engineering, useful for distinguishing context state from loop orchestration.","Gives readers a concrete source in Context Engineering when they need to carry context, state, and receipts across runs and failures.","Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,856,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L856,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;state,builder,practitioner-analysis,ok,https://simonwillison.net/2025/Jun/27/context-engineering/,Context engineering,The term context engineering has recently started to gain traction as a better alternative to prompt engineering. I like it. I think this one may have sticking power. Here's an …,Simon Willison,,2025,,Simon Willison’s Weblog,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0333,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentic Coding in 2026,https://sourcegraph.com/blog/agentic-coding,external,sourcegraph.com,"Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Sourcegraph on supplying deterministic, large-codebase context and code intelligence so recurring agent runs reuse durable repository state instead of rediscovering it each time.","Gives readers a concrete source in Agentic Coding in 2026 when they need to carry context, state, and receipts across runs and failures.","Contextual source from sourcegraph.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,857,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L857,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://sourcegraph.com/blog/agentic-coding,Agentic Coding in 2026: A Practical Guide for Big Code | Sourcegraph,"Learn what agentic coding is, how AI coding agents work in real engineering orgs, and how to give them the codebase context they need to ship safely.",,,,,Sourcegraph,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0334,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentic AI State Management with ScyllaDB and LangGraph,https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/,external,www.scylladb.com,"Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable agent state with checkpointers, write-ahead logs, and time-travel branching.","Gives readers a concrete source in Agentic AI State Management with ScyllaDB and LangGraph when they need to carry context, state, and receipts across runs and failures.","Contextual source from www.scylladb.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,858,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L858,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,practitioner-analysis,ok,https://www.scylladb.com/2026/04/08/agentic-ai-state-management-with-scylladb-and-langgraph/,Agentic AI State Management with ScyllaDB and LangGraph | ScyllaDB,"How to combine LangGraph and ScyllaDB for durable state management, crash recovery, and a highly available backend for your agentic AI applications.",Cynthia Dunlop,2026-04-08,2026,,ScyllaDB,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0335,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Mem0,https://github.com/mem0ai/mem0,external,github.com,"Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Open-source memory layer for retaining user, session, and agent state across repeated agent sessions.","Gives readers a concrete source in Mem0 when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (61,020 stars; 7,102 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,859,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L859,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/mem0ai/mem0,GitHub - mem0ai/mem0: Universal memory layer for AI Agents · GitHub,Universal memory layer for AI Agents. Contribute to mem0ai/mem0 development by creating an account on GitHub.,,2023-06-20,2023,mem0ai/mem0,GitHub,,,,github-api,mem0ai/mem0,61020,7102,Apache-2.0,2023-06-20T08:58:36Z,2026-07-17T05:40:12Z,,2026-07-17T05:43:07+00:00
+ale-0336,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Letta,https://github.com/letta-ai/letta,external,github.com,"Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Stateful agent framework from the MemGPT line with persistent, self-editing memory across runs.","Gives readers a concrete source in Letta when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (23,825 stars; 2,527 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,860,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L860,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/letta-ai/letta,GitHub - letta-ai/letta: Platform for stateful agents: AI with advanced memory that can learn and self-improve over time. · GitHub,Platform for stateful agents: AI with advanced memory that can learn and self-improve over time. - letta-ai/letta,,2023-10-11,2023,letta-ai/letta,GitHub,,,,github-api,letta-ai/letta,23825,2527,Apache-2.0,2023-10-11T07:38:37Z,2026-07-17T05:16:36Z,,2026-07-17T05:43:07+00:00
+ale-0337,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Zep,https://github.com/getzep/zep,external,github.com,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Temporal knowledge graph memory that tracks how facts about users and systems change across sessions.,"Gives readers a concrete source in Zep when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (4,759 stars; 641 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,861,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L861,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/getzep/zep,"GitHub - getzep/zep: Zep | Examples, Integrations, & More · GitHub","Zep | Examples, Integrations, & More. Contribute to getzep/zep development by creating an account on GitHub.",,2023-04-29,2023,getzep/zep,GitHub,,,,github-api,getzep/zep,4759,641,Apache-2.0,2023-04-29T14:06:22Z,2026-07-17T03:47:47Z,,2026-07-17T05:43:07+00:00
+ale-0338,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,LangMem,https://github.com/langchain-ai/langmem,external,github.com,"SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: SDK for extracting, consolidating, and retrieving long-term agent memory between loop runs.","Gives readers a concrete source in LangMem when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (1,565 stars; 176 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,862,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L862,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/langchain-ai/langmem,GitHub - langchain-ai/langmem · GitHub,Contribute to langchain-ai/langmem development by creating an account on GitHub.,,2025-01-21,2025,langchain-ai/langmem,GitHub,,,,github-api,langchain-ai/langmem,1565,176,MIT,2025-01-21T20:06:59Z,2026-07-17T02:32:51Z,,2026-07-17T05:43:07+00:00
+ale-0339,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,Beads,https://github.com/steveyegge/beads,external,github.com,"Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","Gives readers a concrete source in Beads when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (25,380 stars; 1,700 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,863,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L863,,Persist,persist,"Carry context, state, and receipts across runs.",intake;context;state,builder,source-implementation,ok,https://github.com/gastownhall/beads,GitHub - gastownhall/beads: Beads - A memory upgrade for your coding agent · GitHub,Beads - A memory upgrade for your coding agent. Contribute to gastownhall/beads development by creating an account on GitHub.,,2025-10-12,2025,steveyegge/beads,GitHub,,,,github-api,steveyegge/beads,25380,1700,MIT,2025-10-12T03:09:46Z,2026-07-17T03:18:10Z,,2026-07-17T05:43:07+00:00
+ale-0340,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,ARC: Active and Reflection-driven Context Management for Long-Horizon Agents,https://arxiv.org/abs/2601.12030,external,arxiv.org,"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","Gives readers a concrete source in ARC: Active and Reflection-driven Context Management for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2601.12030; inspect its method and evaluation before treating results as production evidence.,medium,README.md,864,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L864,,Persist,persist,"Carry context, state, and receipts across runs.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2601.12030,[2601.12030] ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents,"Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histories grow. This degradation, known as context rot, reflects a failure to maintain coherent and task-relevant internal states over extended reasoning horizons. Existing approaches primarily manage context through raw accumulation or passive summarization, treating it as a static artifact and allowing early errors or misplaced emphasis to persist. Motivated by this perspective, we propose ARC, which is the first framework to systematically formulate context management as an active, reflection-driven process that treats context as a dynamic internal reasoning state during execution. ARC operationalizes this view through reflection-driven monitoring and revision, allowing agents to actively reorganize their working context when misalignment or degradation is detected. Experiments on challenging long-horizon information-seeking benchmarks show that ARC consistently outperforms passive context compression methods, achieving up to an 11% absolute improvement in accuracy on BrowseComp-ZH with Qwen2.5-32B-Instruct.",Yilun Yao; Shan Huang; Elsie Dai; Zhewen Tan; Zhenyu Duan; Shousheng Jia; Yanbing Jiang; Tong Yang,2026-01-17,2026,arXiv,arXiv,,"15 pages, 5 figures",cs.AI,arxiv-api,,,,,,,2601.12030,2026-07-17T05:43:07+00:00
+ale-0341,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers",https://arxiv.org/abs/2603.07670,external,arxiv.org,"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","Gives readers a concrete source in Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2603.07670; inspect its method and evaluation before treating results as production evidence.,medium,README.md,865,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L865,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.07670,"[2603.07670] Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers","Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated. Memory -- the ability to persist, organize, and selectively recall information across interactions -- is what turns a stateless text generator into a genuinely adaptive agent. This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026. We formalize agent memory as a \emph{write--manage--read} loop tightly coupled with perception and action, then introduce a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy. Five mechanism families are examined in depth: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. On the evaluation side, we trace the shift from static recall benchmarks to multi-session agentic tests that interleave memory with decision-making, analyzing four recent benchmarks that expose stubborn gaps in current systems. We also survey applications where memory is the differentiating factor -- personal assistants, coding agents, open-world games, scientific reasoning, and multi-agent teamwork -- and address the engineering realities of write-path filtering, contradiction handling, latency budgets, and privacy governance. The paper closes with open challenges: continual consolidation, causally grounded retrieval, trustworthy reflection, learned forgetting, and multimodal embodied memory.",Pengfei Du,2026-03-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.07670,2026-07-17T05:43:07+00:00
+ale-0342,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering",https://arxiv.org/abs/2604.08224,external,arxiv.org,"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","Gives readers a concrete source in Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2604.08224; inspect its method and evaluation before treating results as production evidence.,medium,README.md,866,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L866,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.08224,"[2604.08224] Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable skills, interaction protocols, and the surrounding harness that makes these modules reliable in practice. This paper reviews that shift through the lens of externalization. Drawing on the idea of cognitive artifacts, we argue that agent infrastructure matters not merely because it adds auxiliary components, but because it transforms hard cognitive burdens into forms that the model can solve more reliably. Under this view, memory externalizes state across time, skills externalize procedural expertise, protocols externalize interaction structure, and harness engineering serves as the unification layer that coordinates them into governed execution. We trace a historical progression from weights to context to harness, analyze memory, skills, and protocols as three distinct but coupled forms of externalization, and examine how they interact inside a larger agent system. We further discuss the trade-off between parametric and externalized capability, identify emerging directions such as self-evolving harnesses and shared agent infrastructure, and discuss open challenges in evaluation, governance, and the long-term co-evolution of models and external infrastructure. The result is a systems-level framework for explaining why practical agent progress increasingly depends not only on stronger models, but on better external cognitive infrastructure.",Chenyu Zhou; Huacan Chai; Wenteng Chen; Zihan Guo; Rong Shan; Yuanyi Song; Tianyi Xu; Yingxuan Yang; Aofan Yu; Weiming Zhang; Congming Zheng; Jiachen Zhu; Zeyu Zheng; Zhuosheng Zhang; Xingyu Lou; Changwang Zhang; Zhihui Fu; Jun Wang; Weiwen Liu; Jianghao Lin; Weinan Zhang,2026-04-09,2026,arXiv,arXiv,,"54 pages, tech report on Externalization in LLM Agents",cs.SE,arxiv-api,,,,,,,2604.08224,2026-07-17T05:43:07+00:00
+ale-0343,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Meta Context Engineering via Agentic Skill Evolution,https://arxiv.org/abs/2601.21557,external,arxiv.org,"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","Gives readers a concrete source in Meta Context Engineering via Agentic Skill Evolution when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2601.21557; inspect its method and evaluation before treating results as production evidence.,medium,README.md,867,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L867,,Persist,persist,"Carry context, state, and receipts across runs.",context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2601.21557,[2601.21557] Meta Context Engineering via Agentic Skill Evolution,"The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and predefined context schemas. They impose structural biases and restrict context optimization to a narrow, intuition-bound design space. To address this, we introduce Meta Context Engineering (MCE), a bi-level framework that supersedes static CE heuristics by co-evolving CE skills and context artifacts. In MCE iterations, a meta-level agent refines engineering skills via agentic crossover, a deliberative search over the history of skills, their executions, and evaluations. A base-level agent executes these skills, learns from training rollouts, and optimizes context as flexible files and code. We evaluate MCE across five disparate domains under offline and online settings. MCE demonstrates consistent performance gains, achieving 5.6--53.8% relative improvement over state-of-the-art agentic CE methods (mean of 16.9%), while maintaining superior context adaptability, transferability, and efficiency in both context usage and training.",Haoran Ye; Xuning He; Vincent Arak; Haonan Dong; Guojie Song,2026-01-29,2026,arXiv,arXiv,,"46 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2601.21557,2026-07-17T05:43:07+00:00
+ale-0344,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Are We Ready for an Agent-Native Memory System?,https://arxiv.org/abs/2606.24775,external,arxiv.org,"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","Gives readers a concrete source in Are We Ready for an Agent-Native Memory System? when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.24775; inspect its method and evaluation before treating results as production evidence.,medium,README.md,868,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L868,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.24775,[2606.24775] Are We Ready For An Agent-Native Memory System?,"Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box. As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from a data management perspective. We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets. Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck. Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability. Finally, we reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization. Based on these findings, we identify promising directions towards building truly agent-native memory systems. The code is publicly available at https://github.com/OpenDataBox/MemoryData.",Wei Zhou; Xuanhe Zhou; Shaokun Han; Hongming Xu; Guoliang Li; Zhiyu Li; Feiyu Xiong; Fan Wu,2026-06-23,2026,arXiv,arXiv,,Paper list available at: https://github.com/OpenDataBox/awesome-agent-memory. Source code available at: https://github.com/OpenDataBox/MemoryData,cs.CL,arxiv-api,,,,,,,2606.24775,2026-07-17T05:43:07+00:00
+ale-0345,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Self-Evolving World Models for LLM Agent Planning,https://arxiv.org/abs/2606.30639,external,arxiv.org,"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","Gives readers a concrete source in Self-Evolving World Models for LLM Agent Planning when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.30639; inspect its method and evaluation before treating results as production evidence.,medium,README.md,869,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L869,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.30639,[2606.30639] Self-Evolving World Models for LLM Agent Planning,"World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignored, misused, or even degrade downstream decision-making. In this paper, we introduce WorldEvolver, a self-evolving world model framework that revises its deployment-time context while keeping the downstream agent and all model parameters frozen. WorldEvolver integrates three modules: (i) Episodic Memory, which exploits real action transitions through retrieval-based simulation; (ii) Semantic Memory, which extracts persistent heuristic rules from prediction-observation mismatches; and (iii) Selective Foresight, which filters low-confidence predictions before integrating them into agent reasoning context. We evaluate WorldEvolver on ALFWorld and ScienceWorld, measuring world model prediction accuracy on Word2World and downstream agent success rate on AgentBoard. Extensive experiments show that WorldEvolver achieves the highest prediction accuracy across three backbones and leads other world model baselines on downstream agent success rate, demonstrating that test-time memory revision enhances both predictive fidelity and planning performance.",Xuan Zhang; Wenxuan Zhang; See-Kiong Ng; Yang Deng,2026-06-29,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.30639,2026-07-17T05:43:07+00:00
+ale-0346,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Rethinking Continual Experience Internalization for Self-Evolving LLM Agents,https://arxiv.org/abs/2606.04703,external,arxiv.org,"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","Gives readers a concrete source in Rethinking Continual Experience Internalization for Self-Evolving LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2606.04703; inspect its method and evaluation before treating results as production evidence.,medium,README.md,870,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L870,,Persist,persist,"Carry context, state, and receipts across runs.",workspace,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2606.04703,[2606.04703] Rethinking Continual Experience Internalization for Self-Evolving LLM Agents,"Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital dimensions of experience internalization: (1) Experience Granularity: We find that principle-level experience is more durable than instance-level experience, as it effectively abstracts transferable strategies away from trajectory-specific details. (2) Experience Injection Pattern: Our analysis reveals that step-wise injection significantly outperforms global injection by aligning experience with intermediate decision states, a property that is critical for long-horizon tool use. (3) Internalization Regime: We demonstrate that off-policy context-distillation on high-quality teacher trajectories provides a substantially more stable training signal than on-policy context-distillation, which is inherently limited by local corrections on student-induced flawed states. Together, these insights yield a simple yet robust recipe for stable and sustainable experience internalization, providing concrete guidance for engineering self-evolving and continually learning LLMs.",Jingwen Chen; Wenkai Yang; Shengda Fan; Wenbo Nie; Chenxing Sun; Shaodong Zheng; Yangen Hu; Lu Pan; Ke Zeng; Yankai Lin,2026-06-03,2026,arXiv,arXiv,,"10 pages, 8 figures",cs.CL,arxiv-api,,,,,,,2606.04703,2026-07-17T05:43:07+00:00
+ale-0347,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,GenericAgent,https://github.com/lsdefine/GenericAgent,external,github.com,"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","Gives readers a concrete source in GenericAgent when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (13,461 stars; 1,558 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,871,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L871,,Persist,persist,"Carry context, state, and receipts across runs.",objective;context,builder,source-implementation,ok,https://github.com/lsdefine/GenericAgent,"GitHub - lsdefine/GenericAgent: Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption · GitHub","Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption - lsdefine/GenericAgent",,2026-01-16,2026,lsdefine/GenericAgent,GitHub,,,,github-api,lsdefine/GenericAgent,13461,1558,MIT,2026-01-16T15:45:24Z,2026-07-17T05:00:04Z,,2026-07-17T05:43:07+00:00
+ale-0348,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Self-GC: Self-Governing Context for Long-Horizon LLM Agents,https://arxiv.org/abs/2607.00692,external,arxiv.org,"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","Gives readers a concrete source in Self-GC: Self-Governing Context for Long-Horizon LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.00692; inspect its method and evaluation before treating results as production evidence.,medium,README.md,872,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L872,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.00692,[2607.00692] Self-GC: Self-Governing Context for Long-Horizon LLM Agents,"Long-horizon LLM agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix. Current systems mostly rely on in-run heuristics such as chronological pruning and tool-output masking, or on final self-summary near a context limit. Heuristics are cheap but blind to future dependencies; summaries preserve narrative state but often hide exact evidence, locators, and editable artifacts. We present Self-GC, where GC denotes self-governing context while deliberately echoing garbage collection: the system does not merely reclaim unused tokens, but governs the lifecycle of agent context objects. Self-GC turns user turns, tool spans, and skill state into indexed objects; asks a side-channel planner to propose fold, mask, and prune actions; and lets the harness enforce recoverable sidecars, safe commit boundaries, and cache-aware commit. On a 33-session Hard Set, Self-GC prunes 43.95% of prefix tokens while leaving 84.85% of future continuations unaffected, compared with no-impact rates of 54.55% to 69.70% for heuristic baselines. On a 332-session production-derived suite, three planner backbones reach no-impact rates of 91.27% to 94.58%, while baselines remain at 77.71% to 87.46%. In production, an online account-level split reduces daytime average input tokens by 10% to 15%, with peak reductions near 20%. These results point to context management as runtime lifecycle control over indexed, recoverable objects rather than post hoc text cleanup.",Xubin Hao; Hongjin Meng; Xin Yin; Jiawei Zhu; Chenpeng Cao,2026-07-01,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.00692,2026-07-17T05:43:07+00:00
+ale-0349,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents,https://arxiv.org/abs/2607.05378,external,arxiv.org,"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","Gives readers a concrete source in CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.05378; inspect its method and evaluation before treating results as production evidence.,medium,README.md,873,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L873,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05378,[2607.05378] CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents,"Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed. Context compaction offers a natural solution by summarizing previous interaction states and continuing the rollout under a compressed context, but incorporating compaction into reinforcement learning remains underexplored. We propose CompactionRL, a reinforcement learning strategy to train long-horizon agentic LLMs with context compaction. Our approach jointly optimizes task execution and summary generation with token-level loss normalization and cross-trajectory generalized advantage estimation. This design enables the LLM agents to learn from compacted long-horizon trajectories. We train CompactionRL on top of open models and observe consistent performance gains on agentic coding tasks. CompactionRL enables the open GLM-4.5-Air model (106B-A30B) to achieve Pass@1 scores of 66.8% on SWE-bench Verified and 24.5% on Terminal-Bench 2.0, with absolute gains of 7.0 and 3.1 points, respectively. Built upon GLM-4.7-Flash (30B-A3B), CompactionRL improves Pass@1 by 5.5 and 6.8 points, reaching 56.0% on SWE-bench Verified and 20.2% on Terminal-Bench 2.0, respectively. CompactionRL is thus deployed in the RL pipeline for training the open GLM-5.2 model (750B-A40B).",Yujiang Li; Zhenyu Hou; Yi Jing; Jie Tang; Yuxiao Dong,2026-07-06,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.05378,2026-07-17T05:43:07+00:00
+ale-0350,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,SelfMem: Self-Optimizing Memory for AI Agents,https://arxiv.org/abs/2607.03726,external,arxiv.org,"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Primary-source operational guidance rather than commentary. Resource-specific angle: Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","Gives readers a concrete source in SelfMem: Self-Optimizing Memory for AI Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.03726; inspect its method and evaluation before treating results as production evidence.,medium,README.md,874,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L874,,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.03726,[2607.03726] SelfMem: Self-Optimizing Memory for AI Agents,"While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory systems to effectively leverage historical experience. Existing memory frameworks typically rely on fixed storage, retrieval, and summarization mechanisms, which can be rigid across different tasks and often require manual tuning. To address this limitation, we propose SelfMem, a self-optimizing memory framework. Inspired by prior work on self-improving AI, we follow the principle of ""teaching an agent to fish rather than giving it a fish."" Instead of forcing the model to follow a predefined memory strategy or format, SelfMem provides an environment with memory tools and feedback signals that allow the agent to explore, evaluate, and refine its own memory strategy. Our results show that SelfMem consistently outperforms retrieval, compression, and agent-memory baselines on BEAM across conversation scales from 100K to 1M tokens. Compared with the strongest baseline, SelfMem improves the official score by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M, respectively. Further question-type analysis shows broad robustness across diverse memory demands, and our optimization study shows that model-guided strategy refinement further improves performance.",Shu Yang; Junchao Wu; Derek F. Wong; Di Wang,2026-07-04,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.03726,2026-07-17T05:43:07+00:00
+ale-0351,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture,https://arxiv.org/abs/2607.04391,external,arxiv.org,"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","Gives readers a concrete source in Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.04391; inspect its method and evaluation before treating results as production evidence.,medium,README.md,875,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L875,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.04391,[2607.04391] Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture,"Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database. MOSS is model-agnostic, storage-agnostic, and API-agnostic: it runs on any relational engine, connects to any LLM provider (or to deterministic non-LLM processes), and deploys on any infrastructure, local or cloud. Its retrieval execution is symbolic and reproducible (once a query is formulated, no LLM participates in the retrieval loop) and every step of the system, from indexing to answer formulation, is logged and inspectable, making MOSS auditable by construction. Rather than imposing an external ontology, MOSS derives its conceptual vocabulary from the corpus itself. We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued documents, 569 inductively derived concepts, 322,662 concept annotations, and eleven metadata graphs totaling approximately five million relations--across four successive infrastructure generations. While the present case is that of a single researcher, the architecture is in no way specific to one person: it serves a team, an institution, or any entity that accumulates knowledge over time. We argue that auditable, sovereign, structurally unbounded memory is a precondition for AI agents intended to accompany a person or an organization over years rather than sessions.",Serge Lacasse; Jérémie Hatier; Alex Baker,2026-07-05,2026,arXiv,arXiv,,"22 pages, 2 figures",cs.CL,arxiv-api,,,,,,,2607.04391,2026-07-17T05:43:07+00:00
+ale-0352,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems",https://arxiv.org/abs/2605.21997,external,arxiv.org,"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","Gives readers a concrete source in The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2605.21997; inspect its method and evaluation before treating results as production evidence.,medium,README.md,876,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L876,,Persist,persist,"Carry context, state, and receipts across runs.",objective;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.21997,"[2605.21997] The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems","Most agent frameworks are built around the language model: a conversation loop comes first, then tools, then rules, and finally a logging layer bolted on for observability, with state persisted as retrievable ""memory."" We describe ActiveGraph, a runtime that inverts this arrangement. The append-only event log is the source of truth; the working graph is a deterministic projection of that log; and behaviors--ordinary functions, classes, LLM-backed routines, or logic attached to typed edges--react to changes in the graph and emit new events. No component instructs another; coordination happens entirely through the shared graph. This single design decision yields three properties that retrieval-and-summarization memory systems do not provide: deterministic replay of any run from its log, cheap forking that branches a run at any event without re-executing the shared prefix, and end-to-end lineage from a high-level goal down to the individual model call that produced each artifact. We present the architecture, a determinism contract that makes replay sound, and a worked diligence example whose full causal structure is reconstructable from the log alone. We discuss--without claiming to demonstrate--why this substrate is unusually well suited to self-improving agents, and how it extends the BabyAGI lineage and prior graph-memory research.",Yohei Nakajima,2026-05-21,2026,arXiv,arXiv,,"11 pages, 1 figure. Open-source Apache-2.0 implementation with reproducible quickstart demo, deterministic replay, fork-and-diff, and lineage tracing",cs.AI,arxiv-api,,,,,,,2605.21997,2026-07-17T05:43:07+00:00
+ale-0353,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Agentics: Memorizing Session Transcripts Isn't Useful,https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts,external,12gramsofcarbon.com,"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","Gives readers a concrete source in Agentics: Memorizing Session Transcripts Isn't Useful when they need to carry context, state, and receipts across runs and failures.","Contextual source from 12gramsofcarbon.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,877,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L877,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,practitioner-analysis,ok,https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts,Agentics: Memorizing Session Transcripts Isn't Useful,"Keep track of artifacts, not scratch. Alt title: Claude, please stop trying to memorize random crap",theahura,,,,12gramsofcarbon.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0354,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,Long-Running Agents,https://addyo.substack.com/p/long-running-agents,external,addyo.substack.com,"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","Gives readers a concrete source in Long-Running Agents when they need to carry context, state, and receipts across runs and failures.","Contextual source from addyo.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,878,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L878,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;state,builder,practitioner-analysis,ok,https://addyo.substack.com/p/long-running-agents,Long-running Agents - by Addy Osmani - Elevate,"A long-running AI agent can keep making progress over hours, days, or weeks.",Addy Osmani,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0355,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems,https://arxiv.org/abs/2607.05844,external,arxiv.org,"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","Gives readers a concrete source in StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.05844; inspect its method and evaluation before treating results as production evidence.,medium,README.md,879,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L879,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05844,[2607.05844] StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems,"Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join algebra; it defines an agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.",Sergey Volkov; Yang Li; Ye Luo,2026-07-07,2026,arXiv,arXiv,,Code and supplementary materials available at: https://github.com/nZiben/statefuse,cs.AI,arxiv-api,,,,,,,2607.05844,2026-07-17T05:43:07+00:00
+ale-0356,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents,https://arxiv.org/abs/2607.08716,external,arxiv.org,"Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Names the failure mode ""behavioral state decay"" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","Gives readers a concrete source in Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08716; inspect its method and evaluation before treating results as production evidence.,medium,README.md,880,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L880,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08716,[2607.08716] Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents,"In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode ""behavioral state decay"". We study memory as an active intervention mechanism rather than passive retrieval. A separate memory agent runs alongside an unmodified action agent, updating a structured memory bank from the recent trajectory and deciding whether to inject a memory-grounded reminder or remain silent. The module is plug-and-play with frontier action agents and existing agent harnesses. Across Terminal-Bench 2.0 and $\tau^2$-Bench, it improves pass@1 for both weaker and stronger action agents, with gains of +8.3 pp on Terminal-Bench and +6.8 pp on $\tau^2$-Bench. Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval. As an early step toward open-weight memory policies, we train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.",Yifan Wu; Lizhu Zhang; Yuhang Zhou; Mingyi Wang; Bo Peng; Serena Li; Xiangjun Fan; Zhuokai Zhao,2026-07-09,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.08716,2026-07-17T05:43:07+00:00
+ale-0357,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,"What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction",https://arxiv.org/abs/2607.08032,external,arxiv.org,"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","Gives readers a concrete source in What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08032; inspect its method and evaluation before treating results as production evidence.,medium,README.md,881,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L881,,Persist,persist,"Carry context, state, and receipts across runs.",objective;context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08032,"[2607.08032] What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents","Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions. Because none of this memory is free, four largely separate research communities have each learned to compact it. They evict or quantize the KV cache, prune or distill prompts, bound architectural state, and consolidate agent memory. We argue that these are instances of one problem: a rate--distortion decision about what context-derived information to retain versus discard, at what fidelity, under a resource budget, so as to preserve downstream task utility. We make this lens precise with a single compaction objective and a layer-agnostic lower bound, use it to build a seven-axis taxonomy that classifies methods from across the stack uniformly, and use it to transfer mechanisms between layers that have never been connected, from serving-stack KV management to agent long-term memory. Two patterns hold across the survey. At every layer the signal that decides what to keep is attention magnitude or recency, and it fails in the same way everywhere, by discarding, before the query is known and with no way to undo it, information the query later needs. And while compression is measured carefully on single-turn long context, the repeated compaction that agents actually perform is almost never measured, and no benchmark holds one budget axis across all the layers at once. We turn both observations into a benchmark proposal, a small reference experiment, and a set of compaction-aware design principles, and we map the open problems.",Ashwin Gerard Colaco; Nada Lahjouji,2026-07-09,2026,arXiv,arXiv,,,cs.LG,arxiv-api,,,,,,,2607.08032,2026-07-17T05:43:07+00:00
+ale-0358,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling,https://arxiv.org/abs/2607.07666,external,arxiv.org,"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","Gives readers a concrete source in A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.07666; inspect its method and evaluation before treating results as production evidence.,medium,README.md,882,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L882,,Persist,persist,"Carry context, state, and receipts across runs.",context;delegation;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07666,[2607.07666] A hierarchical memory architecture overcomes context limits in long-horizon multi-agent computational modeling,"Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session continuity and quantitative rigor. Here we present Ensemble QSP, a multi-agent framework featuring a three-layer hierarchical memory architecture that keeps injected context bounded and constant in project duration (mid-term project state: median 301 tokens, max 4,050, across 104 runs) by capping each state category and evicting completed work, enabling continuous autonomous operation without context degradation. The system orchestrates five specialist worker agents under domain-expert principal investigators, enforcing physical constraints through physics-based checklists and structured-domain knowledge. Comprehensive benchmarking demonstrates robust autonomous pharmacokinetic-pharmacodynamic model selection without human intervention, consistent result quality across both lower-cost and frontier LLMs, improved PK parameter recovery relative to single-agent baselines, and stable model selection across linguistically diverse prompts of the same task. Feature-level ablation across physiologically based pharmacokinetic (PBPK) models spanning a broad complexity range shows that PI-agent oversight improves debugging efficiency while preserving final accuracy across conditions. The architecture is structurally domain-agnostic, adding a new scientific domain requires only a new PI agent configuration.",Shivendra G. Tewari; Holly Kimko,2026-07-08,2026,arXiv,arXiv,,"19 pages, 4 figures, 2 tables. Preprint submitted for publication",q-bio.QM,arxiv-api,,,,,,,2607.07666,2026-07-17T05:43:07+00:00
+ale-0359,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents,https://arxiv.org/abs/2607.07676,external,arxiv.org,"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","Gives readers a concrete source in SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.07676; inspect its method and evaluation before treating results as production evidence.,medium,README.md,883,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L883,,Persist,persist,"Carry context, state, and receipts across runs.",state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.07676,[2607.07676] SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents,"Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable. We introduce SkillCenter, to our knowledge the largest open skill library for agents by total count: 216,938 structured skills across 24 domain bundles. A SkillGate-filtered pipeline contributes 114,565 source-grounded skills from peer-reviewed journals, ArXiv, and over 24,000 technical sources, integrated with 102,373 community skills from GitHub and the ClawHub marketplace. We present the end-to-end framework that builds the pipeline subset: multi-source acquisition, an LLM-based quality gate (SkillGate), template-driven generation, iterative source-grounding, and quality-controlled publishing. Source grounding is a traceability guarantee: each retained claim maps to an exact quotation in its source. All skills ship as offline-searchable SQLite FTS5 bundles.",Tianming Sha; Yue Zhao; Lichao Sun; Yushun Dong,2026-07-08,2026,arXiv,arXiv,,"44 pages, 5 figures. Code: https://github.com/LabRAI/SkillCenter ; Data: https://huggingface.co/datasets/Tommysha/skillcenter-bundles",cs.AI,arxiv-api,,,,,,,2607.07676,2026-07-17T05:43:07+00:00
+ale-0360,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Blog,📝,How version control will evolve for the agent boom,https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom,external,entire.io,"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","Gives readers a concrete source in How version control will evolve for the agent boom when they need to carry context, state, and receipts across runs and failures.","Contextual source from entire.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,884,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L884,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;state;exit,builder,practitioner-analysis,ok,https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom,How Version Control Will Evolve for the Agent Boom · Entire,"To meet the demand of the agent boom, Git hosting must return to its original promise: a distributed network of many hosts.",,2026-07-06,2026,,Entire,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0361,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,self-learning-skills,https://github.com/Kulaxyz/self-learning-skills,external,github.com,"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","Gives readers a concrete source in self-learning-skills when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (882 stars; 35 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,885,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L885,,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,source-implementation,ok,https://github.com/Kulaxyz/self-learning-skills,"GitHub - Kulaxyz/self-learning-skills: A self-improving skill for AI coding agents (Claude Code, Cursor, AGENTS.md): recognize a hard-won golden path in a session and harvest it into a reusable skill/rule for next time. · GitHub","A self-improving skill for AI coding agents (Claude Code, Cursor, AGENTS.md): recognize a hard-won golden path in a session and harvest it into a reusable skill/rule for next time. - Kulaxyz/self-learning-skills",,2026-06-28,2026,Kulaxyz/self-learning-skills,GitHub,,,,github-api,Kulaxyz/self-learning-skills,882,35,MIT,2026-06-28T19:30:36Z,2026-07-17T03:49:26Z,,2026-07-17T05:43:07+00:00
+ale-0362,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,GitLake: Git-for-data for the agentic lakehouse,https://arxiv.org/abs/2607.08319,external,arxiv.org,"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","Gives readers a concrete source in GitLake: Git-for-data for the agentic lakehouse when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.08319; inspect its method and evaluation before treating results as production evidence.,medium,README.md,886,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L886,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.08319,[2607.08319] GitLake: Git-for-data for the agentic lakehouse,"We present GitLake, a Git-for-data design for an agent-first lakehouse. The system lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, letting agents work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and publish through a final merge, so all outputs become visible atomically or none do. Finally, we report production lessons as well as correctness insights from a preliminary Alloy model of our core abstractions.",Weiming Sheng; Jinlang Wang; Manuel Barros; Aldrin Montana; Jacopo Tagliabue; Luca Bigon,2026-07-09,2026,arXiv,arXiv,,"Pre-print of the paper accepted at DASHSys, VLDB 2026, Boston, USA",cs.DB,arxiv-api,,,,,,,2607.08319,2026-07-17T05:43:07+00:00
+ale-0363,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Shared Selective Persistent Memory for Agentic LLM Systems,https://arxiv.org/abs/2607.09493,external,arxiv.org,"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","Gives readers a concrete source in Shared Selective Persistent Memory for Agentic LLM Systems when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.09493; inspect its method and evaluation before treating results as production evidence.,medium,README.md,887,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L887,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context;state;budget;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09493,[2607.09493] Shared Selective Persistent Memory for Agentic LLM Systems,"Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is token-inefficient and counterproductive: irrelevant context degrades generation quality. We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and output constraints) while discarding session-specific reasoning traces. Crucially, this memory is shared: workspaces encapsulating selective memory can be transferred across users with role-based access control, enabling collaborative reuse without redundant specification. We implement it in a deployed collaborative workspace platform where LLM agents produce, edit, and maintain git-versioned artifacts (dashboards, reports, and data-driven documents) from heterogeneous sources (CSV, SQL, REST APIs, and MCP servers). A complementary zero-token data refresh mechanism decouples generated programs from runtime data, enabling artifact reuse without re-invocation. Across three enterprise scenarios, shared selective persistent memory achieves 96% task completion (vs. 79% without memory and 71% with full history). Zero-token refresh eliminates LLM re-invocation for recurring updates (14x task-time reduction), while summary-driven generation cuts per-invocation token cost by 97x versus raw data injection. A replication on four public datasets confirms generalizability, with zero-token refresh succeeding in 12/12 trials. Notably, naive full-history persistence actively degrades completion by biasing the agent with stale traces, while selective memory outperforms both extremes.",Sanjana Pedada; Aditya Dhavala; Neelraj Patil,2026-07-10,2026,arXiv,arXiv,,"11 pages, 2 figures, 4 tables",cs.AI,arxiv-api,,,,,,,2607.09493,2026-07-17T05:43:07+00:00
+ale-0364,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Scoped Verification for Reliable Long-Horizon Agentic Context Evolution,https://arxiv.org/abs/2607.09175,external,arxiv.org,"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","Gives readers a concrete source in Scoped Verification for Reliable Long-Horizon Agentic Context Evolution when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.09175; inspect its method and evaluation before treating results as production evidence.,medium,README.md,888,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L888,,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.09175,[2607.09175] Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift,"Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is updated from operational experience while the model, tools, and harness remain fixed. Over long evolution horizons, flat-text maintenance makes verification increasingly difficult as accumulated instructions grow and interact. We propose Graph-Regularized Agentic Context Evolution (GRACE), which maintains the persistent instruction component as a typed semantic graph and validates proposed updates within the local typed neighborhoods of modified nodes. Accepted graph updates are reconstructed as incremental edits to the textual instruction checkpoint used at deployment. We evaluate GRACE within a fixed telecom agent harness derived from $\tau^2$-bench under a controlled distribution-shift protocol. Across five independent replications, GRACE improves strict reliability, measured by pass^3, from the Gemini 2.5 Flash zero-shot value of 0.091 to 0.673$\pm$0.136 at the final checkpoint. This exceeds a Gemini 3.1 Pro zero-shot reference of 0.242 on the same held-out set, while the flat-text HCE baseline finishes at 0.191$\pm$0.051. These results identify two requirements for reliable long-horizon context evolution, a structural substrate that makes verification local and a consolidation mechanism that keeps accumulated instruction content usable.",Dan C. Hsu; Luke Lu,2026-07-10,2026,arXiv,arXiv,,"18 pages, 3 figs",cs.AI,arxiv-api,,,,,,,2607.09175,2026-07-17T05:43:07+00:00
+ale-0365,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,AgentMemory,https://github.com/rohitg00/agentmemory,external,github.com,"Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Persistent cross-session memory for coding agents: auto-capture lifecycle hooks, an MCP server, and a REST API so progress, decisions, and context survive across runs and tools.","Gives readers a concrete source in AgentMemory when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (25,247 stars; 2,088 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,889,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L889,,Persist,persist,"Carry context, state, and receipts across runs.",workspace;context;state,builder,source-implementation,ok,https://github.com/rohitg00/agentmemory,GitHub - rohitg00/agentmemory: #1 Persistent memory for AI coding agents based on real-world benchmarks · GitHub,#1 Persistent memory for AI coding agents based on real-world benchmarks - rohitg00/agentmemory,,2026-02-25,2026,rohitg00/agentmemory,GitHub,,,,github-api,rohitg00/agentmemory,25247,2088,Apache-2.0,2026-02-25T07:32:52Z,2026-07-17T05:45:20Z,,2026-07-17T05:43:07+00:00
+ale-0366,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,TencentDB-Agent-Memory,https://github.com/TencentCloud/TencentDB-Agent-Memory,external,github.com,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,Persistent memory is treated as an external runtime artifact. Resource-specific angle: Tencent Cloud's open-source local-first long-term memory for AI agents: a four-tier progressive pipeline from capture through consolidation with zero external API dependencies.,"Gives readers a concrete source in TencentDB-Agent-Memory when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (9,020 stars; 830 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,890,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L890,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/TencentCloud/TencentDB-Agent-Memory,"GitHub - TencentCloud/TencentDB-Agent-Memory: TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies. · GitHub","TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies. - TencentCloud/TencentDB-Agent-Memory",,2026-04-07,2026,TencentCloud/TencentDB-Agent-Memory,GitHub,,,,github-api,TencentCloud/TencentDB-Agent-Memory,9020,830,NOASSERTION,2026-04-07T06:34:26Z,2026-07-17T05:32:42Z,,2026-07-17T05:43:07+00:00
+ale-0367,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,agent-memory (Neo4j Labs),https://github.com/neo4j-labs/agent-memory,external,github.com,"Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official Neo4j Labs graph-native memory system that stores conversations, builds knowledge graphs from agent interactions, and lets agents learn from their own reasoning traces.","Gives readers a concrete source in agent-memory (Neo4j Labs) when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (373 stars; 85 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,891,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L891,,Persist,persist,"Carry context, state, and receipts across runs.",context,builder,source-implementation,ok,https://github.com/neo4j-labs/agent-memory,"GitHub - neo4j-labs/agent-memory: A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j. · GitHub","A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j. - neo4j-labs/agent-memory",,2026-01-06,2026,neo4j-labs/agent-memory,GitHub,,,,github-api,neo4j-labs/agent-memory,373,85,Apache-2.0,2026-01-06T23:07:16Z,2026-07-16T18:06:32Z,,2026-07-17T05:43:07+00:00
+ale-0368,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Tool,🧰,re_gent,https://github.com/regent-vcs/re_gent,external,github.com,"Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Makes persistence and context management visible as runtime design choices. Resource-specific angle: Agent-native version control layered on top of Git that records agent activity at the prompt level, so you can answer why the agent did something and undo agent work without losing your own.","Gives readers a concrete source in re_gent when they need to carry context, state, and receipts across runs and failures.","Inspectable GitHub source (780 stars; 57 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,892,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L892,,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,source-implementation,ok,https://github.com/regent-vcs/re_gent,GitHub - regent-vcs/re_gent: Version-Control for AI coding agents. · GitHub,Version-Control for AI coding agents. Contribute to regent-vcs/re_gent development by creating an account on GitHub.,,2026-04-30,2026,regent-vcs/re_gent,GitHub,,,,github-api,regent-vcs/re_gent,780,57,Apache-2.0,2026-04-30T09:09:41Z,2026-07-15T15:28:45Z,,2026-07-17T05:43:07+00:00
+ale-0369,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure,https://arxiv.org/abs/2607.11388,external,arxiv.org,"State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: State-centered framework that structures a long-horizon computer-use agent's state around a unified causal representation of task progress, regulating every update through verifier-backed state transitions with checkpointing and targeted failure recovery, lifting Qwen3.5-27B from 31.6% to 62.2% on OSWorld-Verified.","Gives readers a concrete source in StructAgent: Harness Long-Horizon Digital Agents with Unified Causal Structure when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.11388; inspect its method and evaluation before treating results as production evidence.,medium,README.md,893,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L893,2026-07-15,Persist,persist,"Carry context, state, and receipts across runs.",verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11388,[2607.11388] StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure,"Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts containing accumulated observations, intermediate edits, failed attempts, and partially completed executions. Existing agents typically operate over raw interaction history, making task progress difficult to interpret, verify, and recover, which ultimately limits reliable long-horizon execution. In this paper, we argue that addressing this challenge requires explicitly structuring both the agent's state and workflow around a unified causal representation of task progress. We present \textbf{StructAgent}, a state-centered framework that introduces a unified state for maintaining compact, verifiable task progress and a structured workflow that regulates progress through verifier-backed state transitions. Building on this design, StructAgent further enables explicit progress checkpointing, evidence-driven task completion, targeted failure recovery, and tool-supported execution, while ensuring that all progress updates remain grounded in verification. Extensive experiments demonstrate that StructAgent consistently improves a wide range of LLM and VLM backbones on long-horizon computer-use tasks. On OSWorld-Verified, it improves Qwen3.5-9B from 27.0\% to 46.9\% success rate and Qwen3.5-27B from 31.6\% to 62.2\%, while achieving a new open-source state of the art of 78.9\% with MiniMax-M3. Moreover, the same framework generalizes beyond desktop environments to Minecraft, demonstrating the generality of our design.",Wenyi Wu; Sibo Zhu; Kun Zhou; Aayush Salvi; Zixuan Song; Biwei Huang,2026-07-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.11388,2026-07-17T05:43:07+00:00
+ale-0370,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,https://arxiv.org/abs/2607.10608,external,arxiv.org,"Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Diagnoses how agents resolve contradictions in their own persistent memory, finding they tend to comply with the most recent or most assertive entry rather than the correct one, a failure mode for any loop that accumulates state across runs.","Gives readers a concrete source in The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.10608; inspect its method and evaluation before treating results as production evidence.,medium,README.md,894,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L894,2026-07-15,Persist,persist,"Carry context, state, and receipts across runs.",context;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.10608,[2607.10608] The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,"Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments. Existing work mostly treats memory as a supply problem, asking what experience to write, how to store it, and which entry to retrieve for the next task. Yet we still lack a clear account of how models consume retrieved memory across a multi-step action trajectory. This consumption process matters because it determines not only what memories should be retrieved, but also what models and control policies are needed to use them safely. To diagnose this process, we propose Entry--Propagation--Recovery (E-P-R), a trajectory-level framework that asks where memory first changes an action, whether that change carries forward, and whether the agent can recover after leaving a correct path. We instantiate E-P-R on WebArena and on MemTrapBench, a controlled benchmark we build to isolate these phases. We find that the main failure often begins at entry: agents adopt conflicting memory at the first exposed decision point even when it is task-wrong. Repeated exposure then amplifies this early error, while recovery after divergence is weak. Together, these effects create a compliance trap: across models, conflicting memory induces similar compliance rates, but once agents comply, their success rates collapse to a low floor. Stronger agents therefore suffer larger absolute damage because each compliance event erases more baseline capability. These results suggest that memory-augmented agents should be evaluated not only by retrieval quality or final success rate, but by how they consume memory throughout the trajectory.",Yixiong Chen; Xinyi Bai; Alan Yuille,2026-07-12,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.10608,2026-07-17T05:43:07+00:00
+ale-0371,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,"Conversational Context: Session, State, and Memory",https://adk.dev/sessions/,external,adk.dev,"Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK model separating a conversation thread, its mutable state, and searchable cross-session memory, with service backends for durable persistence.","Gives readers a concrete source in Conversational Context: Session, State, and Memory when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,895,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L895,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,official-documentation,ok,https://adk.dev/sessions/,"Conversational Context: Session, State, and Memory - Agent Development Kit (ADK) Agent Development Kit (ADK)",Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0372,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Persistence,https://docs.langchain.com/oss/python/langgraph/persistence,external,docs.langchain.com,"Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official LangGraph checkpoint model: save state at every super-step, retain pending writes, recover interrupted execution, support human review, and enable memory and time travel.","Gives readers a concrete source in Persistence when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from docs.langchain.com; use it for current product or standard behavior.,high,README.md,896,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L896,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state;escalation,builder,official-documentation,ok,https://docs.langchain.com/oss/python/langgraph/persistence,Persistence - Docs by LangChain,LangGraph's persistence layer gives agents short-term memory through checkpointers and long-term memory through stores.,LangChain,,,LangGraph,LangChain,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0373,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Workflow checkpoints,https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints,external,learn.microsoft.com,"Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official checkpointing guide covering super-step state, pending messages and requests, shared state, in-memory and durable storage providers, and resuming long-running workflows.","Gives readers a concrete source in Workflow checkpoints when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from learn.microsoft.com; use it for current product or standard behavior.,high,README.md,897,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L897,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;state,builder,official-documentation,ok,https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints,Microsoft Agent Framework Workflows - Checkpoints | Microsoft Learn,In-depth look at Checkpoints in Microsoft Agent Framework Workflows.,Microsoft,,,Microsoft Agent Framework,Microsoft,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0374,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Docs,📚,Agent state,https://strandsagents.com/docs/user-guide/concepts/agents/state/,external,strandsagents.com,"Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official state guide separating conversation, agent, and invocation lifetimes, with validation hooks and cross-session persistence for durable agent behavior.","Gives readers a concrete source in Agent state when they need to carry context, state, and receipts across runs and failures.",Primary official documentation from strandsagents.com; use it for current product or standard behavior.,high,README.md,898,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L898,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",state,builder,official-documentation,ok,https://strandsagents.com/docs/user-guide/concepts/agents/state/,State Management | Strands Agents,The open source toolkit for building production agents.,Strands Agents,,,Strands Agents,Strands Agents,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0375,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,https://arxiv.org/abs/2607.13591,external,arxiv.org,"Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","The work turns loop quality into a measurable task or score. Resource-specific angle: Learns when to retrieve, consolidate, and forget rather than treating memory as passive storage; across six benchmarks, three frameworks, and three LLMs, it reports up to 15.2 points higher task success with 5-20% fewer tokens.","Gives readers a concrete source in Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.13591; inspect its method and evaluation before treating results as production evidence.,medium,README.md,899,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L899,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.13591,[2607.13591] Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,"Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics. We argue that this static view of memory is a core bottleneck for agentic learning because optimal memory behavior is fundamentally context-dependent. The early stages of the tasks, benefit from minimal retrieval because memory is sparse; recurring goal types benefit from plan reuse rather than generic nearest-neighbor lookup; stuck agents benefit from re-retrieval with alternative queries; and across long task streams, the memory store itself must be consolidated and pruned to remain useful. We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process and learns an online policy that adaptively decides when, what, and how much to retrieve, when to inject a distilled plan, and when to consolidate or forget. MemCon is backend-agnostic: it wraps any existing memory implementation, learns from task-by-task binary feedback with no pretraining and no additional LLM calls, and uses a lightweight tabular contextual bandit with UCB exploration that converges within tens of tasks. Across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones, MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5--20%.",Eric Hanchen Jiang; Zhi Zhang; Yuchen Wu; Levina Li; Dong Liu; Xiao Liang; Rui Sun; Yubei Li; Edward Sun; Haozheng Luo; Zhaolu Kang; Aylin Caliskan; Kai-Wei Chang; Ying Nian Wu,2026-07-15,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2607.13591,2026-07-17T05:43:07+00:00
+ale-0376,"State, Memory, And Context Persistence",state-memory-and-context-persistence,Paper,📄,Why Git Is the Memory Solution for the Agentic Development Lifecycle,https://arxiv.org/abs/2607.14390,external,arxiv.org,"Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Binds agent memory to versioned Git artifacts and evaluates retrieval across eight corpora; reported best retrieval reaches about 0.31 MRR and decision synthesis 0.83 sufficiency at 382-980 tokens per query, with capture quality still the main constraint.","Gives readers a concrete source in Why Git Is the Memory Solution for the Agentic Development Lifecycle when they need to carry context, state, and receipts across runs and failures.",Research source arXiv:2607.14390; inspect its method and evaluation before treating results as production evidence.,medium,README.md,900,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L900,2026-07-17,Persist,persist,"Carry context, state, and receipts across runs.",context;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.14390,[2607.14390] Why Git Is the Memory Solution for the Agentic Development Lifecycle,"Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session. Memory for this setting, the agentic development lifecycle (ADLC), is usually posed as one retrieval problem and built as machinery: tiered stores, memory graphs, compiled wikis, model-judged admission. We argue memory should instead be git-bound -- built into the repository's version control, inheriting the guarantees the machinery struggles to construct: ground truth from commits, freshness from rebuild, verification from the merge, containment from review. On this ledger we solve two problems separately, then combine them. Seed supply is closed as an eight-corpus retrieval study under a pre-registered ship discipline: five imported ranking mechanisms rejected, two kept, and a best configuration of ~0.31 pooled MRR -- ~60x the raw-transcript grep floor, ~15x an honest parsed-turn floor. Answer assembly is where ranking stops helping: single-shot retrieval scores only 0.07-0.20 answer-sufficiency on real developer questions, and ungated episode injection measurably degrades good answers. A router dispatches breadth to a git-anchored structural map, pointed lookups to confidence-gated episodes, and rationale to decision synthesis, which reconstructs why-arcs no single session contains (0.83 sufficiency on a young ~50k-LOC production system). Routed, the system answers at 382-980 tokens per question -- three orders of magnitude below the recorded history. Because ground truth is mined from commit-session links rather than annotated, every result is replicable on any user's own history at zero labeling cost. The remaining constraint is capture. Code, benchmark, and paper source: github.com/rekal-dev/rekal-cli.",Frank Guo,2026-07-15,2026,arXiv,arXiv,,8 pages,cs.SE,arxiv-api,,,,,,,2607.14390,2026-07-17T05:43:07+00:00
+ale-0377,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AutoGen,https://github.com/microsoft/autogen,external,github.com,"Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Multi-agent programming framework for conversations, tool use, and orchestration; active development has moved to the Microsoft Agent Framework.",Gives readers a concrete source in AutoGen when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (59,783 stars; 8,998 forks; CC-BY-4.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,908,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L908,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,source-implementation,ok,https://github.com/microsoft/autogen,GitHub - microsoft/autogen: A programming framework for agentic AI · GitHub,A programming framework for agentic AI. Contribute to microsoft/autogen development by creating an account on GitHub.,,2023-08-18,2023,microsoft/autogen,GitHub,,,,github-api,microsoft/autogen,59783,8998,CC-BY-4.0,2023-08-18T11:43:45Z,2026-07-17T04:18:46Z,,2026-07-17T05:43:07+00:00
+ale-0378,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Microsoft Agent Framework,https://github.com/microsoft/agent-framework,external,github.com,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.,"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Microsoft's successor to AutoGen and Semantic Kernel for building and orchestrating multi-agent workflows in Python and .NET.",Gives readers a concrete source in Microsoft Agent Framework when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (12,171 stars; 2,045 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,909,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L909,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/microsoft/agent-framework,"GitHub - microsoft/agent-framework: A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. · GitHub","A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. - microsoft/agent-framework",,2025-04-28,2025,microsoft/agent-framework,GitHub,,,,github-api,microsoft/agent-framework,12171,2045,MIT,2025-04-28T19:40:42Z,2026-07-17T03:58:35Z,,2026-07-17T05:43:07+00:00
+ale-0379,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,LangGraph,https://github.com/langchain-ai/langgraph,external,github.com,"Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Graph-based framework for controllable agent workflows, persistence, and human-in-the-loop steps.",Gives readers a concrete source in LangGraph when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (37,472 stars; 6,279 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,910,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L910,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;escalation,builder,source-implementation,ok,https://github.com/langchain-ai/langgraph,GitHub - langchain-ai/langgraph: Build resilient agents. · GitHub,Build resilient agents. Contribute to langchain-ai/langgraph development by creating an account on GitHub.,,2023-08-09,2023,langchain-ai/langgraph,GitHub,,,,github-api,langchain-ai/langgraph,37472,6279,MIT,2023-08-09T18:33:12Z,2026-07-17T05:21:44Z,,2026-07-17T05:43:07+00:00
+ale-0380,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,CrewAI,https://github.com/crewAIInc/crewAI,external,github.com,"Framework for multi-agent workflows organized around roles, tasks, and crews.","Framework for multi-agent workflows organized around roles, tasks, and crews.","Framework for multi-agent workflows organized around roles, tasks, and crews.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Framework for multi-agent workflows organized around roles, tasks, and crews.",Gives readers a concrete source in CrewAI when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (55,668 stars; 7,854 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,911,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L911,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,source-implementation,ok,https://github.com/crewAIInc/crewAI,"GitHub - crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. · GitHub","Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. - crewAIInc/crewAI",,2023-10-27,2023,crewAIInc/crewAI,GitHub,,,,github-api,crewAIInc/crewAI,55668,7854,MIT,2023-10-27T03:26:59Z,2026-07-17T05:04:58Z,,2026-07-17T05:43:07+00:00
+ale-0381,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,LlamaIndex Workflows,https://developers.llamaindex.ai/python/llamaagents/workflows/,external,developers.llamaindex.ai,Event-driven workflow abstraction for agentic applications.,Event-driven workflow abstraction for agentic applications.,Event-driven workflow abstraction for agentic applications.,"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Event-driven workflow abstraction for agentic applications.",Gives readers a concrete source in LlamaIndex Workflows when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,912,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L912,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger,builder,technical-documentation,ok,https://developers.llamaindex.ai/python/llamaagents/workflows/,Introduction | Developer Documentation,,,,,,Developer Documentation,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0382,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,OpenAI Agents SDK handoffs,https://openai.github.io/openai-agents-python/handoffs/,external,openai.github.io,First-class delegation between specialized agents.,First-class delegation between specialized agents.,First-class delegation between specialized agents.,"Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: First-class delegation between specialized agents.",Gives readers a concrete source in OpenAI Agents SDK handoffs when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from openai.github.io; use it for current product or standard behavior.,high,README.md,913,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L913,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,official-documentation,ok,https://openai.github.io/openai-agents-python/handoffs/,Handoffs - OpenAI Agents SDK,,,,,,openai.github.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0383,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Agent Protocol,https://agentprotocol.ai/,external,agentprotocol.ai,"API protocol for agent interaction, useful for separating loop managers from agent runtimes.","API protocol for agent interaction, useful for separating loop managers from agent runtimes.","API protocol for agent interaction, useful for separating loop managers from agent runtimes.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: API protocol for agent interaction, useful for separating loop managers from agent runtimes.",Gives readers a concrete source in Agent Protocol when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,914,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L914,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,technical-documentation,ok,https://agentprotocol.ai/,AgentProtocol.ai — A practical guide to AI agent communication standards.,"AgentProtocol.ai is an independent, vendor-neutral guide to AI agent communication standards — MCP, A2A, Agent Protocol, AI agent APIs and agent interoperability.",AgentProtocol.ai,,,,AgentProtocol.ai,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0384,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AgentKit,https://github.com/inngest/agent-kit,external,github.com,"TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","TypeScript toolkit for durable, event-driven agents on workflow infrastructure.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: TypeScript toolkit for durable, event-driven agents on workflow infrastructure.",Gives readers a concrete source in AgentKit when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (913 stars; 136 forks; Apache-2.0 license; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,915,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L915,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger,builder,source-implementation,ok,https://github.com/inngest/agent-kit,GitHub - inngest/agent-kit: AgentKit: Build multi-agent networks in TypeScript with deterministic routing and rich tooling via MCP. · GitHub,AgentKit: Build multi-agent networks in TypeScript with deterministic routing and rich tooling via MCP. - inngest/agent-kit,,2024-11-18,2024,inngest/agent-kit,GitHub,,,,github-api,inngest/agent-kit,913,136,Apache-2.0,2024-11-18T05:28:42Z,2026-07-13T12:05:29Z,,2026-07-17T05:43:07+00:00
+ale-0385,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,deepagents,https://github.com/langchain-ai/deepagents,external,github.com,"LangChain project for deeper, longer-running agents with middleware and harness patterns.","LangChain project for deeper, longer-running agents with middleware and harness patterns.","LangChain project for deeper, longer-running agents with middleware and harness patterns.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: LangChain project for deeper, longer-running agents with middleware and harness patterns.",Gives readers a concrete source in deepagents when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (26,333 stars; 3,694 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,916,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L916,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,source-implementation,ok,https://github.com/langchain-ai/deepagents,GitHub - langchain-ai/deepagents: The batteries-included agent harness. · GitHub,The batteries-included agent harness. Contribute to langchain-ai/deepagents development by creating an account on GitHub.,,2025-07-27,2025,langchain-ai/deepagents,GitHub,,,,github-api,langchain-ai/deepagents,26333,3694,MIT,2025-07-27T23:07:53Z,2026-07-17T05:38:17Z,,2026-07-17T05:43:07+00:00
+ale-0386,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Temporal for AI,https://temporal.io/solutions/ai,external,temporal.io,"Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable execution for long-running agent workflows: crash-proof state, automatic retries, and human-in-the-loop signals.",Gives readers a concrete source in Temporal for AI when they need to choose an implementation surface for repeatable agent work.,"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.",high,README.md,917,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L917,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;budget;escalation,builder,technical-documentation,ok,https://temporal.io/solutions/ai,Temporal for AI | Temporal,"Temporal is a durable workflow platform that ensures AI applications run reliably, every time. Build faster, prevent failures, and stand out from the crowd.",,,,,temporal.io,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0387,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Restate,https://restate.dev/,external,restate.dev,"Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Durable execution runtime for building resilient, stateful agents and workflows that survive failures mid-loop.",Gives readers a concrete source in Restate when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,918,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L918,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state,builder,implementation,ok,https://www.restate.dev/,Restate - Build innately resilient distributed apps,Restate is a lightweight runtime that lets developers build innately resilient distributed apps without the complexity tax.,,,,,Restate,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0388,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,DBOS,https://www.dbos.dev/,external,www.dbos.dev,"Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Lightweight PostgreSQL-backed durable execution library for crash-proof agent workflows, queues, and scheduled triggers.",Gives readers a concrete source in DBOS when they need to choose an implementation surface for repeatable agent work.,"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.",high,README.md,919,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L919,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;intake,builder,implementation,ok,https://www.dbos.dev/,DBOS | Durable Workflow Orchestration,"DBOS is an open source durable execution and workflow orchestration system that radically simplifies the development and operation of reliable, observable workflows.",,,,,dbos.dev,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0389,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Composio Agent Orchestrator,https://github.com/ComposioHQ/agent-orchestrator,external,github.com,"Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Orchestrates parallel coding agents in isolated worktrees that plan tasks, fix CI failures, respond to reviews, and manage their own PR lifecycle.",Gives readers a concrete source in Composio Agent Orchestrator when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (8,314 stars; 1,195 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,920,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L920,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,source-implementation,ok,https://github.com/AgentWrapper/agent-orchestrator,"GitHub - AgentWrapper/agent-orchestrator: AO is an agent IDE, that helps developers manage fleets of coding agents to do your day to day tasks for parallel coding agents. It comes with an agentic orchestrator that plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews. · GitHub","AO is an agent IDE, that helps developers manage fleets of coding agents to do your day to day tasks for parallel coding agents. It comes with an agentic orchestrator that plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews. - AgentWrapper/agent-orchestrator",,2026-02-13,2026,ComposioHQ/agent-orchestrator,GitHub,,,,github-api,ComposioHQ/agent-orchestrator,8314,1195,Apache-2.0,2026-02-13T09:52:36Z,2026-07-17T04:40:02Z,,2026-07-17T05:43:07+00:00
+ale-0390,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Omnigent,https://github.com/omnigent-ai/omnigent,external,github.com,"Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.",Gives readers a concrete source in Omnigent when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (7,397 stars; 1,043 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,921,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L921,,Build,build,"Choose runtimes, tools, and delegation surfaces.",budget;escalation,builder,source-implementation,ok,https://github.com/omnigent-ai/omnigent,"GitHub - omnigent-ai/omnigent: Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device. · GitHub","Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device. - omnigent-ai/omnigent",,2026-06-11,2026,omnigent-ai/omnigent,GitHub,,,,github-api,omnigent-ai/omnigent,7397,1043,Apache-2.0,2026-06-11T12:18:13Z,2026-07-17T05:38:06Z,,2026-07-17T05:43:07+00:00
+ale-0391,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution,https://arxiv.org/abs/2604.11378,external,arxiv.org,"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.",Gives readers a concrete source in From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2604.11378; inspect its method and evaluation before treating results as production evidence.,medium,README.md,922,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L922,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;escalation;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.11378,[2604.11378] From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution,"The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window. This paradigm has three structural weaknesses: implicit dependencies between steps, unbounded recovery loops, and mutable execution history that complicates debugging. We characterize the Agent Loop as a single ready unit scheduler: at any moment, at most one executable unit is active, and the choice of which unit to activate comes from opaque LLM inference rather than an inspectable policy. This perspective places Agent Loops and graph based execution engines on a single semantic continuum. We propose SGH, Structured Graph Harness, which lifts control flow from implicit context into an explicit static DAG. SGH makes three commitments: execution plans are immutable within a plan version, planning execution and recovery are separated into three layers, and recovery follows a strict escalation protocol. These choices trade some expressiveness for controllability, verifiability, and implementability. Our contributions are fourfold: a scheduler unified framework that applies classical scheduling theory to LLM agent execution and identifies challenges introduced by non deterministic LLM nodes; a trade off analysis of controllability, expressiveness, and implementability across 70 surveyed systems; a formal specification including a node state machine with termination and soundness guarantees; and an attributable experimental framework with a seven group design for future validation. This is a position paper and design proposal. We provide a theoretical framework, design analysis, and experimental protocol, not a production implementation or empirical results.",Hu Wei,2026-04-13,2026,arXiv,arXiv,,"51 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2604.11378,2026-07-17T05:43:07+00:00
+ale-0392,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Eve,https://github.com/vercel/eve,external,github.com,"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.",Gives readers a concrete source in Eve when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (3,770 stars; 343 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,923,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L923,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;state,builder,source-implementation,ok,https://github.com/vercel/eve,GitHub - vercel/eve: The Framework for Building Agents · GitHub,The Framework for Building Agents. Contribute to vercel/eve development by creating an account on GitHub.,,2026-06-16,2026,vercel/eve,GitHub,,,,github-api,vercel/eve,3770,343,Apache-2.0,2026-06-16T10:51:20Z,2026-07-17T05:32:55Z,,2026-07-17T05:43:07+00:00
+ale-0393,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework,https://arxiv.org/abs/2603.11445,external,arxiv.org,"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.",Gives readers a concrete source in Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.11445; inspect its method and evaluation before treating results as production evidence.,medium,README.md,924,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L924,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;exit,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.11445,[2603.11445] Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework for Complex Query Resolution,"We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.",Xing Zhang; Yanwei Cui; Guanghui Wang; Wei Qiu; Ziyuan Li; Fangwei Han; Yajing Huang; Hengzhi Qiu; Bing Zhu; Peiyang He,2026-03-12,2026,arXiv,arXiv,,ICLR 2026 Workshop on MALGAI,cs.AI,arxiv-api,,,,,,,2603.11445,2026-07-17T05:43:07+00:00
+ale-0394,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents,https://arxiv.org/abs/2603.22386,external,arxiv.org,"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.",Gives readers a concrete source in From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2603.22386; inspect its method and evaluation before treating results as production evidence.,medium,README.md,925,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L925,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.22386,[2603.22386] From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents,"Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows, which we treat as agentic computation graphs (ACGs). We organize the literature based on when workflow structure is determined, where structure refers to which components or agents are present, how they depend on each other, and how information flows between them. This lens distinguishes static methods, which fix a reusable workflow scaffold before deployment, from dynamic methods, which select, generate, or revise the workflow for a particular run before or during execution. We further organize prior work along three dimensions: when structure is determined, what part of the workflow is optimized, and which evaluation signals guide optimization (e.g., task metrics, verifier signals, preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating reusable design choices from the structures actually deployed in a given run and from realized runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. Our goal is to provide a clear vocabulary, a unified framework for positioning new methods, a more comparable view of existing body of literature, and a more reproducible evaluation standard for future work in workflow optimizations for LLM agents.",Ling Yue; Kushal Raj Bhandari; Ching-Yun Ko; Dhaval Patel; Shuxin Lin; Nianjun Zhou; Jianxi Gao; Pin-Yu Chen; Shaowu Pan,2026-03-23,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.22386,2026-07-17T05:43:07+00:00
+ale-0395,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Agent-as-a-Router,https://github.com/LanceZPF/agent-as-a-router,external,github.com,"Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.",Gives readers a concrete source in Agent-as-a-Router when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (647 stars; 14 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,926,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L926,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation;verification,builder,source-implementation,ok,https://github.com/LanceZPF/agent-as-a-router,GitHub - LanceZPF/agent-as-a-router: The official implementations of Agent-as-a-Router: Agentic Model Routing for Coding Tasks. · GitHub,The official implementations of Agent-as-a-Router: Agentic Model Routing for Coding Tasks. - LanceZPF/agent-as-a-router,,2026-06-20,2026,LanceZPF/agent-as-a-router,GitHub,,,,github-api,LanceZPF/agent-as-a-router,647,14,MIT,2026-06-20T16:00:51Z,2026-07-17T05:13:11Z,,2026-07-17T05:43:07+00:00
+ale-0396,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Amp: Custom Agents,https://ampcode.com/news/custom-agents,external,ampcode.com,"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.",Gives readers a concrete source in Amp: Custom Agents when they need to choose an implementation surface for repeatable agent work.,"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,927,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L927,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;delegation,builder,practitioner-analysis,ok,https://ampcode.com/news/custom-agents,Amp,"Plugins can now create agents, run them once, and keep talking to their threads.",,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0397,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,AgentsMesh,https://github.com/AgentsMesh/AgentsMesh,external,github.com,"Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.",Gives readers a concrete source in AgentsMesh when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (2,282 stars; 228 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,928,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L928,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace,builder,source-implementation,ok,https://github.com/AgentsMesh/AgentsMesh,"GitHub - AgentsMesh/AgentsMesh: The AI Agent Workforce Platform. Run a hundred AI coding agents across your own machines — schedule, isolate, and steer them all from one console. · GitHub","The AI Agent Workforce Platform. Run a hundred AI coding agents across your own machines — schedule, isolate, and steer them all from one console. - AgentsMesh/AgentsMesh",,2026-02-28,2026,AgentsMesh/AgentsMesh,GitHub,,,,github-api,AgentsMesh/AgentsMesh,2282,228,NOASSERTION,2026-02-28T07:10:42Z,2026-07-17T01:12:07Z,,2026-07-17T05:43:07+00:00
+ale-0398,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Bernstein,https://github.com/sipyourdrink-ltd/bernstein,external,github.com,"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.",Gives readers a concrete source in Bernstein when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (682 stars; 62 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,929,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L929,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;workspace;delegation;verification,builder,source-implementation,ok,https://github.com/sipyourdrink-ltd/bernstein,"GitHub - sipyourdrink-ltd/bernstein: Deterministic, audit-grade multi-agent orchestration for CLI coding agents (Claude Code, Codex, Gemini CLI, +40 more). HMAC-chained audit log, signed agent cards, per-artefact lineage, byte-identical replay with divergence detection, air-gap deploy. https://bernstein.run · GitHub","Deterministic, audit-grade multi-agent orchestration for CLI coding agents (Claude Code, Codex, Gemini CLI, +40 more). HMAC-chained audit log, signed agent cards, per-artefact lineage, byte-identical replay with divergence detection, air-gap deploy. https://bernstein.run - sipyourdrink-ltd/bernstein",,2026-03-22,2026,sipyourdrink-ltd/bernstein,GitHub,,,,github-api,sipyourdrink-ltd/bernstein,682,62,Apache-2.0,2026-03-22T14:52:26Z,2026-07-17T05:36:58Z,,2026-07-17T05:43:07+00:00
+ale-0399,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Aeon,https://github.com/aaronjmars/aeon,external,github.com,"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.",Gives readers a concrete source in Aeon when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (577 stars; 208 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,930,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L930,,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;context;state,builder,source-implementation,ok,https://github.com/aeonfun/aeon,"GitHub - aeonfun/aeon: The most autonomous agent framework. No approval loops. No babysitting. Configure once, forget forever. · GitHub","The most autonomous agent framework. No approval loops. No babysitting. Configure once, forget forever. - aeonfun/aeon",,2026-03-04,2026,aaronjmars/aeon,GitHub,,,,github-api,aaronjmars/aeon,577,208,MIT,2026-03-04T19:44:49Z,2026-07-16T21:58:55Z,,2026-07-17T05:43:07+00:00
+ale-0400,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,h5i,https://github.com/h5i-dev/h5i,external,github.com,"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.",Gives readers a concrete source in h5i when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (470 stars; 39 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,931,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L931,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;verification,builder,source-implementation,ok,https://github.com/h5i-dev/h5i,"GitHub - h5i-dev/h5i: Auditable workspaces for AI coding agents: sandboxed worktrees, programmable multi-agent orchestration, automated security checks, up to 95% less token waste, and persistent memory. · GitHub","Auditable workspaces for AI coding agents: sandboxed worktrees, programmable multi-agent orchestration, automated security checks, up to 95% less token waste, and persistent memory. - h5i-dev/h5i",,2026-03-11,2026,h5i-dev/h5i,GitHub,,,,github-api,h5i-dev/h5i,470,39,Apache-2.0,2026-03-11T04:30:52Z,2026-07-16T21:21:27Z,,2026-07-17T05:43:07+00:00
+ale-0401,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,https://arxiv.org/abs/2607.02807,external,arxiv.org,"A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: A shepherd agent with global context steers a population of search agents that each work in their own Git branch with local context, countering the context accumulation of solo long-running agents and matching or beating baselines on 13 of 15 open-ended discovery tasks.",Gives readers a concrete source in SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.02807; inspect its method and evaluation before treating results as production evidence.,medium,README.md,932,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L932,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;context,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.02807,[2607.02807] SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,"Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, then proceed with low-level edits while missing other superior approaches to the problem. We hypothesize two harness-level design choices contribute to this behavior: accumulating context in a single long-running agent and only exposing a single program state to edit. We introduce SwarmResearch, an orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents, each operating with local context in their respective git branch. On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques on 13/15 tasks, driven by higher-level exploration. Compared with fixed scaling of serial and parallel agents, SwarmResearch's orchestrator-guided scaling discovers better-performing solutions by adapting parallelism at different search depths.",Yuvraj Virk; Zack Edds; Chunqiu Steven Xia; Lingming Zhang,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02807,2026-07-17T05:43:07+00:00
+ale-0402,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Scaling Long-Running Autonomous Coding,https://cursor.com/blog/scaling-agents,external,cursor.com,"Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Cursor traces the coordination designs behind week-long autonomous coding runs, from flat agents with locking to optimistic concurrency to a planner/worker/judge hierarchy, letting hundreds of concurrent workers push to one branch on projects exceeding a million lines.",Gives readers a concrete source in Scaling Long-Running Autonomous Coding when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,933,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L933,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,practitioner-analysis,ok,https://cursor.com/blog/scaling-agents,Scaling long-running autonomous coding · Cursor,We've been experimenting with running coding agents autonomously for weeks at a time.,Wilson Lin,,,,Cursor,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0403,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,babysitter,https://github.com/a5c-ai/babysitter,external,github.com,"Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Harness-agnostic orchestration framework that runs agent workflows as process-as-code with mandatory enforcement stops after every step, quality-convergence loops that re-run verify-and-refine until thresholds pass, human-approval breakpoints, and an immutable event-sourced journal for deterministic replay across 12 coding harnesses.",Gives readers a concrete source in babysitter when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,554 stars; 90 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,934,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L934,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;verification;state;escalation;exit,builder,source-implementation,ok,https://github.com/a5c-ai/babysitter,"GitHub - a5c-ai/babysitter: Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration · GitHub","Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration - a5c-ai/babysitter",,2026-01-05,2026,a5c-ai/babysitter,GitHub,,,,github-api,a5c-ai/babysitter,1554,90,MIT,2026-01-05T15:26:53Z,2026-07-17T03:58:54Z,,2026-07-17T05:43:07+00:00
+ale-0404,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,claude-code-merge-queue,https://github.com/funador/claude-code-merge-queue,external,github.com,"Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Local FIFO merge queue that serializes parallel Claude Code agents landing on a shared codebase, with a machine-wide build lock and a landing gate that blocks the integration branch until a configured check command passes, wired into the WorktreeCreate hook.",Gives readers a concrete source in claude-code-merge-queue when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (12 stars; 1 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,935,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L935,,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake,builder,source-implementation,ok,https://github.com/funador/claude-code-merge-queue,GitHub - funador/claude-code-merge-queue: The local merge queue for parallel Claude Code agents · GitHub,The local merge queue for parallel Claude Code agents - funador/claude-code-merge-queue,,2026-07-10,2026,funador/claude-code-merge-queue,GitHub,,,,github-api,funador/claude-code-merge-queue,12,1,MIT,2026-07-10T22:05:05Z,2026-07-17T04:52:41Z,,2026-07-17T05:43:07+00:00
+ale-0405,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Blog,📝,Devin can now manage Devins,https://cognition.com/blog/devin-can-now-manage-devins,external,cognition.com,"Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Cognition's multi-Devin architecture where a coordinator Devin scopes a task into pieces, delegates each to a managed Devin running in its own isolated VM with terminal, browser, and dev environment, monitors progress, resolves conflicts, compiles the results, and reads workers' full trajectories to learn what worked and where they got stuck.",Gives readers a concrete source in Devin can now manage Devins when they need to choose an implementation surface for repeatable agent work.,"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,936,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L936,,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation,builder,practitioner-analysis,ok,https://cognition.com/blog/devin-can-now-manage-devins,Devin can now Manage Devins | Cognition,"Devin can now break down large tasks and delegate them to a team of managed Devins, with each running in its own isolated VM in parallel.",,2026-03-19,2026,,cognition.com,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0406,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,pilotfish,https://github.com/Nanako0129/pilotfish,external,github.com,"Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Multi-model orchestration layer for Claude Code where the frontier model plans, decides, and reviews while cheaper models execute volume work through six global subagent roles, with quality guarded by fresh-context verifier subagents rather than expensive models everywhere and graceful degradation when the frontier model is unavailable, shipped as three config files with no runtime code.",Gives readers a concrete source in pilotfish when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (474 stars; 37 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,937,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L937,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context;delegation,builder,source-implementation,ok,https://github.com/Nanako0129/pilotfish,"GitHub - Nanako0129/pilotfish: Multi-model orchestration layer for Claude Code — the frontier model plans, cheaper models execute, verification guards quality. One-prompt install. · GitHub","Multi-model orchestration layer for Claude Code — the frontier model plans, cheaper models execute, verification guards quality. One-prompt install. - Nanako0129/pilotfish",,2026-07-08,2026,Nanako0129/pilotfish,GitHub,,,,github-api,Nanako0129/pilotfish,474,37,MIT,2026-07-08T10:53:34Z,2026-07-17T04:50:38Z,,2026-07-17T05:43:07+00:00
+ale-0407,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,fable-advisor,https://github.com/DannyMac180/fable-advisor,external,github.com,"Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Claude Code plugin implementing an architect pattern where Fable 5 owns specs, decomposition, and verification while routing implementation to cheaper lanes (Grok 4.5 by default, GPT-5.6 via Codex CLI, or Sonnet/Opus fallback), with cross-vendor review and optional racing of two implementers on the same spec.",Gives readers a concrete source in fable-advisor when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (527 stars; 45 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,938,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L938,,Build,build,"Choose runtimes, tools, and delegation surfaces.",verification,builder,source-implementation,ok,https://github.com/DannyMac180/fable-advisor,"GitHub - DannyMac180/fable-advisor: Claude Fable as an orchestrator for Opus, GPT and Grok · GitHub","Claude Fable as an orchestrator for Opus, GPT and Grok - DannyMac180/fable-advisor",,2026-07-03,2026,DannyMac180/fable-advisor,GitHub,,,,github-api,DannyMac180/fable-advisor,527,45,MIT,2026-07-03T01:53:51Z,2026-07-16T18:52:50Z,,2026-07-17T05:43:07+00:00
+ale-0408,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,agent-chief,https://github.com/SmileLikeYe/agent-chief,external,github.com,"Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Local-first chief-of-staff layer that guards human attention across a fleet of agents and alerts: hard rules kill noise fast while a cache-stable LLM judge batches, blocks, or escalates what remains.",Gives readers a concrete source in agent-chief when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (780 stars; 3 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,939,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L939,,Build,build,"Choose runtimes, tools, and delegation surfaces.",escalation,builder,source-implementation,ok,https://github.com/SmileLikeYe/agent-chief,"GitHub - SmileLikeYe/agent-chief: Attention is your scarcest resource. Chief is the local-first layer that guards it — turning every agent, alert, and feed into one honest call: interrupt, or not. · GitHub","Attention is your scarcest resource. Chief is the local-first layer that guards it — turning every agent, alert, and feed into one honest call: interrupt, or not. - SmileLikeYe/agent-chief",,2026-07-04,2026,SmileLikeYe/agent-chief,GitHub,,,,github-api,SmileLikeYe/agent-chief,780,3,MIT,2026-07-04T15:28:56Z,2026-07-16T02:48:32Z,,2026-07-17T05:43:07+00:00
+ale-0409,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,OpenTag,https://github.com/amplifthq/opentag,external,github.com,"Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.","Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Turns an existing work thread into a governed agent work loop: @-mention a coding agent from Slack, GitHub, GitLab, Linear, Lark, Telegram, or Discord, and OpenTag curates the context and manages the run.",Gives readers a concrete source in OpenTag when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (1,363 stars; 73 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,940,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L940,,Build,build,"Choose runtimes, tools, and delegation surfaces.",context,builder,source-implementation,ok,https://github.com/amplifthq/opentag,"GitHub - amplifthq/opentag: Open-source @agent mentions for Slack and GitHub. OpenTag routes tagged requests to Codex, Claude Code, then returns results in thread. · GitHub","Open-source @agent mentions for Slack and GitHub. OpenTag routes tagged requests to Codex, Claude Code, then returns results in thread. - amplifthq/opentag",,2026-06-24,2026,amplifthq/opentag,GitHub,,,,github-api,amplifthq/opentag,1363,73,MIT,2026-06-24T08:05:12Z,2026-07-17T01:52:47Z,,2026-07-17T05:43:07+00:00
+ale-0410,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,herdr,https://github.com/ogulcancelik/herdr,external,github.com,"Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Single-binary terminal multiplexer purpose-built for running fleets of coding agents (Claude Code, Codex, Copilot CLI, Cursor Agent, and 15+ others) with per-agent state tracking.",Gives readers a concrete source in herdr when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (17,294 stars; 1,082 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,941,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L941,,Build,build,"Choose runtimes, tools, and delegation surfaces.",state,builder,source-implementation,ok,https://github.com/ogulcancelik/herdr,GitHub - ogulcancelik/herdr: agent multiplexer that lives in your terminal. · GitHub,agent multiplexer that lives in your terminal. Contribute to ogulcancelik/herdr development by creating an account on GitHub.,,2026-03-27,2026,ogulcancelik/herdr,GitHub,,,,github-api,ogulcancelik/herdr,17294,1082,NOASSERTION,2026-03-27T17:54:33Z,2026-07-17T05:42:55Z,,2026-07-17T05:43:07+00:00
+ale-0411,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Tool,🧰,Orca,https://github.com/stablyai/orca,external,github.com,"Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.","Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop. Resource-specific angle: Open-source agent development environment for orchestrating a fleet of parallel coding agents (20+ backends) on your own subscriptions, with per-agent workspaces and review flow.",Gives readers a concrete source in Orca when they need to choose an implementation surface for repeatable agent work.,"Inspectable GitHub source (20,709 stars; 1,498 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,942,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L942,,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace,builder,source-implementation,ok,https://github.com/stablyai/orca,GitHub - stablyai/orca: Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile. · GitHub,Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile. - stablyai/orca,,2026-03-17,2026,stablyai/orca,GitHub,,,,github-api,stablyai/orca,20709,1498,MIT,2026-03-17T03:28:57Z,2026-07-17T05:43:56Z,,2026-07-17T05:43:07+00:00
+ale-0412,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,Agentic Routing: The Harness-Native Data Flywheel,https://arxiv.org/abs/2607.11399,external,arxiv.org,"Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.","State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Argues model routing must live inside the execution harness rather than in single-turn cost-quality tradeoffs, making step-level model selections from execution state and logging each decision as structured telemetry that feeds back to improve both routers and models.",Gives readers a concrete source in Agentic Routing: The Harness-Native Data Flywheel when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.11399; inspect its method and evaluation before treating results as production evidence.,medium,README.md,943,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L943,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",state;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11399,[2607.11399] Agentic Routing: The Harness-Native Data Flywheel,"Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification. At the same time, frontier and open models are becoming structurally specialized: a model that is strong at code editing, long-context recovery, tool use, mathematical reasoning, or low-latency response may not dominate on the other axes. This makes model selection inside an agent a core systems problem rather than a per-query serving trick. Existing routing methods mostly optimize single-turn cost-quality trade-offs and therefore miss the execution state, intermediate failures, and feedback loops that make agents different from chat completion. We propose Harness-Native agentic routing, a step-level routing paradigm that selects either a single best-fit model for cost-effective execution or multiple complementary models for ensemble-style accuracy improvement, conditioned on the full harness state. The key insight is that every routing decision naturally produces a structured data record -- consisting of the query, harness state, model choice or model set, execution trace, outcome, and cost -- whose labels are supplied by the environment rather than by the router itself. These records form a harness-native data flywheel: execution traces train better routers and harness-native models, which improve cost-quality trade-offs and generate more traces under the same budget. We instantiate this idea in OpenSquilla with a four-layer routing stack, an open LightGBM cold-start ranker, and a staged router-model path that turns logged arena records into progressively stronger routing policies. The report studies singleton and multi-model routing on agentic benchmarks including DRACO and PinchBench, and argues that agentic routing is not merely cost control, but a data engine for agent-native training.",Xinchen Liu; Hang Zhou; Yingjie Zong; Yuchuan Tian; Liuyang Song; Shuo Zhang; Yulong Li; Wei He; Mengyu Zheng; Runke Liu; Siyang Cheng; Xiang Kuang; Hailin Hu; Kai Han; Yunhe Wang,2026-07-13,2026,arXiv,arXiv,,Code: https://github.com/opensquilla/opensquilla,cs.CL,arxiv-api,,,,,,,2607.11399,2026-07-17T05:43:07+00:00
+ale-0413,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Paper,📄,A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution,https://arxiv.org/abs/2607.11138,external,arxiv.org,"Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Formal orchestration architecture that runs agent workflows on a call stack with lazy capability discovery, giving multi-agent loops explicit, inspectable control flow instead of implicit prompt-driven handoffs.",Gives readers a concrete source in A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution when they need to choose an implementation surface for repeatable agent work.,Research source arXiv:2607.11138; inspect its method and evaluation before treating results as production evidence.,medium,README.md,944,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L944,2026-07-15,Build,build,"Choose runtimes, tools, and delegation surfaces.",intake;delegation,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.11138,[2607.11138] A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery,"The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously. This leads to decision-space explosion, context window saturation, and degraded routing accuracy. To address these limitations, this paper presents a hierarchical, skill-based architecture for agentic orchestration. Capabilities are organized as a rooted tree where internal nodes make routing decisions and leaf nodes execute deterministic tasks. The runtime enforces a single-step execution loop governed by a Last-In-First-Out (LIFO) stack, giving the agent a form of memory akin to a Pushdown Automaton, therefore enabling it to track nested execution contexts and resume deterministically from any depth. Capability discovery follows a manifest-driven, lazy-loading protocol: only the immediate children of the active node are loaded, so memory and prompt costs scale with the explored path rather than the global registry. By replacing global memory with localized stack frames, the architecture prevents outputs from one execution branch from leaking into another, establishing the isolation guarantees required for deployment in regulated enterprise environments. We also discuss UPI Help, an AI-powered digital payments support product, as a motivating production deployment context. We provide a mathematical formalization of the orchestration state, detailed algorithmic analysis of the execution loop, and controlled benchmarks comparing flat and hierarchical routing under increasing tool catalogs, multi-step workflow pressure, and visible schema-token exposure per LLM call.",Prashant Devadiga; Abhishek; Adithya Mishra; Alok Singh; Amisha Sinha; Asit Desai; Gaurang Dahad; Harshit Bhushan; Mandati Pramod Reddy; Prakhar Gupta; Rupesh Patil; Siddhi Behere,2026-07-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.11138,2026-07-17T05:43:07+00:00
+ale-0414,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Graph-based agent workflows,https://adk.dev/graphs/,external,adk.dev,"Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official ADK 2.0 graph runtime for explicit, deterministic workflows that mix agents, tools, and code with branching, state, human input, and bounded cycles.",Gives readers a concrete source in Graph-based agent workflows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from adk.dev; use it for current product or standard behavior.,high,README.md,945,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L945,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",workspace;state;escalation,builder,official-documentation,ok,https://adk.dev/graphs/,Graph-based agent workflows - Agent Development Kit (ADK) Agent Development Kit (ADK),Build powerful multi-agent systems with Agent Development Kit (ADK),Google Agent Development Kit,,,Google Agent Development Kit,Google,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0415,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Flows,https://docs.crewai.com/en/concepts/flows,external,docs.crewai.com,"Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official event-driven workflow layer with typed state, branching and loops, persistence decorators, and resume or fork operations for long-running executions.",Gives readers a concrete source in Flows when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from docs.crewai.com; use it for current product or standard behavior.,high,README.md,946,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L946,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",trigger;state,builder,official-documentation,ok,https://docs.crewai.com/v1.15.3/en/concepts/flows,Flows - CrewAI,Learn how to create and manage AI workflows using CrewAI Flows.,CrewAI,,,CrewAI,CrewAI,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0416,Orchestration And Multi-Agent Delegation,orchestration-and-multi-agent-delegation,Docs,📚,Graph,https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/,external,strandsagents.com,"Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official deterministic graph pattern for multi-agent dependencies and cycles, with shared execution state and explicit execution limits.",Gives readers a concrete source in Graph when they need to choose an implementation surface for repeatable agent work.,Primary official documentation from strandsagents.com; use it for current product or standard behavior.,high,README.md,947,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L947,2026-07-17,Build,build,"Choose runtimes, tools, and delegation surfaces.",delegation;state,builder,official-documentation,ok,https://strandsagents.com/docs/user-guide/concepts/multi-agent/graph/,Graph Multi-Agent Pattern | Strands Agents,The open source toolkit for building production agents.,Strands Agents,,,Strands Agents,Strands Agents,,,,primary-page,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0417,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-bench,https://www.swebench.com/,external,www.swebench.com,Benchmark for resolving real GitHub issues through code editing and tests.,Benchmark for resolving real GitHub issues through code editing and tests.,Benchmark for resolving real GitHub issues through code editing and tests.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for resolving real GitHub issues through code editing and tests.,Gives readers a concrete source in SWE-bench when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,955,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L955,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://www.swebench.com/,SWE-bench Leaderboards,,,,,,swebench.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0418,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,https://arxiv.org/abs/2310.06770,external,arxiv.org,Original SWE-bench paper.,Original SWE-bench paper.,Original SWE-bench paper.,Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Original SWE-bench paper.,Gives readers a concrete source in SWE-bench: Can Language Models Resolve Real-World GitHub Issues? when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2310.06770; inspect its method and evaluation before treating results as production evidence.,medium,README.md,956,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L956,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2310.06770,[2310.06770] SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,"Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we introduce SWE-bench, an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. The best-performing model, Claude 2, is able to solve a mere $1.96$% of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.",Carlos E. Jimenez; John Yang; Alexander Wettig; Shunyu Yao; Kexin Pei; Ofir Press; Karthik Narasimhan,2023-10-10,2023,arXiv,arXiv,,"Data, code, and leaderboard are available at https://www.swebench.com ICLR 2024, https://openreview.net/forum?id=VTF8yNQM66",cs.CL,arxiv-api,,,,,,,2310.06770,2026-07-17T05:43:07+00:00
+ale-0419,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,SWE-bench Goes Live,https://arxiv.org/abs/2505.23419,external,arxiv.org,Dynamic benchmark designed to reduce overfitting to static issue sets.,Dynamic benchmark designed to reduce overfitting to static issue sets.,Dynamic benchmark designed to reduce overfitting to static issue sets.,The work turns loop quality into a measurable task or score. Resource-specific angle: Dynamic benchmark designed to reduce overfitting to static issue sets.,Gives readers a concrete source in SWE-bench Goes Live when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2505.23419; inspect its method and evaluation before treating results as production evidence.,medium,README.md,957,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L957,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2505.23419,[2505.23419] SWE-bench Goes Live!,"The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability and introduce risks of overfitting and data contamination. In this work, we present SWE-bench-Live, a live-updatable benchmark designed to overcome these challenges. Our initial release consists of 1,319 tasks derived from real GitHub issues created since 2024, spanning 93 repositories. Each task is accompanied by a dedicated Docker image to ensure reproducible execution. Central to our benchmark is \method, an automated curation pipeline that streamlines the entire process from instance creation to environment setup, removing manual bottlenecks and enabling scalability and continuous updates. We evaluate a range of state-of-the-art agent frameworks and LLMs on SWE-bench-Live, revealing a substantial performance gap compared to static benchmarks like SWE-bench, even under controlled evaluation conditions. To better understand this discrepancy, we perform detailed analyses across repository origin, issue recency, and task difficulty. By providing a fresh, diverse, and executable benchmark grounded in live repository activity, SWE-bench-Live facilitates rigorous, contamination-resistant evaluation of LLMs and agents in dynamic, real-world software development settings.",Linghao Zhang; Shilin He; Chaoyun Zhang; Yu Kang; Bowen Li; Chengxing Xie; Junhao Wang; Maoquan Wang; Yufan Huang; Shengyu Fu; Elsie Nallipogu; Qingwei Lin; Yingnong Dang; Saravan Rajmohan; Dongmei Zhang,2025-05-29,2025,arXiv,arXiv,,"Homepage: \url{https://swe-bench-live.github.io/}, Code: \url{https://github.com/SWE-bench-Live}, Dataset: \url{https://huggingface.co/SWE-bench-Live}",cs.SE,arxiv-api,,,,,,,2505.23419,2026-07-17T05:43:07+00:00
+ale-0420,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Terminal-Bench,https://www.tbench.ai/,external,www.tbench.ai,Benchmark for agents operating in terminal environments.,Benchmark for agents operating in terminal environments.,Benchmark for agents operating in terminal environments.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for agents operating in terminal environments.,Gives readers a concrete source in Terminal-Bench when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,958,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L958,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://www.tbench.ai/,Terminal-Bench,A benchmark for terminal agents,,,,,Terminal-Bench,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0421,Benchmarks And Evaluation,benchmarks-and-evaluation,Tool,🧰,Terminal-Bench repository,https://github.com/harbor-framework/terminal-bench,external,github.com,Open-source benchmark and harness for hard terminal tasks.,Open-source benchmark and harness for hard terminal tasks.,Open-source benchmark and harness for hard terminal tasks.,The work turns loop quality into a measurable task or score. Resource-specific angle: Open-source benchmark and harness for hard terminal tasks.,Gives readers a concrete source in Terminal-Bench repository when they need to measure progress and gate completion with repeatable evidence.,"Inspectable GitHub source (2,458 stars; 558 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,959,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L959,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,builder;evaluator,source-implementation,ok,https://github.com/harbor-framework/terminal-bench,GitHub - harbor-framework/terminal-bench: A benchmark for LLMs on complicated tasks in the terminal · GitHub,A benchmark for LLMs on complicated tasks in the terminal - harbor-framework/terminal-bench,,2025-01-17,2025,harbor-framework/terminal-bench,GitHub,,,,github-api,harbor-framework/terminal-bench,2458,558,Apache-2.0,2025-01-17T22:34:26Z,2026-07-16T09:24:33Z,,2026-07-17T05:43:07+00:00
+ale-0422,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AgentBench,https://arxiv.org/abs/2308.03688,external,arxiv.org,Multi-environment benchmark for evaluating LLMs as agents.,Multi-environment benchmark for evaluating LLMs as agents.,Multi-environment benchmark for evaluating LLMs as agents.,The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-environment benchmark for evaluating LLMs as agents.,Gives readers a concrete source in AgentBench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2308.03688; inspect its method and evaluation before treating results as production evidence.,medium,README.md,960,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L960,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2308.03688,[2308.03688] AgentBench: Evaluating LLMs as Agents,"The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities. Our extensive test over \num API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and many OSS competitors that are no larger than 70B. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Improving instruction following and training on high quality multi-round alignment data could improve agent performance. And different from existing assumptions, training on code present ambivalent impacts on different agent tasks. Datasets, environments, and an integrated evaluation package for AgentBench are released at https://github.com/THUDM/AgentBench.",Xiao Liu; Hao Yu; Hanchen Zhang; Yifan Xu; Xuanyu Lei; Hanyu Lai; Yu Gu; Hangliang Ding; Kaiwen Men; Kejuan Yang; Shudan Zhang; Xiang Deng; Aohan Zeng; Zhengxiao Du; Chenhui Zhang; Sheng Shen; Tianjun Zhang; Yu Su; Huan Sun; Minlie Huang; Yuxiao Dong; Jie Tang,2023-08-07,2023,arXiv,arXiv,,Published in ICLR 2024,cs.AI,arxiv-api,,,,,,,2308.03688,2026-07-17T05:43:07+00:00
+ale-0423,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,WebArena,https://arxiv.org/abs/2307.13854,external,arxiv.org,Realistic web environment for autonomous agents.,Realistic web environment for autonomous agents.,Realistic web environment for autonomous agents.,Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Realistic web environment for autonomous agents.,Gives readers a concrete source in WebArena when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2307.13854; inspect its method and evaluation before treating results as production evidence.,medium,README.md,961,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L961,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2307.13854,[2307.13854] WebArena: A Realistic Web Environment for Building Autonomous Agents,"With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress.",Shuyan Zhou; Frank F. Xu; Hao Zhu; Xuhui Zhou; Robert Lo; Abishek Sridhar; Xianyi Cheng; Tianyue Ou; Yonatan Bisk; Daniel Fried; Uri Alon; Graham Neubig,2023-07-25,2023,arXiv,arXiv,,"Our code, data, environment reproduction resources, and video demonstrations are publicly available at https://webarena.dev/",cs.AI,arxiv-api,,,,,,,2307.13854,2026-07-17T05:43:07+00:00
+ale-0424,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,OSWorld,https://arxiv.org/abs/2404.07972,external,arxiv.org,Benchmark for multimodal agents operating full computer environments.,Benchmark for multimodal agents operating full computer environments.,Benchmark for multimodal agents operating full computer environments.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for multimodal agents operating full computer environments.,Gives readers a concrete source in OSWorld when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2404.07972; inspect its method and evaluation before treating results as production evidence.,medium,README.md,962,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L962,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2404.07972,[2404.07972] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments,"Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWorld, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36% of the tasks, the best model achieves only 12.24% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWorld provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.",Tianbao Xie; Danyang Zhang; Jixuan Chen; Xiaochuan Li; Siheng Zhao; Ruisheng Cao; Toh Jing Hua; Zhoujun Cheng; Dongchan Shin; Fangyu Lei; Yitao Liu; Yiheng Xu; Shuyan Zhou; Silvio Savarese; Caiming Xiong; Victor Zhong; Tao Yu,2024-04-11,2024,arXiv,arXiv,,"51 pages, 21 figures",cs.AI,arxiv-api,,,,,,,2404.07972,2026-07-17T05:43:07+00:00
+ale-0425,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,ToolBench,https://arxiv.org/abs/2307.16789,external,arxiv.org,Tool-use benchmark and dataset for tool-augmented agents.,Tool-use benchmark and dataset for tool-augmented agents.,Tool-use benchmark and dataset for tool-augmented agents.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Tool-use benchmark and dataset for tool-augmented agents.,Gives readers a concrete source in ToolBench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2307.16789; inspect its method and evaluation before treating results as production evidence.,medium,README.md,963,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L963,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2307.16789,[2307.16789] ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs,"Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excellent tool-use capabilities of state-of-the-art (SOTA) closed-source LLMs, e.g., ChatGPT. To bridge this gap, we introduce ToolLLM, a general tool-use framework encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is constructed automatically using ChatGPT. Specifically, the construction can be divided into three stages: (i) API collection: we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub; (ii) instruction generation: we prompt ChatGPT to generate diverse instructions involving these APIs, covering both single-tool and multi-tool scenarios; (iii) solution path annotation: we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To enhance the reasoning capabilities of LLMs, we develop a novel depth-first search-based decision tree algorithm. It enables LLMs to evaluate multiple reasoning traces and expand the search space. Moreover, to evaluate the tool-use capabilities of LLMs, we develop an automatic evaluator: ToolEval. Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction. Experiments show that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. Our ToolLLaMA also demonstrates strong zero-shot generalization ability in an out-of-distribution tool-use dataset: APIBench.",Yujia Qin; Shihao Liang; Yining Ye; Kunlun Zhu; Lan Yan; Yaxi Lu; Yankai Lin; Xin Cong; Xiangru Tang; Bill Qian; Sihan Zhao; Lauren Hong; Runchu Tian; Ruobing Xie; Jie Zhou; Mark Gerstein; Dahai Li; Zhiyuan Liu; Maosong Sun,2023-07-31,2023,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2307.16789,2026-07-17T05:43:07+00:00
+ale-0426,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,GAIA,https://arxiv.org/abs/2311.12983,external,arxiv.org,"Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for general AI assistants requiring reasoning, tool use, and multi-step work.",Gives readers a concrete source in GAIA when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2311.12983; inspect its method and evaluation before treating results as production evidence.,medium,README.md,964,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L964,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2311.12983,[2311.12983] GAIA: a benchmark for General AI Assistants,"We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark.",Grégoire Mialon; Clémentine Fourrier; Craig Swift; Thomas Wolf; Yann LeCun; Thomas Scialom,2023-11-21,2023,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2311.12983,2026-07-17T05:43:07+00:00
+ale-0427,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Tau-bench,https://arxiv.org/abs/2406.12045,external,arxiv.org,Benchmark for tool-agent-user interactions in realistic domains.,Benchmark for tool-agent-user interactions in realistic domains.,Benchmark for tool-agent-user interactions in realistic domains.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for tool-agent-user interactions in realistic domains.,Gives readers a concrete source in Tau-bench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2406.12045; inspect its method and evaluation before treating results as production evidence.,medium,README.md,965,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L965,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2406.12045,[2406.12045] $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains,"Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state. We also propose a new metric (pass^k) to evaluate the reliability of agent behavior over multiple trials. Our experiments show that even state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point to the need for methods that can improve the ability of agents to act consistently and follow rules reliably.",Shunyu Yao; Noah Shinn; Pedram Razavi; Karthik Narasimhan,2024-06-17,2024,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2406.12045,2026-07-17T05:43:07+00:00
+ale-0428,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,VisualWebArena,https://arxiv.org/abs/2401.13649,external,arxiv.org,Visually grounded web-agent benchmark extending WebArena.,Visually grounded web-agent benchmark extending WebArena.,Visually grounded web-agent benchmark extending WebArena.,The work turns loop quality into a measurable task or score. Resource-specific angle: Visually grounded web-agent benchmark extending WebArena.,Gives readers a concrete source in VisualWebArena when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2401.13649; inspect its method and evaluation before treating results as production evidence.,medium,README.md,966,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L966,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2401.13649,[2401.13649] VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks,"Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on realistic \textit{visually grounded tasks}. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web. Our code, baseline models, and data is publicly available at https://jykoh.com/vwa.",Jing Yu Koh; Robert Lo; Lawrence Jang; Vikram Duvvur; Ming Chong Lim; Po-Yu Huang; Graham Neubig; Shuyan Zhou; Ruslan Salakhutdinov; Daniel Fried,2024-01-24,2024,arXiv,arXiv,,Accepted to ACL 2024. 24 pages. Project page: https://jykoh.com/vwa,cs.LG,arxiv-api,,,,,,,2401.13649,2026-07-17T05:43:07+00:00
+ale-0429,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AppWorld,https://arxiv.org/abs/2407.18901,external,arxiv.org,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Benchmark of interactive app tasks with state-based and execution-based evaluation.,Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of interactive app tasks with state-based and execution-based evaluation.,Gives readers a concrete source in AppWorld when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2407.18901; inspect its method and evaluation before treating results as production evidence.,medium,README.md,967,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L967,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2407.18901,[2407.18901] AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents,"Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built $\textbf{AppWorld Engine}$, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created $\textbf{AppWorld Benchmark}$ (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT-4o, solves only ~49% of our 'normal' tasks and ~30% of 'challenge' tasks, while other models solve at least 16% fewer. This highlights the benchmark's difficulty and AppWorld's potential to push the frontiers of interactive coding agents. The project website is available at https://appworld.dev/.",Harsh Trivedi; Tushar Khot; Mareike Hartmann; Ruskin Manku; Vinty Dong; Edward Li; Shashank Gupta; Ashish Sabharwal; Niranjan Balasubramanian,2024-07-26,2024,arXiv,arXiv,,ACL'24 Camera Ready,cs.SE,arxiv-api,,,,,,,2407.18901,2026-07-17T05:43:07+00:00
+ale-0430,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Vending-Bench,https://arxiv.org/abs/2502.15840,external,arxiv.org,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for long-term coherence of autonomous agents; documents how small errors compound over very long loop horizons.,Gives readers a concrete source in Vending-Bench when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2502.15840; inspect its method and evaluation before treating results as production evidence.,medium,README.md,968,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L968,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2502.15840,[2502.15840] Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents,"While Large Language Models (LLMs) can exhibit impressive proficiency in isolated, short-term tasks, they often fail to maintain coherent performance over longer time horizons. In this paper, we present Vending-Bench, a simulated environment designed to specifically test an LLM-based agent's ability to manage a straightforward, long-running business scenario: operating a vending machine. Agents must balance inventories, place orders, set prices, and handle daily fees - tasks that are each simple but collectively, over long horizons (>20M tokens per run) stress an LLM's capacity for sustained, coherent decision-making. Our experiments reveal high variance in performance across multiple LLMs: Claude 3.5 Sonnet and o3-mini manage the machine well in most runs and turn a profit, but all models have runs that derail, either through misinterpreting delivery schedules, forgetting orders, or descending into tangential ""meltdown"" loops from which they rarely recover. We find no clear correlation between failures and the point at which the model's context window becomes full, suggesting that these breakdowns do not stem from memory limits. Apart from highlighting the high variance in performance over long time horizons, Vending-Bench also tests models' ability to acquire capital, a necessity in many hypothetical dangerous AI scenarios. We hope the benchmark can help in preparing for the advent of stronger AI systems.",Axel Backlund; Lukas Petersson,2025-02-20,2025,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2502.15840,2026-07-17T05:43:07+00:00
+ale-0431,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Vending-Bench leaderboard,https://andonlabs.com/evals/vending-bench,external,andonlabs.com,Live long-horizon coherence results from Andon Labs.,Live long-horizon coherence results from Andon Labs.,Live long-horizon coherence results from Andon Labs.,The work turns loop quality into a measurable task or score. Resource-specific angle: Live long-horizon coherence results from Andon Labs.,Gives readers a concrete source in Vending-Bench leaderboard when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,969,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L969,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://andonlabs.com/evals/vending-bench,Vending-Bench: Testing long-term coherence in agents | Andon Labs,"How do agents act over very long horizons? We answer this by letting agents manage a simulated vending machine business. The agents need to handle ordering, inventory management, and pricing over long context horizons to successfully make money.",,,,,andonlabs.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0432,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios,https://arxiv.org/abs/2512.18470,external,arxiv.org,"Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.","The work turns loop quality into a measurable task or score. Resource-specific angle: Release-note-derived evolution tasks where agents score far below isolated-issue benchmarks, quantifying the long-horizon gap loops must manage.",Gives readers a concrete source in SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,970,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L970,,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2512.18470,[2512.18470] SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios,"Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-horizon endeavor: developers interpret high-level requirements, coordinate changes across many files, and evolve codebases over multiple iterations while preserving functionality. We introduce SWE-EVO, a benchmark for this long-horizon software evolution challenge. Constructed from release notes of seven mature open-source Python projects, SWE-EVO comprises 48 tasks requiring multi-step modifications spanning an average of 21 files, validated against test suites averaging 874 tests per instance. Experiments reveal a striking capability gap: GPT-5.4 with OpenHands achieves only 25% on SWE-EVO versus 72.80% achieved by GPT-5.2 on SWE-Bench Verified, showing that current agents struggle with sustained, multi-file reasoning. We also propose Fix Rate, a metric capturing partial progress on these complex, long-horizon tasks.",Tue Le; Minh V. T. Thai; Dung Nguyen Manh; Huy Phan Nhat; Nghi D. Q. Bui,2025-12-20,2025,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2512.18470,2026-07-17T05:43:07+00:00
+ale-0433,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification,https://arxiv.org/abs/2604.01687,external,arxiv.org,"A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: A skill generator and a co-evolving surrogate verifier improve multi-file skill packages over iterations, evaluated on the SkillsBench benchmark of structured skill bundles.",Gives readers a concrete source in EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.01687; inspect its method and evaluation before treating results as production evidence.,medium,README.md,971,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L971,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.01687,[2604.01687] CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification,"Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose CoEvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, CoEvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, CoEvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and also exhibits strong generalization capabilities to six additional LLMs.",Hanrong Zhang; Shicheng Fan; Henry Peng Zou; Yankai Chen; Zhenting Wang; Jiayu Zhou; Chengze Li; Wei-Chieh Huang; Yifei Yao; Kening Zheng; Xue Liu; Xiaoxiao Li; Philip S. Yu,2026-04-02,2026,arXiv,arXiv,,Code will be released,cs.AI,arxiv-api,,,,,,,2604.01687,2026-07-17T05:43:07+00:00
+ale-0434,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering,https://arxiv.org/abs/2605.17526,external,arxiv.org,"Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark for agents on multi-dependency, interactive enterprise tasks, with automated evaluation that probes where long-horizon loops break down.",Gives readers a concrete source in SaaSBench: Coding Agents in Long-Horizon Enterprise SaaS Engineering when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,972,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L972,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.17526,[2605.17526] SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering,"As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to capture the heterogeneous environments, full-stack orchestration, and system-level complexity of real enterprise Software as a Service (SaaS) systems, leaving a critical gap in assessing agents under realistic engineering constraints. To fill this gap, we introduce SaaSBench, the first benchmark designed to explore the boundaries of AI agents in enterprise SaaS engineering. Spanning 30 complex tasks across 6 SaaS domains with 5,370 validation nodes, it incorporates 8 programming languages, 6 databases, and 13 frameworks to meticulously mirror real-world software heterogeneity. Furthermore, we design a dependency-aware hybrid evaluation paradigm tailored for complex systems with long horizons and multi-component coupling, enabling fine-grained, reproducible assessment. Crucially, our extensive experiments reveal a striking insight: the primary bottleneck for state-of-the-art agents is not generating isolated code logic, but successfully configuring and integrating a multi-component system. Over 95\% of task failures occur before agents even reach deep business logic, with models often falling victim to overconfidence and prematurely halting during foundational system setup, or getting trapped in ineffective debugging loops. We hope SaaSBench serves as a practical and challenging testbed to drive the evolution of reliable, system-level coding agents. The code is available at \url{https://github.com/ShadeCloak/SaaSbench}.",Qingnan Ren; Shun Zou; Shiting Huang; Ziao Zhang; Kou Shi; Zhen Fang; Yiming Zhao; Yu Zeng; Qisheng Su; Lin Chen; Yong Wang; Zehui Chen; Xiangxiang Chu; Feng Zhao,2026-05-17,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2605.17526,2026-07-17T05:43:07+00:00
+ale-0435,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades,https://arxiv.org/abs/2605.15846,external,arxiv.org,"115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: 115 real version-upgrade tasks across 17 repositories requiring multi-file changes (median ~3,700 lines), stressing how far agent loops sustain coherent, large-scale work.",Gives readers a concrete source in RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,973,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L973,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.15846,[2605.15846] RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades,"Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass/fail evaluation outcomes, and thus fail to capture long-horizon, multi-target development at real engineering scale. To address this gap, we present RoadmapBench, a benchmark of 115 long-horizon coding tasks grounded in real open-source version upgrades across 17 repositories and 5 programming languages. Each task places the agent on a source-version code snapshot and provides a multi-target roadmap instruction requiring it to implement the functionality introduced in the target version, with a median modification of 3,700 lines across 51 files. We conduct a systematic evaluation on thirteen frontier models and find that even the strongest, Claude-Opus-4.7, resolves only 39.1% of tasks, while the weakest achieves merely 5.2%, in stark contrast to existing bug-fix benchmarks, suggesting that long-horizon software development remains a largely unsolved problem.",Xinbo Xu; Ruihan Yang; Haiyang Shen; Wendong Xu; Bofei Gao; Ruoyu Wu; Kean Shi; Weichu Xie; Xuanzhong Chen; Ming Wu; Jason Zeng; Michael Heinrich; Elvis Zhang; Liang Chen; Kuan Li; Baobao Chang,2026-05-15,2026,arXiv,arXiv,,"30 pages, 15 figures",cs.SE,arxiv-api,,,,,,,2605.15846,2026-07-17T05:43:07+00:00
+ale-0436,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code,https://arxiv.org/abs/2503.07832,external,arxiv.org,"Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.",Gives readers a concrete source in RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,974,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L974,,Verify,verify,"Gate progress with tests, evals, and evidence.",state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2503.07832,[2503.07832] RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code,"Recent advances in language model (LM) agents and function calling have enabled autonomous, feedback-driven systems to solve problems across various digital domains. To better understand the unique limitations of LM agents, we introduce RefactorBench, a benchmark consisting of 100 large handcrafted multi-file refactoring tasks in popular open-source repositories. Solving tasks within RefactorBench requires thorough exploration of dependencies across multiple files and strong adherence to relevant instructions. Every task is defined by 3 natural language instructions of varying specificity and is mutually exclusive, allowing for the creation of longer combined tasks on the same repository. Baselines on RefactorBench reveal that current LM agents struggle with simple compositional tasks, solving only 22% of tasks with base instructions, in contrast to a human developer with short time constraints solving 87%. Through trajectory analysis, we identify various unique failure modes of LM agents, and further explore the failure mode of tracking past actions. By adapting a baseline agent to condition on representations of state, we achieve a 43.9% improvement in solving RefactorBench tasks. We further extend our state-aware approach to encompass entire digital environments and outline potential directions for future research. RefactorBench aims to support the study of LM agents by providing a set of real-world, multi-hop tasks within the realm of code.",Dhruv Gautam; Spandan Garg; Jinu Jang; Neel Sundaresan; Roshanak Zilouchian Moghaddam,2025-03-10,2025,arXiv,arXiv,,ICLR 2025 Camera Ready,cs.AI,arxiv-api,,,,,,,2503.07832,2026-07-17T05:43:07+00:00
+ale-0437,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents,https://arxiv.org/abs/2606.22678,external,arxiv.org,"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.",Gives readers a concrete source in RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,975,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L975,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.22678,[2606.22678] RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents,"Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks. Existing benchmarks evaluate these agents almost exclusively on outcome correctness: whether generated code passes tests or resolves issues. We argue that this outcome-only lens is insufficient: an agent that arrives at a correct solution through reckless trial-and-error, without planning, verification, or graceful recovery, is fundamentally less reliable than one that follows sound engineering discipline. We introduce RigorBench, the first benchmark designed to measure process discipline in AI coding agents. RigorBench evaluates these harnesses across five pillars: Planning Fidelity, Verification Coverage, Recovery Efficiency, Abstention Quality, and Atomic Transition Integrity. A composite RigorScore aggregates these dimensions into a single metric via a weighted sum. We curate a suite of 30 tasks spanning five categories - Plan-Then-Build, Verify-Or-Die, Doom Loop Gauntlet, Know When to Fold, and Don't Break the Build-and evaluate leading harnesses in a controlled with/without experimental design against baseline coding assistants. Our results show that structured process discipline not only improves process quality scores by an average of 41% but also raises downstream outcome correctness by 17%, providing the first quantitative evidence that how agents code matters as much as what they produce. We release the full benchmark, scoring rubrics, and trajectory analysis tools as open-source artifacts.",Meher Bhaskar Madiraju; Meher Sai Preetam Madiraju,2026-06-21,2026,arXiv,arXiv,,"9 pages, 7 tables, 1 figure",cs.SE,arxiv-api,,,,,,,2606.22678,2026-07-17T05:43:07+00:00
+ale-0438,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks,https://arxiv.org/abs/2603.24755,external,arxiv.org,"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.",Gives readers a concrete source in SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,976,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L976,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state;budget,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.24755,[2603.24755] SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks,"Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmarks attempt to remedy this but heavily constrain an agent's design decision space, making it impossible to faithfully measure how their decisions shape future extensions. We introduce SlopCodeBench, a benchmark of 36 problems and 196 checkpoints where agents repeatedly extend their own solutions. Unlike prior iterative benchmarks, our evolving specifications demand architectural decisions but leave internal structure to the agent. We measure two forms of degradation: structural erosion (concentrated complexity) and verbosity (redundant code). Evaluating 15 coding agents across open and closed models, we find that no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints. Quality degrades across checkpoints, with structural erosion rising in 77% of trajectories and verbosity in 75.5%. Compared to 473 open-source Python repositories, agent code is 2.3x more verbose and 2.0x more eroded, and the human repositories degrade less often and by smaller margins across their git histories. Explicit quality guidance reduces initial verbosity and erosion by up to a third, without affecting degradation rates. SlopCodeBench provides the first measurement of code degradation under iterative extension, revealing that agents pass checkpoints while producing code that erodes and bloats with each turn.",Gabriel Orlanski; Devjeet Roy; Alexander Yun; Changho Shin; Alex Gu; Albert Ge; Dyah Adila; Nicholas Roberts; Frederic Sala; Aws Albarghouthi,2026-03-25,2026,arXiv,arXiv,"10.5281/zenodo.18405900,",Code and Leaderboards are located at https://www.scbench.ai,cs.SE,arxiv-api,,,,,,,2603.24755,2026-07-17T05:43:07+00:00
+ale-0439,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces,https://arxiv.org/abs/2602.14337,external,arxiv.org,"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","The work turns loop quality into a measurable task or score. Resource-specific angle: Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.",Gives readers a concrete source in LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,977,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L977,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2602.14337,[2602.14337] LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces,"Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics, fail to rigorously evaluate the long-horizon planning and execution capabilities essential for realistic software engineering. To address these gaps, we introduce LongCLI-Bench, a comprehensive benchmark designed to evaluate agentic capabilities across long-horizon, realistic tasks. We curated 20 high-quality, long-horizon tasks from over 1,000 computer science assignments and real-world workflows, covering four engineering categories: from scratch, feature addition, bug fixing, and refactoring. We propose a dual-set testing protocol for LongCLI-Bench, which measures requirement fulfillment (fail-to-pass) and regression avoidance (pass-to-pass), and incorporates step-level scoring to pinpoint execution failures. Extensive experiments reveal that even state-of-the-art agents achieve pass rates below 20% in LongCLI-Bench. Step-level analysis further indicates that the majority of tasks stall at less than 30% completion, highlighting that critical failures often occur in the early stages. Although self-correction offers marginal gains, human-agent collaboration through plan injection and interactive guidance yields significantly higher improvements. These results highlight that future research must emphasize the development of synergistic human-agent workflows alongside advances in agents' planning and execution capabilities to overcome key challenges in long-horizon task performance.",Yukang Feng; Jianwen Sun; Zelai Yang; Jiaxin Ai; Chuanhao Li; Zizhen Li; Fanrui Zhang; Kang He; Rui Ma; Jifan Lin; Jie Sun; Yang Xiao; Sizhuo Zhou; Wenxiao Wu; Yiming Liu; Pengfei Liu; Yu Qiao; Shenglin Zhang; Kaipeng Zhang,2026-02-15,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2602.14337,2026-07-17T05:43:07+00:00
+ale-0440,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?,https://arxiv.org/abs/2606.29920,external,arxiv.org,"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.",Gives readers a concrete source in Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios? when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,978,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L978,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.29920,[2606.29920] Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?,"Rubric-based scoring has become a widely used paradigm in model evaluation, typically with LLM-as-a-Judge (LaaJ) for rubric scoring. However, the reliability of LaaJ for rubric scoring remains underexplored. This concern is especially pronounced in agentic scenarios, where long, complex outputs further challenge reliable scoring. To address this, we conduct a systematic meta-evaluation of LaaJ reliability for rubric verification. We introduce RuVerBench, the first benchmark for assessing LaaJ reliability in rubric verification for agentic scenarios. RuVerBench covers two prevalent agentic domains, deep research and agentic coding, with 2,458 instances, each containing a model-generated output, a rubric, and a human-annotated label indicating whether the output satisfies the rubric. Using RuVerBench, we evaluate numerous frontier LLMs and find that even the most advanced models achieve strong performance but still exhibit substantial noise. We further analyze the impact of key LaaJ strategies, including prompt design, batching, and majority voting, on rubric verification. We find that weaker models are more sensitive to prompt variations, batched verification presents a trade-off between accuracy and efficiency, and majority voting yields effective but diminishing returns. We have released our dataset and code to facilitate future research: https://github.com/THU-KEG/RuVerBench.",Yangda Peng; Yunjia Qi; Hao Peng; Haotian Xia; Guanzhong He; Xintong Shi; Richeng Xuan; Songyuanyi Lu; Yixian Liu; Zhichao Hu; Yuhong Liu; Lei Hou; Bin Xu; Juanzi Li,2026-06-29,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2606.29920,2026-07-17T05:43:07+00:00
+ale-0441,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SentinelBench: A Benchmark for Long-Running Monitoring Agents,https://arxiv.org/abs/2606.05342,external,arxiv.org,"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","The work turns loop quality into a measurable task or score. Resource-specific angle: Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.",Gives readers a concrete source in SentinelBench: A Benchmark for Long-Running Monitoring Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,979,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L979,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.05342,[2606.05342] SentinelBench: A Benchmark for Long-Running Monitoring Agents,"AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress. This is the wrong approach for many long-running tasks, which are better served by a strategy of sustained attention. Instead, agents should monitor an environment, notice when an external event makes progress possible, then respond promptly without wasting resources while waiting. To measure progress on this class of tasks, we introduce SentinelBench, an open-source benchmark for time-evolving monitoring tasks. SentinelBench contains 100 tasks across 10 synthetic web environments, including email, calendars, finance, professional networking, and entertainment. Each environment exposes a live web interface and replays a scripted sequence of events, requiring agents to navigate and reason about web pages whose state shifts underfoot. SentinelBench measures task completion, reaction time, and resource use, exposing the tradeoff between responsiveness and cost. We report results across three models and two browser-agent harnesses, establishing performance baselines for future comparison and demonstrating how agent design choices can dramatically impact key metrics. Together, these results show that SentinelBench distinguishes meaningful differences in agent behavior.",Matheus Kunzler Maldaner; Adam Fourney; Amanda Swearngin; Hussein Mozannar; Gagan Bansal; Maya Murad; Rafah Hosn; Saleema Amershi,2026-06-03,2026,arXiv,arXiv,,"18 pages, 16 figures",cs.AI,arxiv-api,,,,,,,2606.05342,2026-07-17T05:43:07+00:00
+ale-0442,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-Together: Evaluating Coding Agents in Interactive User Sessions,https://arxiv.org/abs/2606.29957,external,arxiv.org,"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.",Gives readers a concrete source in SWE-Together: Evaluating Coding Agents in Interactive User Sessions when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,980,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L980,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.29957,[2606.29957] SWE-Together: Evaluating Coding Agents in Interactive User Sessions,"Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assistance is interactive, with users clarifying goals, adding constraints, and correcting mistakes over multiple turns. We introduce SWE-Together, a multi-turn benchmark reconstructed from real user-agent coding sessions. To make real interactions verifiable, we curate 109 repository-level tasks from 11,260 recorded sessions, selecting sessions with recoverable repository states, clear user goals, and observable outcomes. To replay these interactions across agents, we build a reactive LLM-based user simulator that preserves the original users' intents and provides feedback when the coding agent's progress requires it. To evaluate agents as collaborators, we measure both final repository correctness and the number of corrective feedback turns required during the interaction. Experiments with frontier coding agents show that stronger agents generally achieve higher final success rates while requiring fewer interventions, suggesting an improved user experience.",Yifan Wu; Zhuokai Zhao; Songlin Li; Ho Hin Lee; Jiacheng Zhu; Shirley Wu; Tianhe Yu; Serena Li; Lizhu Zhang; Xiangjun Fan; Shengzhi Li,2026-06-29,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2606.29957,2026-07-17T05:43:07+00:00
+ale-0443,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break,https://arxiv.org/abs/2604.11978,external,arxiv.org,"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","The work turns loop quality into a measurable task or score. Resource-specific angle: Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.",Gives readers a concrete source in The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,981,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L981,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;escalation,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2604.11978,[2604.11978] The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break,"Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross-domain diagnostic benchmark for systematically constructing tasks and analyzing long-horizon failure behaviors in LLM-based agents. Using HORIZON, we evaluate state-of-the-art (SOTA) agents from multiple model families (GPT-5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon-dependent degradation patterns. We further propose a trajectory-grounded LLM-as-a-Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter-annotator \kappa=0.61; human-judge \kappa=0.84). Our findings offer an initial methodological step toward systematic, cross-domain analysis of long-horizon agent failures and offer practical guidance for building more reliable long-horizon agents. We release our project website at \href{https://xwang2775.github.io/horizon-leaderboard/}{HORIZON Leaderboard} and welcome contributions from the community.",Xinyu Jessica Wang; Haoyue Bai; Yiyou Sun; Haorui Wang; Shuibai Zhang; Wenjie Hu; Mya Schroder; Bilge Mutlu; Dawn Song; Robert D Nowak,2026-04-13,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2604.11978,2026-07-17T05:43:07+00:00
+ale-0444,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents,https://arxiv.org/abs/2603.29231,external,arxiv.org,"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.",Gives readers a concrete source in Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2603.29231; inspect its method and evaluation before treating results as production evidence.,medium,README.md,982,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L982,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2603.29231,[2603.29231] Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents,"Existing benchmarks measure capability -- whether a model succeeds on a single attempt -- but production deployments require reliability -- consistent success across repeated attempts on tasks of varying duration. We show these properties diverge systematically as task duration grows, and that pass@1 on short tasks is structurally blind to this divergence. We introduce a reliability science framework for long-horizon LLM agents with four metrics: Reliability Decay Curve (RDC), Variance Amplification Factor (VAF), Graceful Degradation Score (GDS), and Meltdown Onset Point (MOP). We evaluate 10 models across 23,392 episodes on a 396-task benchmark spanning four duration buckets and three domains. Key findings: (1) reliability decay is domain-stratified -- SE GDS drops from 0.90 to 0.44 while document processing is nearly flat (0.74 to 0.71); (2) VAF bifurcates by capability tier -- high VAF is a capability signature, not an instability signal; (3) capability and reliability rankings diverge substantially, with multi-rank inversions at long horizons; (4) frontier models have the highest meltdown rates (up to 19%) because they attempt ambitious multi-step strategies that sometimes spiral; and (5) memory scaffolds universally hurt long-horizon performance across all 10 models. These results motivate reliability as a first-class evaluation dimension alongside capability.",Aaditya Khanal; Yangyang Tao; Junxiu Zhou,2026-03-31,2026,arXiv,arXiv,,"23 pages, 4 figures",cs.AI,arxiv-api,,,,,,,2603.29231,2026-07-17T05:43:07+00:00
+ale-0445,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SEAGym: An Evaluation Environment for Self-Evolving LLM Agents,https://arxiv.org/abs/2606.17546,external,arxiv.org,"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.",Gives readers a concrete source in SEAGym: An Evaluation Environment for Self-Evolving LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,983,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L983,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;budget,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.17546,[2606.17546] SEAGym: An Evaluation Environment for Self-Evolving LLM Agents,"Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batches, frozen update-validation, held-out ID and OOD transfer views, replay diagnostics, and saved snapshot and metric records. Instantiating SEAGym on Terminal-Bench 2.0 and HLE, we compare ACE, TF-GRPO, and AHE under a shared epoch/batch protocol. The results show that these evaluation views provide complementary signals about the evolution process: frequent updates may fail to improve held-out performance, useful intermediate snapshots may collapse later, and source diversity and model backend can affect harness reliability.",Congjie Zheng; Chuanyi Xue; Bin Liang; Jun Yang; Changshui Zhang,2026-06-16,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.17546,2026-07-17T05:43:07+00:00
+ale-0446,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions,https://arxiv.org/abs/2605.24110,external,arxiv.org,"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.",Gives readers a concrete source in EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,984,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L984,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.24110,[2605.24110] EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions,"Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assessment. This leaves out a basic question: can an agent keep its own codebase working as requirements change? We introduce EvoCode-Bench, a benchmark of 26 stateful coding tasks and 227 evaluated rounds. Each task preserves the agent's workspace for 5-15 rounds, states requirements through observable behavior, and uses cumulative executable tests to check new requirements and still-active prior ones. We evaluate 13 coding agents with two metrics: MT@4, a four-attempt fail-stop multi-round score, and SR, a single-round score from a reference-completed prior state. For most agents, SR exceeds MT@4 by 22-40 points. The gap also changes rankings: the highest-SR agent (78.9) ranks only third in persistent execution (44.0 MT@4). Even the strongest agents achieve only about 50% success on multi-turn metrics, and aggregate pass rate drops below half of round-1 performance by round 5. Failure analysis shows tier-dependent behavior: weaker agents fail early, while stronger agents survive long enough to expose specification-tracking and regression failures. We release the benchmark data and Harbor multi-turn infrastructure.",Haiyang Shen; Xuanzhong Chen; Wendong Xu; Yun Ma; Liang Chen; Kuan Li,2026-05-22,2026,arXiv,arXiv,,"Work in Progress; 32 pages, 10 figures, preprint",cs.AI,arxiv-api,,,,,,,2605.24110,2026-07-17T05:43:07+00:00
+ale-0447,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,On the Reliability of Computer Use Agents,https://arxiv.org/abs/2604.17849,external,arxiv.org,"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.",Gives readers a concrete source in On the Reliability of Computer Use Agents when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2604.17849; inspect its method and evaluation before treating results as production evidence.,medium,README.md,985,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L985,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2604.17849,[2604.17849] On the Reliability of Computer Use Agents,"Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task. This raises a fundamental question: if an agent can succeed at a task once, what prevents it from doing so reliably? In this work, we study the sources of unreliability in computer-use agents through three factors: stochasticity during execution, ambiguity in task specification, and variability in agent behavior. We analyze these factors on OSWorld using repeated executions of the same task together with paired statistical tests that capture task-level changes across settings. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions. These findings suggest the need to evaluate agents under repeated execution, to allow agents to resolve task ambiguity through interaction, and to favor strategies that remain stable across runs.",Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang,2026-04-20,2026,arXiv,arXiv,,"33 pages, 3 figures, 4 tables",cs.AI,arxiv-api,,,,,,,2604.17849,2026-07-17T05:43:07+00:00
+ale-0448,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation,https://arxiv.org/abs/2605.12925,external,arxiv.org,"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.",Gives readers a concrete source in AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2605.12925; inspect its method and evaluation before treating results as production evidence.,medium,README.md,986,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L986,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2605.12925,[2605.12925] AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation,"Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show that this equivalence is empirically false. We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks. Of these, 47 have enough passing trajectories to construct task-level process references, yielding a 1,815-trajectory evaluation subset. Among passing trajectories in this subset, 10.7% exhibit behavior we call a Lucky Pass: regression cycles, blind retries, missing verification, or temporally disordered exploration, implementation, and verification. We introduce AgentLens, a framework for process-level assessment of SWE-agent trajectories, and define AgentLens-Bench, a dataset of 1,815 trajectories annotated with quality scores, waste signals, divergence points, and 47 task-level Prefix Tree Acceptor (PTA) references. AgentLens builds PTA references by merging multiple passing solutions for the same task, and uses a context-sensitive intent labeler to assign actions to Exploration, Implementation, Verification, or Orchestration based on trajectory history rather than tool identity alone. On AgentLens-Bench, the quality score separates passing trajectories into Lucky, Solid, and Ideal tiers and further decomposes Lucky Passes into five recurring mechanisms. Across the eight model backends, Lucky rates range from 0.5% to 23.2%, and some models move by as many as five rank positions when ranked by quality score instead of pass rate. We plan to release the project repository soon, including AgentLens-Bench artifacts, the AgentLens SDK, and the analysis tooling.",Priyam Sahoo; Gaurav Mittal; Xiaomin Li; Shengjie Ma; Benjamin Steenhoek; Pingping Lin; Yu Hu,2026-05-13,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2605.12925,2026-07-17T05:43:07+00:00
+ale-0449,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction,https://arxiv.org/abs/2601.21008,external,arxiv.org,"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.",Gives readers a concrete source in ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,987,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L987,,Verify,verify,"Gate progress with tests, evals, and evidence.",trigger;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2601.21008,[2601.21008] ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research,"Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), identifying constraint conflicts, and repairing formulations until feasibility is restored. Existing LLM benchmarks mostly treat OR as one-shot translation from problem descriptions to solver code, omitting this diagnostic loop. We formalize infeasible-model repair as a solver-in-the-loop Markov Decision Process in which each action triggers solver re-execution and IIS recomputation, yielding deterministic, verifiable feedback. We introduce ORLoopBench, a benchmark suite with two components: OR-Debug-Bench releases 5,362 LP/MILP repair instances, while OR-Bias-Bench evaluates closed-form operational decision rationality across inventory settings. Solver-verified RLVR training enables an 8B model to surpass frontier APIs on LP repair (95.3% vs 92.4% RR @5), improves diagnostic behavior, and transfers to MILP repair. The same evaluation exposes semantic drift in whole-model code regeneration: feasible regenerated MILPs can solve the wrong problem. Process-level evaluation with solver oracles enables targeted training for reliable OR self-correction.",Ruicheng Ao; David Simchi-Levi; Xinshang Wang,2026-01-28,2026,arXiv,arXiv,,"58 pages, accepted by ICML 2026",cs.LG,arxiv-api,,,,,,,2601.21008,2026-07-17T05:43:07+00:00
+ale-0450,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis,https://arxiv.org/abs/2605.30434,external,arxiv.org,"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.",Gives readers a concrete source in LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,988,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L988,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.30434,[2605.30434] LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis,"Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability to track evolving analytical context over long horizons untested. We introduce LongDS, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states. LongDS comprises 68 tasks constructed from real-world Kaggle notebooks, spanning 2,225 turns across six domains including Geoscience, Business, and Education. Tasks are designed around state-evolution patterns (e.g., counterfactual perturbation, rollback, multi-state composition), with an average dependency span of 11.3 turns. Evaluating five state-of-the-art models, we find that the best model reaches only 48.45% average accuracy, performance drops nearly 47 points from early to late turns, and long-horizon errors account for 52%--69% of failures. Further analysis shows that additional agent steps do not necessarily improve performance, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget. We release LongDS to support research on reliable long-horizon agentic data analysis. Code and data will be released at https://github.com/zjunlp/DataMind.",Kewei Xu; Xiaoben Lu; Shuofei Qiao; Zihan Ding; Haoming Xu; Lei Liang; Ningyu Zhang,2026-05-28,2026,arXiv,arXiv,,Ongoing work,cs.LG,arxiv-api,,,,,,,2605.30434,2026-07-17T05:43:07+00:00
+ale-0451,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks,https://arxiv.org/abs/2602.16313,external,arxiv.org,"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.",Gives readers a concrete source in MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,989,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L989,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2602.16313,[2602.16313] MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks,"Existing evaluations of agents with memory typically assess memorization and action in isolation. One class of benchmarks evaluates memorization by testing recall of past conversations or text but fails to capture how memory is used to guide future decisions. Another class focuses on agents acting in single-session tasks without the need for long-term memory. However, in realistic settings, memorization and action are tightly coupled: agents acquire memory while interacting with the environment, and subsequently rely on that memory to solve future tasks. To capture this setting, we introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops. The benchmark consists of human-crafted agentic tasks with explicitly interdependent subtasks, where agents must learn from earlier actions and feedback by distilling experiences into memory, and subsequently use that memory to guide later actions to solve the overall task. MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning, and reveals that agents with near-saturated performance on existing long-context memory benchmarks like LoCoMo perform poorly in our agentic setting, exposing a gap in current evaluations for agents with memory.",Zexue He; Yu Wang; Churan Zhi; Yuanzhe Hu; Tzu-Ping Chen; Lang Yin; Ze Chen; Tong Arthur Wu; Siru Ouyang; Zihan Wang; Jiaxin Pei; Julian McAuley; Yejin Choi; Alex Pentland,2026-02-18,2026,arXiv,arXiv,,,cs.CL,arxiv-api,,,,,,,2602.16313,2026-07-17T05:43:07+00:00
+ale-0452,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations,https://arxiv.org/abs/2606.00832,external,arxiv.org,"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.",Gives readers a concrete source in Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,990,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L990,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;state;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2606.00832,[2606.00832] Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations,"Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmarks evaluate agents within a single session, ignoring past actions, stated preferences, and prior decisions that agents must integrate to fulfill personalized user goals. We introduce Momento, a benchmark for persistent agentic task completion in multi-session service environments, requiring agents to take consequential, tool-mediated actions while resolving temporal dependencies and evolving user goals across sessions. Experimental results reveal that current agents fail primarily through misestimation of user state, treating prior session history as a reliable proxy for current context rather than stale information requiring re-validation, highlighting a substantial gap between current agent capabilities and realistic long-horizon human-agent interaction.",Adril Putra Merin; David Anugraha; Ayu Purwarianti; Genta Indra Winata,2026-05-30,2026,arXiv,arXiv,,Preprint,cs.CL,arxiv-api,,,,,,,2606.00832,2026-07-17T05:43:07+00:00
+ale-0453,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows,https://arxiv.org/abs/2605.14678,external,arxiv.org,"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.",Gives readers a concrete source in π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,991,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L991,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification;exit,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2605.14678,[2605.14678] $π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows,"The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act on such hidden intents before they are explicitly stated, especially in sustained multi-turn interactions where user needs emerge gradually. To address this gap, we introduce $\pi$-Bench, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas. By incorporating hidden user intents, inter-task dependencies, and cross-session continuity, $\pi$-Bench evaluates agents' ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use. Experiments show (1) proactive assistance remains challenging, (2) a clear distinction between task completion and proactivity, and (3) the value of prior interaction for proactive intent resolution in later tasks.",Haoran Zhang; Luxin Xu; Zhilin Wang; Runquan Gui; Shunkai Zhang; Haodi Lei; Zihao He; Bingsu He; Chicheng Qin; Tong Zhu; Xiaoye Qu; Yang Yang; Yu Cheng; Yafu Li,2026-05-14,2026,arXiv,arXiv,,44 pages,cs.AI,arxiv-api,,,,,,,2605.14678,2026-07-17T05:43:07+00:00
+ale-0454,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation,https://arxiv.org/abs/2603.23638,external,arxiv.org,"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.",Gives readers a concrete source in Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,992,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L992,,Verify,verify,"Gate progress with tests, evals, and evidence.",state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.23638,[2603.23638] Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation in an Uncertain Enterprise Environment,"Large language model (LLM) agents are increasingly tested on complex tasks, but their ability to allocate scarce resources over long horizons remains unclear. Unlike reactive tasks with immediate feedback, this setting requires agents to make binding commitments under partial observability, delayed consequences, hard resource budgets, and shifting dynamics. We introduce EnterpriseArena, a 132-month CFO simulator that evaluates long-horizon resource allocation under uncertainty in a FinTech lending firm. Agents must manage liquidity, close books, gather costly signals, and request equity or debt financing across changing macroeconomic regimes. The simulator is built from transformed firm-level financial data, anonymized business documents, decade-scale macroeconomic and industry signals, and expert-validated operating rules. Experiments across 23 LLMs and four agent frameworks show that current agents remain far from robust: only 15.4% of trials survive the full horizon, larger models do not reliably outperform smaller ones, and failures cascade across observation, action timing, and capital sizing. These findings establish long-horizon resource allocation under uncertainty as a distinct capability gap for LLM agents.",Yi Han; Yan Wang; Lingfei Qian; Haohang Li; Yupeng Cao; Yueru He; Xueqing Peng; Nanhan Shen; Yitao Xu; Yankai Chen; Dongji Feng; Jimin Huang; Xue Liu; Jian-Yun Nie; Sophia Ananiadou,2026-03-24,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2603.23638,2026-07-17T05:43:07+00:00
+ale-0455,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer,https://arxiv.org/abs/2607.05202,external,arxiv.org,"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.",Gives readers a concrete source in EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,993,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L993,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.05202,[2607.05202] EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer,"Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmarks target information retention rather than procedural reuse. We introduce EvoAgentBench, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software engineering, and knowledge work. EvoAgentBench extracts trace-grounded Abilities from agent executions, canonicalizes them into operational units, and builds domain-specific Ability Graphs linking tasks that share procedural overlap. By design, every test task is backed by verified training-side Ability support. Across a 528/267 train/test split, two scaffolds, and three backbones, curated Ability content transfers reliably across model families, but no current automatic method sustains positive gain in all settings. EvoAgentBench shifts self-evolution evaluation from aggregate accuracy comparison to fine-grained diagnosis of experience encoding, routing, and uptake. The benchmark is publicly available at https://huggingface.co/datasets/EverMind-AI/EvoAgentBench.",Xingze Gao; Chuanrui Hu; Hongda Chen; Pengfei Yao; Zhao Wang; Yi Bai; Zhengwei Wu; Yunyun Han; Xiaofeng Cong; Jie Gui; Yafeng Deng; Teng Li,2026-07-06,2026,arXiv,arXiv,,"15 pages, 2 figures, 8 tables",cs.AI,arxiv-api,,,,,,,2607.05202,2026-07-17T05:43:07+00:00
+ale-0456,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents,https://arxiv.org/abs/2607.02255,external,arxiv.org,"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","Persistent memory is treated as an external runtime artifact. Resource-specific angle: Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.",Gives readers a concrete source in AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,994,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L994,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.02255,[2607.02255] AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents,"Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw cross-decision transcript appended. The prompt thus stays bounded across runs of any length, and any single layer can be ablated in isolation. We instantiate the contract in Slay the Spire 2, a closed-rule stochastic deck-building game whose runs require hundreds of tactical and strategic decisions. A public online benchmark of frontier LLMs on the same game reports zero wins at the lowest difficulty across five configurations, and the developer-reported human win rate at the same difficulty is 16%; the task is hard but not saturated. Within our harness, a fixed-A0 ablation shows the largest observed difference when triggered strategic skills are enabled: the no-store baseline wins 3/10 games and adding the skill layer 6/10. At this sample size the comparison is directional rather than statistically decisive (Fisher exact p\approx0.37); a cross-backbone probe and public accumulating-context baselines are reported as operational comparisons rather than controlled tests of the contract variable itself. We release a reproducible testbed: 298 completed trajectories with condition tags, frozen memory/skill snapshots, prompt records, and analysis scripts -- an agent design and a validated, reusable methodology for studying how explicit memory layers shape long-horizon LLM-agent decisions.",Xiangchen Cheng; Yunwei Jiang; Jianwen Sun; Zizhen Li; Chuanhao Li; Xiangcheng Cao; Yihao Liu; Fanrui Zhang; Li Jin; Kaipeng Zhang,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02255,2026-07-17T05:43:07+00:00
+ale-0457,Benchmarks And Evaluation,benchmarks-and-evaluation,Paper,📄,Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops,https://arxiv.org/abs/2607.05197,external,arxiv.org,"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.",Gives readers a concrete source in Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops when they need to measure progress and gate completion with repeatable evidence.,Research source arXiv:2607.05197; inspect its method and evaluation before treating results as production evidence.,medium,README.md,995,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L995,,Verify,verify,"Gate progress with tests, evals, and evidence.",delegation;verification;budget,researcher;evaluator,research-preprint,ok,https://arxiv.org/abs/2607.05197,[2607.05197] Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops,"Iterative repair loops have become a core design pattern in LLM-based software engineering systems. These workflows repeatedly generate, validate, and repair artifacts using feedback such as compiler errors or test failures. Despite their widespread use, the impact of repair-loop iteration limits remains poorly understood, as most prior work adopts fixed, often arbitrary, repair budgets. We study repair-loop effectiveness across multiple software engineering tasks, including code generation, test generation, and code translation. Across several representative workflows, datasets, and contemporary low-cost LLMs, we observe a consistent pattern of diminishing returns: the first three to four repair iterations account for most achievable gains, while later iterations contribute only marginal improvements. We further find that repair behavior is influenced more strongly by workflow orchestration and feedback design than by the underlying model itself. These results suggest that repair budgets should be treated as an explicit experimental variable, as they directly affect evaluation outcomes, computational cost, runtime, and reproducibility in LLM-based software engineering research.",Tobias Kiecker; Eik Reichmann; Hosung Kang; Gabin An; Lars Grunske,2026-07-06,2026,arXiv,arXiv,,"4 Pages (+1 for references), NIER Paper",cs.SE,arxiv-api,,,,,,,2607.05197,2026-07-17T05:43:07+00:00
+ale-0458,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,"DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks",https://arxiv.org/abs/2607.07946,external,arxiv.org,"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: 113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","Gives readers a concrete source in DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks when they need to measure progress and gate completion with repeatable evidence.",Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,996,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L996,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.07946,"[2607.07946] DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks","DeepSWE is a benchmark of 113 original, long-horizon software engineering tasks for evaluating coding agents. Most public agentic coding benchmarks follow SWE-bench in mining merged fixes from public GitHub repositories, which creates two problems: the fixes and their discussion were likely seen during pretraining, so a high score can reflect recall rather than problem-solving; and each task is graded by the tests that shipped with its merged fix, which were written to confirm one specific fix rather than grade an arbitrary solution, so they can fail a correct alternative or pass an incomplete one. DeepSWE avoids both. Its tasks are written from scratch across 91 active open-source repositories and five languages and are never contributed back upstream, so their reference solutions stay out of the public record that model training scrapes; and each task is graded by a hand-written verifier that checks the requested functionality and accepts any implementation that provides it. When an independent LLM judge re-reviews graded runs, it disagrees with DeepSWE's verifier about an order of magnitude less often than with SWE-Bench Pro's inherited tests (1.4% versus 32.4%). Despite being about half the length of SWE-Bench Pro's prompts, DeepSWE's prompts describe tasks whose reference solutions touch 5.5x more code, and the benchmark separates frontier agents across a wider score band than the leaderboards on which they otherwise cluster. We release the benchmark, its verifiers, and the full record of evaluation trajectories.",Wenqi Huang; Charley Lee; Leonard Tng; Serena Ge,2026-07-08,2026,arXiv,arXiv,,"32 pages, 10 figures. Code and data: https://github.com/datacurve-ai/deep-swe ; https://deepswe.datacurve.ai/",cs.SE,arxiv-api,,,,,,,2607.07946,2026-07-17T05:43:07+00:00
+ale-0459,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization,https://arxiv.org/abs/2607.07744,external,arxiv.org,"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.",Gives readers a concrete source in PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,997,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L997,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.07744,[2607.07744] PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization,"Coding-agent benchmarks have largely measured whether agents can produce functionally correct patches, but production software also demands measurable speedups on real execution targets. Performance optimization is a distinct agentic task: agents must profile executions, diagnose cross-layer bottlenecks, edit code without breaking correctness, and verify that gains are reproducible rather than measurement artifacts. We introduce PERFOPT-Bench, a benchmark for evaluating this full performance-engineering loop. Each task provides a correct but deliberately suboptimal codebase and asks the agent to improve a target performance metric; scoring requires hidden correctness tests, verified-speedup measurement, and trajectory-level audit. We evaluate 7 agent stacks with different LLMs and agent frameworks on 7 long-horizon optimization tasks. The results show that optimization performance is workload-dependent rather than determined by model identity alone: no single stack dominates, and changing the agent framework can materially change the same LLM's per-task speedup profile. We further find that raw speedup is unsafe as a benchmark score, since some large gains arise from benchmark-specific shortcut exploitation; an exploratory relay pilot suggests that restarting from an externalized optimization summary can recover additional headroom after an initial session stops. The benchmark and our evaluation are available at: https://anonymous.4open.science/r/Dataset-D3CC.",Yingyun Cui; Yi Xie; Piaohong Wang; Jiawei Ma; Bo Liu; Liangliang Cao,2026-07-08,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.07744,2026-07-17T05:43:07+00:00
+ale-0460,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Benchmarking coding agents on Databricks' multi-million line codebase,https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase,external,www.databricks.com,"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.",Gives readers a concrete source in Benchmarking coding agents on Databricks' multi-million line codebase when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,998,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L998,,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification;budget,researcher;evaluator,benchmark,ok,https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase,Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase | Databricks Blog,"Databricks shares results from its internal coding benchmark, evaluating coding agents on a multi-million line codebase to optimize engineering cost and performance.",,2026,2026,,Databricks,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0461,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks,https://arxiv.org/abs/2607.08768,external,arxiv.org,"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.",Gives readers a concrete source in UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,999,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L999,,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;context;verification;state;escalation,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.08768,[2607.08768] UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks,"The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they often rely on sandboxed environments and single-turn evaluation paradigms. Moreover, their scenario-based task taxonomies mix multiple model capabilities within the same task category, making it difficult to identify the root causes of agent failures. To address these limitations, we introduce UniClawBench, the first capability-driven benchmark designed to evaluate proactive agents in dynamic, real-world settings. UniClawBench is built around five foundational model capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination. Based on these capabilities, we design 400 bilingual real-world tasks. Unlike previous benchmarks that rely on static, pre-recorded answers, our benchmark evaluates agents in live Docker containers using fine-grained, step-by-step completion checkpoints. Furthermore, we design a closed-loop evaluation strategy comprising an executor agent, a hidden supervisor agent, and a user agent to simulate realistic multi-turn human feedback without leaking grading criteria. To disentangle base model capabilities from framework-level design choices, we evaluate state-of-the-art models under multiple agent frameworks. Through comprehensive comparisons across both models and frameworks, we show how base model capabilities and agent framework designs jointly shape performance in real-world environments. To facilitate future research, we make our benchmark and code publicly available at https://github.com/HKU-MMLab/UniClawBench.",Zhekai Chen; Chengqi Duan; Kaiyue Sun; Bohao Li; Yuqing Wang; Manyuan Zhang; Xihui Liu,2026-07-09,2026,arXiv,arXiv,,Project Page: https://uniclawbench.github.io | GitHub Repo: https://github.com/HKU-MMLab/UniClawBench,cs.CL,arxiv-api,,,,,,,2607.08768,2026-07-17T05:43:07+00:00
+ale-0462,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills,https://arxiv.org/abs/2607.09016,external,arxiv.org,"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.",Gives readers a concrete source in SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1000,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1000,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.09016,[2607.09016] SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills,"Agent skills extend LLM agents with reusable procedures, tools, and domain-specific workflows, but their safety depends on resolving dependencies among interacting instructions. We introduce SkillLogic, a framework for analyzing logical relations in skill files and constructing executable tests from them. Our taxonomy covers eight relation types, including preconditions that gate valid actions, constraints that limit how allowed actions may be performed, and fallbacks that specify recovery behavior after failure. Using SkillLogic, we scan over 5000 public skills and find that 70% contain at least one logical relation. We then construct SLBench, an 86-case executable benchmark from high-confidence, high-impact, and locally testable relations. Evaluating Codex and Claude Code across six LLM backbones shows unsafe rates up to 70%, with violations leading to privacy leaks, unsafe configuration changes, and incomplete cleanup. The human audit attributes failures to both agent capability gaps and low-salience skill text. We further show that SLGuard, a lightweight inference-time scaffold, reduces violations by 63% on targeted cases. Our results establish logical-relation following as a distinct reliability challenge for skill-guided agents.",Xuan Chen; Chengpeng Wang; Lu Yan; Xiangyu Zhang,2026-07-10,2026,arXiv,arXiv,,,cs.CR,arxiv-api,,,,,,,2607.09016,2026-07-17T05:43:07+00:00
+ale-0463,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution,https://arxiv.org/abs/2603.13428,external,arxiv.org,"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.",Gives readers a concrete source in SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1001,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1001,,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2603.13428,[2603.13428] SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution,"Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-running systems, are now entrusted to drive this evolution. Yet, existing benchmarks evaluate agents on isolated, one-off coding tasks, neglecting the temporal dependencies and technical debt inherent in real-world software evolution. To bridge this gap, we introduce DeepCommit, an agentic pipeline that reconstructs verifiable Milestone DAGs from noisy commit logs, where milestones are defined as functionally cohesive development goals. These executable sequences enable SWE-Milestone, a benchmark that evaluates agents on streams of milestone-level tasks, requiring them to sustain system integrity and limit error accumulation, dimensions of long-term software evolution largely missing from current benchmarks. Our evaluation of 12 frontier models across 4 agent frameworks reveals a critical vulnerability: overall performance scores drop significantly from >80% on isolated tasks to at most 38% in continuous settings, exposing agents' profound struggle with long-term maintenance and error propagation.",Gangda Deng; Zhaoling Chen; Zhongming Yu; Haoyang Fan; Yuhong Liu; Yuxin Yang; Dhruv Parikh; Rajgopal Kannan; Le Cong; Mengdi Wang; Qian Zhang; Viktor Prasanna; Xiangru Tang; Xingyao Wang,2026-03-13,2026,arXiv,arXiv,,ICML 2026,cs.SE,arxiv-api,,,,,,,2603.13428,2026-07-17T05:43:07+00:00
+ale-0464,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,AgentAbstain: Do LLM Agents Know When Not to Act?,https://arxiv.org/abs/2607.10059,external,arxiv.org,"Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark measuring whether agents correctly abstain from acting when a task is underspecified, unsafe, or impossible, rather than proceeding anyway, a capability every unattended loop depends on for safe exits.",Gives readers a concrete source in AgentAbstain: Do LLM Agents Know When Not to Act? when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1002,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1002,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.10059,[2607.10059] AgentAbstain: Do LLM Agents Know When Not to Act?,"Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain. This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions. To close this gap, we present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act. At its core, AgentAbstain is a paired-task benchmark built on an agent-native taxonomy of 8 abstention scenarios across pre-execution reasoning and runtime discovery. It contains 263 paired tasks across 42 executable sandbox environments, where each pair consists of a should-act task and a should-abstain variant produced through a controlled perturbation to the instruction, tool, or environment state. To scale this paired design and resist data contamination, we propose AbstainGen, a fully automated pipeline that synthesizes sandbox environments and generates paired tasks end-to-end, validated by deterministic replay and semantic LLM judges; fresh task instances can be regenerated on demand, and three independent annotators rate 94-98% of sampled tasks as well-designed. Across 17 frontier LLMs in 4 agent harnesses, the best agent (Gemini 3.1 Pro) achieves only 59.5% paired accuracy (correct on both the act and abstain sides of each paired task). More importantly, abstention capability is largely independent of general task-solving capability, indicating that scaling task-solving alone will not close this gap. We further identify failure modes such as post-hoc abstention, in which agents execute irreversible actions before recognizing abstention triggers. Our code and dataset are open-sourced at agentabstain.github.io.",Xun Liu; Yi Evie Zhang; Vira Kasprova; Parisa Rabbani; Pardis Sadat Zahraei; Tianyu Zhang; Ali Ebrahimpour-Boroojeny; Varun Chandrasekaran,2026-07-11,2026,arXiv,arXiv,,"56 pages, 13 figures",cs.AI,arxiv-api,,,,,,,2607.10059,2026-07-17T05:43:07+00:00
+ale-0465,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,"Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy",https://arxiv.org/abs/2607.10526,external,arxiv.org,"Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark showing that once a stateful personal agent is nudged into a sycophantic stance, it persists across later sessions through memory, so single-turn sycophancy tests understate the risk in long-running agents.","Gives readers a concrete source in Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy when they need to measure progress and gate completion with repeatable evidence.",Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1003,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1003,2026-07-15,Verify,verify,"Gate progress with tests, evals, and evidence.",context;verification;state,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.10526,"[2607.10526] Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Stateful Personal Agents","Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills. This persistence turns conversational sycophancy into a state-writing failure: accepted user-centric claims can be committed as lasting preferences, background facts, or workflows and later reused after the original conversation is gone. We call this persistent sycophancy and introduce the Personal Agent Sycophancy Benchmark (PASB), a 1,600-task benchmark that traces whether a conversational claim is accepted, written into durable agent state, and reused in a later neutral query. Unlike prior benchmarks that provide pre-written memories, PASB evaluates real agents (Hermes-Agent and OpenClaw) that decide what to store. It isolates the write process by combining four scenario framings with four temporal delivery patterns and separating a five-turn persist stage from a cleared three-turn query stage, ensuring downstream effects arise only from durable state. Across twelve models, the commit boundary is the key inflection point: downstream failure increases from 45.0% in session-only episodes to 71.9% after commitment, a consistent increase of 27.0 percentage points. Committed claims exhibit three write-time patterns: status promotion, attribution removal, and scope broadening. These patterns become stronger under memory-like or procedural framing, repeated reinforcement, and even across domain boundaries. These results show that agent sycophancy is fundamentally a state-writing governance problem. Once user content is committed to durable memory, safety must govern what agents write, not only what they say. PASB identifies the write-time controls needed to gate risky commits while preserving the source, role, and scope of stored content beyond response-level mitigations.",Xutao Mao; Liangjie Zhao; Leyao Wang; Rui Qian; Qiang Huang; Wentao Wang; Bo Han; Xiang Zheng; Cong Wang,2026-07-12,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.10526,2026-07-17T05:43:07+00:00
+ale-0466,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,Set-shifting Behavioral Test for Harnessed Agents,https://arxiv.org/abs/2607.13396,external,arxiv.org,"Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Tests whether harnessed agents adapt when hidden tool reliability changes, paired with no-shift controls that separate genuine adaptation failures from ordinary task errors and expose routine lock-in.",Gives readers a concrete source in Set-shifting Behavioral Test for Harnessed Agents when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1004,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1004,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.13396,[2607.13396] Set-shifting Behavioral Test for Harnessed Agents,"What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmark mounts tool-skill libraries with redundancies, where many tools solve the same task but differ in hidden reliability. In our evaluation framework, a branched schedule shifts the reliable tool group at hidden boundaries and pairs every shift with a no-shift control. We find that agents, by default, settle on a small recurring routine within a few turns of each boundary, with call shares concentrating on a few discrete values after each reliability shift. We score the set-shifting accuracy for each agent trajectory: the joint probability of routing to the target tool group in every post-shift window. We test open-weight LLMs in an open-source agentic harness and find qualitatively distinct failure modes across the same set of routines. We also find that set framing, how the toolset presents the alternatives as competing or complementary, shifts the routing dynamics.",Ziwei Ye,2026-07-15,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.13396,2026-07-17T05:43:07+00:00
+ale-0467,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,https://arxiv.org/abs/2607.14642,external,arxiv.org,"Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.","Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Applies 11 server-evolution mutations to 123 MCP servers and evaluates 12 LLMs; reported performance drops include 13.7% for GPT-5.4 and 14.4% for Claude Sonnet 4.6, quantifying tool-interface drift.",Gives readers a concrete source in MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1005,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1005,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",workspace,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.14642,[2607.14642] MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,"As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities. However, these benchmarks overlook the continuous evolution of tool interfaces and functionalities within MCP servers, resulting in flawed assessments that fail to capture the agent's adaptability in changing tool landscapes. To bridge this gap, we introduce \textbf{MCPEvol-Bench}, a novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution. Inspired by large-scale empirical study, we propose 11 mutation operators to simulate realistic tool evolution within 123 MCP servers. We benchmark 12 state-of-the-art LLMs on multiple versions of MCP servers, revealing that even frontier models struggle to adapt to evolving tools. For instance, GPT-5.4 and Claude-Sonnet-4-6 exhibit performance declines of 13.7\% and 14.4\% in evolved MCP servers, respectively, accompanied by substantial increases in planning and reasoning errors. These findings highlight the vulnerability of LLM-driven workflows, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.",Huanxi Liu; Kun Hu; Jiaqi Liao; Qiang Wang; Pengfei Qian; YuanZhao Zhai; Dawei Feng; Bo Ding; Huaimin Wang,2026-07-16,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.14642,2026-07-17T05:43:07+00:00
+ale-0468,Benchmarks And Evaluation,benchmarks-and-evaluation,Benchmark,🧪,MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,https://arxiv.org/abs/2607.15205,external,arxiv.org,"Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.","The work turns loop quality into a measurable task or score. Resource-specific angle: Provides 652 issue-PR instances across 23 languages, seven image categories, and four relevance levels; the strongest evaluated agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, leaving substantial room for visual-evidence-aware repair loops.",Gives readers a concrete source in MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization when they need to measure progress and gate completion with repeatable evidence.,Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.,high,README.md,1006,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1006,2026-07-17,Verify,verify,"Gate progress with tests, evals, and evidence.",intake;verification,researcher;evaluator,benchmark,ok,https://arxiv.org/abs/2607.15205,[2607.15205] MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,"Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated mostly as a text-only task. Existing multimodal SE benchmarks evaluate end-to-end repair, entangling localization with patch synthesis and obscuring whether visual input helped, hurt, or was ignored. We introduce \textbf{MM-IssueLoc}, a controlled benchmark and evaluation protocol for repository-level localization with visual evidence. MM-IssueLoc contains 652 issue-PR instances across 23 languages, with annotations for 7 image categories and 4 relevance levels. It provides file-level and function-level gold labels, paired text-only and with-image evaluation, and VCE-based diagnostics that convert images into structured textual evidence. We evaluate LLM-based and retrieval-based systems, including MM-IssueLoc-VL-Emb as a controlled multimodal retriever. Results show that existing systems remain far from reliable multimodal repository localization: the strongest agent reaches 38.96 file Acc@5 and 22.45 function Acc@10, while the strongest retriever reaches 33.86 function Acc@10. Cross-benchmark comparisons show that high localization scores on text-dominant SWE benchmarks do not transfer cleanly to multimodal issue localization. MM-IssueLoc turns visual evidence into an explicit evaluation variable, enabling future work to test whether systems improve by using visual evidence for localization, rather than by relying on text-only cues or downstream patch-generation effects.",Shaoxiong Zhan; Shi Hu; Boyu Feng; Hai Lin; Andrew Gong; Zhengda Zhou; Jiaying Zhou; Yunyun Hou; Hao Su; Hai-Tao Zheng,2026-07-16,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.15205,2026-07-17T05:43:07+00:00
+ale-0469,Operations Playbooks,operations-playbooks,Blog,📝,Agentic Engineering: The Agent Loop,https://junpingyi.com/books/agentic-engineering/agent-loop/,external,junpingyi.com,Minimal mental model for the loop underlying agent operation.,Minimal mental model for the loop underlying agent operation.,Minimal mental model for the loop underlying agent operation.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Minimal mental model for the loop underlying agent operation.,Gives readers a concrete source in Agentic Engineering: The Agent Loop when they need to bound risk before recurring or unattended execution.,"Contextual source from junpingyi.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1014,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1014,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://junpingyi.com/books/agentic-engineering/agent-loop/,Chapter 1: The Agent Loop — Agentic Engineering: How to Build AI Agents Like Claude Code,Chapter 1: The Agent Loop from Agentic Engineering: How to Build AI Agents Like Claude Code,,,,,junpingyi.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0470,Operations Playbooks,operations-playbooks,Blog,📝,"The agent loop: ReAct, plan-and-execute, reflection",https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection,external,www.kunwar.page,Practical walkthrough of the base loop and common variants.,Practical walkthrough of the base loop and common variants.,Practical walkthrough of the base loop and common variants.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Practical walkthrough of the base loop and common variants.,"Gives readers a concrete source in The agent loop: ReAct, plan-and-execute, reflection when they need to bound risk before recurring or unattended execution.","Contextual source from www.kunwar.page; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1015,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1015,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://www.kunwar.page/chapter/067-the-agent-loop-react-plan-and-execute-reflection,"Chapter 67: The agent loop: ReAct, plan-and-execute, reflection — The Holy Grail Basic agent loop: generate, check for tool calls, execute tools and loop back, or return final answer on no tool call. ReAct interleaves Thought, Action, and Observation triplets; each Thought improves the next Action choice by externalizing reasoning. Agent cost vs single-shot: one LLM call versus 5-12 interleaved LLM and tool calls, showing the 10x cost and latency multiplier.",An agent is a loop of `model.generate()` calls with tool calls in between. The loop is the entire pattern,,,,,kunwar.page,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0471,Operations Playbooks,operations-playbooks,Blog,📝,How to Build an Agent,https://ampcode.com/how-to-build-an-agent,external,ampcode.com,"Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Thorsten Ball's demystification of the inner agent loop: a model, a loop, and enough tokens.",Gives readers a concrete source in How to Build an Agent when they need to bound risk before recurring or unattended execution.,"Contextual source from ampcode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1016,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1016,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,operator;security,practitioner-analysis,ok,https://ampcode.com/notes/how-to-build-an-agent,Amp,"Building a fully functional, code-editing agent in less than 400 lines.",,,,,ampcode.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0472,Operations Playbooks,operations-playbooks,Blog,📝,Agentic Coding Recommendations,https://lucumr.pocoo.org/2025/6/12/agentic-coding/,external,lucumr.pocoo.org,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Armin Ronacher's field notes on which practices hold up when agents do most of the work.,Gives readers a concrete source in Agentic Coding Recommendations when they need to bound risk before recurring or unattended execution.,"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1017,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1017,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://lucumr.pocoo.org/2025/6/12/agentic-coding/,Agentic Coding Recommendations | Armin Ronacher's Thoughts and Writings,Current recommendations of agentic coding.,,2025-06-12,2025,,Armin Ronacher's Thoughts and Writings,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0473,Operations Playbooks,operations-playbooks,Blog,📝,Coding Agents 101: The Art of Actually Getting Things Done,https://devin.ai/agents101,external,devin.ai,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Practical delegation guidance from the Devin team on scoping tasks agents can actually finish.,Gives readers a concrete source in Coding Agents 101: The Art of Actually Getting Things Done when they need to bound risk before recurring or unattended execution.,"Contextual source from devin.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1018,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1018,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;exit,operator;security,practitioner-analysis,ok,https://devin.ai/agents101,Coding Agents 101: The Art of Actually Getting Things Done,Coding Agents 101: The Art of Actually Getting Things Done,,,,,devin.ai,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0474,Operations Playbooks,operations-playbooks,Blog,📝,How Anthropic teams use Claude Code,https://claude.com/blog/how-anthropic-teams-use-claude-code,external,claude.com,"Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Cross-team field report of real recurring agent workflows in engineering, security, and data science.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Cross-team field report of real recurring agent workflows in engineering, security, and data science.",Gives readers a concrete source in How Anthropic teams use Claude Code when they need to bound risk before recurring or unattended execution.,"Contextual source from claude.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1019,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1019,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,operator;security,practitioner-analysis,ok,https://claude.com/blog/how-anthropic-teams-use-claude-code,How Anthropic teams use Claude Code | Claude by Anthropic,Teams across Anthropic use Claude Code for everything from debugging production issues and navigating unfamiliar codebases to building custom automation tools. Here's how. ‍,,,,,Claude,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0475,Operations Playbooks,operations-playbooks,Blog,📝,How Boris Uses Claude Code,https://howborisusesclaudecode.com/,external,howborisusesclaudecode.com,"Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.","Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Unofficial but concrete compilation of Boris Cherny's autonomous setups: parallel worktrees, auto mode, `/loop`, `/schedule`, dynamic workflows, and `/goal` completion conditions.",Gives readers a concrete source in How Boris Uses Claude Code when they need to bound risk before recurring or unattended execution.,"Contextual source from howborisusesclaudecode.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1020,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1020,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;trigger;workspace;exit,operator;security,practitioner-analysis,ok,https://howborisusesclaudecode.com/,Boris Cherny's Claude Code Tips — How He Actually Uses It (121+ Tips),"121+ tips from Boris Cherny, creator of Claude Code, on his daily workflow: CLAUDE.md, worktrees, plan mode, hooks, subagents, and more.",@CarolinaCherry,,,,How Boris Uses Claude Code,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0476,Operations Playbooks,operations-playbooks,Blog,📝,Agent of the Day: Copilot Agent PR Analysis,https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/,external,github.github.com,"Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.","Primary-source operational guidance rather than commentary. Resource-specific angle: Official walkthrough of a daily scheduled agentic workflow that ingests PR data, analyzes it, and publishes findings to a Discussion, a concrete recurring loop with trigger, intake, analysis, and output.",Gives readers a concrete source in Agent of the Day: Copilot Agent PR Analysis when they need to bound risk before recurring or unattended execution.,"Contextual source from github.github.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1021,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1021,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake,operator;security,practitioner-analysis,ok,https://github.github.com/gh-aw/blog/2026-05-26-agent-of-the-day/,"Agent of the Day – May 26, 2026 | GitHub Agentic Workflows",Copilot Agent PR Analysis: a daily workflow that monitors GitHub Copilot coding agent performance across pull requests,,,2026,,GitHub Agentic Workflows,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0477,Operations Playbooks,operations-playbooks,Paper,📄,"Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows",https://arxiv.org/abs/2607.07052,external,arxiv.org,"Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Production lifecycle in which repeatedly validated agent-loop behaviors are promoted into deterministic workflows and demoted on regression, cutting per-incident agent cost by over 70% across eight months of a cloud AIOps system.","Gives readers a concrete source in Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows when they need to bound risk before recurring or unattended execution.",Research source arXiv:2607.07052; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1022,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1022,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07052,"[2607.07052] Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production","AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.",Arun Malik,2026-07-08,2026,arXiv,arXiv,,"Conference-style paper; 10 pages (estimated from manuscript formatting if applicable); focuses on agentic AI, AIOps, workflow automation, deterministic execution, and LLM cost optimization",cs.SE,arxiv-api,,,,,,,2607.07052,2026-07-17T05:43:07+00:00
+ale-0478,Operations Playbooks,operations-playbooks,Paper,📄,Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,https://arxiv.org/abs/2607.08010,external,arxiv.org,"Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Production tool-making pipeline that mines live execution traces to compile recurring SOP steps into validated, versioned tools agents call instead of regenerating code, cutting median latency 42% and errors up to 53% in a fulfillment-center alarm-triage deployment.",Gives readers a concrete source in Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.08010; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1023,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1023,,Govern,govern,"Bound permissions, cost, failure, and escalation.",intake;workspace,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.08010,[2607.08010] Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,"Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment. The tool-maker grounds synthesis in the live environment as it collects execution traces, observes backend schemas and values, generates candidate tools, and repairs them against labeled cases. At runtime, the production agent calls these tools directly and falls back to code generation only when needed. We deploy the approach in a Fulfillment Center alarm-triage system, where an agent diagnoses alarms against a 44-node SOP over heterogeneous metric backends. In production, tool calls reduce p50 latency by 42%. On 1,500 historical alarms, they reduce end-to-end error rate by up to 53% by suppressing run-to-run variance in repeated steps. Because tools return compact structured verdicts, they also enable a simpler direct-call architecture, reducing p50 latency by a further 62% in a controlled ablation. Versioned tools also improve auditability and expose specification gaps and upstream data drift. Our results show that self-evolving agents can make industrial LLM systems faster, more reliable, and easier to operate.",Kalle Kujanpää; Ning Liu; Shahnawaz Alam; Yeshwanth Reddy Sura; Tianyu Yang; Kristina Klinkner; Shervin Malmasi,2026-07-09,2026,arXiv,arXiv,,Preprint,cs.CL,arxiv-api,,,,,,,2607.08010,2026-07-17T05:43:07+00:00
+ale-0479,Operations Playbooks,operations-playbooks,Blog,📝,AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines,https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines,external,www.sabrina.dev,"Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Sabrina Ramonov's practitioner walkthrough of building verified loops with Claude Code `/goal` and cloud routines: a six-part loop-engineering framework, five worked examples each with a verifiable end state, and a production daily support-ticket cleanup routine whose independent checker agents illustrate why the checker is the hard part.",Gives readers a concrete source in AI Loop Engineering: Build Autonomous Agents with Claude Code /goal and Routines when they need to bound risk before recurring or unattended execution.,"Contextual source from www.sabrina.dev; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1024,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1024,,Govern,govern,"Bound permissions, cost, failure, and escalation.",objective;verification;state,operator;security,practitioner-analysis,ok,https://www.sabrina.dev/p/loop-engineering-claude-code-goal-routines,AI Loop Engineering: Build Autonomous Agents with Claude Code /goal + Routines,"What loop engineering means in 2026, how to use the Claude Code /goal command, and how to build your first autonomous AI agent with a routine.",Sabrina Ramonov 🍄,,,,sabrina.dev,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0480,Operations Playbooks,operations-playbooks,Paper,📄,Agent Delivery Engineering Predictive Reliability Framework,https://arxiv.org/abs/2607.07689,external,arxiv.org,"Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Proactive health-trajectory prediction for long-horizon multi-agent systems, aggregating 20 heterogeneous signals across five layers into a trust-margin metric with 8-hour forecasts at 76.8% direction accuracy, detecting degradation concealed by normal surface metrics across 15 days of production traffic.",Gives readers a concrete source in Agent Delivery Engineering Predictive Reliability Framework when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07689; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1025,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1025,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07689,[2607.07689] Agent Delivery Engineering Predictive Reliability Framework,"Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory prediction from passive degradation detection. ADE-PRF aggregates 20 heterogeneous signals across five layers into a Trust Margin (TM) metric (39.2-point dynamic range). Triple-method parallel prediction enables 8-hour forecasts: the Exponential method achieves MAE=1.228, Direction Accuracy=76.8%, with 99.65% within +/-10-point tolerance. Production validation spans 380,227 predictions and 280,579 validations across six agent profiles over 15 continuous days, plus seven sandbox-controlled experiments. Key findings include detection of ""false prosperity"" -- degradation concealed by normal surface metrics -- and immediate TM coupling with ground-truth states upon ADE plugin integration, with 16/20 factors relying on ADE-collected data. Exponential consistently outperforms Kalman. ADE-PRF provides among the earliest reliability quantification with forward-looking warnings for production LLM agents.",Dexing Liu,2026-07-08,2026,arXiv,arXiv,,"117pages,83figures",cs.MA,arxiv-api,,,,,,,2607.07689,2026-07-17T05:43:07+00:00
+ale-0481,Operations Playbooks,operations-playbooks,Tool,🧰,rocketplaneIO,https://github.com/olemeyer/rocketplaneIO,external,github.com,"Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Self-hosted AI SRE for Kubernetes whose copilot investigates autonomously via eBPF traces, logs, and live service maps, but can only act through named, reversible, risk-graded operations.",Gives readers a concrete source in rocketplaneIO when they need to bound risk before recurring or unattended execution.,"Inspectable GitHub source (141 stars; 3 forks; Apache-2.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1026,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1026,,Govern,govern,"Bound permissions, cost, failure, and escalation.",trigger;intake;budget;escalation;exit,builder;operator;security,source-implementation,ok,https://github.com/olemeyer/rocketplaneIO,"GitHub - olemeyer/rocketplaneIO: Self-hosted AI SRE for Kubernetes — zero-instrumentation eBPF observability plus a copilot that fixes issues through guardrailed, self-verifying actions. BYO-LLM, air-gapped capable. · GitHub","Self-hosted AI SRE for Kubernetes — zero-instrumentation eBPF observability plus a copilot that fixes issues through guardrailed, self-verifying actions. BYO-LLM, air-gapped capable. - olemeyer/rocketplaneIO",,2026-07-06,2026,olemeyer/rocketplaneIO,GitHub,,,,github-api,olemeyer/rocketplaneIO,141,3,Apache-2.0,2026-07-06T11:24:01Z,2026-07-15T11:30:27Z,,2026-07-17T05:43:07+00:00
+ale-0482,Operations Playbooks,operations-playbooks,Blog,📝,Migrating a Production AI Agent to GPT-5.6,https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6,external,ploy.ai,"Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Lorenzo Gentile's July 2026 case study of swapping the model inside Ploy's production website-building agent (plans pages, writes components, screenshots its own work, decides when done): roughly a third of initial cross-model eval failures traced to Opus-specific harness assumptions rather than the new model, GPT-5.6 invented placeholder values for optional tool parameters (fixed via required-but-nullable schemas, after 52-64% of file reads silently returned empty), cache keys needed workspace scoping (0% to 83.7% first-call hits), and reasoning state moved to self-contained encrypted blobs, concrete evidence that eval harnesses and regression gates are the load-bearing layer when changing the model inside a production loop.",Gives readers a concrete source in Migrating a Production AI Agent to GPT-5.6 when they need to bound risk before recurring or unattended execution.,"Contextual source from ploy.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1027,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1027,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;state;exit,operator;security,practitioner-analysis,ok,https://ploy.ai/blog/migrating-a-production-ai-agent-to-gpt-5-6,Migrating a production AI agent to GPT-5.6 | Ploy,"For four months, no frontier model beat Claude Opus in our production evals. GPT-5.6 did. This is what we learned while migrating.",,,,,Ploy,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0483,Operations Playbooks,operations-playbooks,Paper,📄,Coding-agents can replicate scientific machine learning papers,https://arxiv.org/abs/2607.02134,external,arxiv.org,"Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.","Translates agent-loop ideas into operator-facing workflows for repeated delegated work. Resource-specific angle: Runs coding agents independently 12 times across four scientific machine-learning papers; all workspaces satisfy the study's completion criteria, and 158 implementation targets are linked to report evidence, offering a concrete playbook for evidence-backed research replication.",Gives readers a concrete source in Coding-agents can replicate scientific machine learning papers when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.02134; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1028,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1028,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.02134,[2607.02134] Coding-agents can replicate scientific machine learning papers,"Scientific machine learning papers typically make computational claims, e.g., that the relative mean square error is less than 5% or that the 95% predictive credible interval covers the test data. A coding agent can be prompted to replicate those claims from paper materials alone, but the prompt does not by itself reliably preserve progress or check whether generated evidence supports the paper's claims. We introduce Paper-replication, a workflow that makes each selected paper claim a target with recorded evidence, and implement it as a coding-agent skill. The workflow makes the agent record those targets, reconstruct the paper's method, run computational experiments, link generated outputs to provenance and comparisons with the paper's claims, record where matched evidence appears in the replication report, and pass validation checks before completion. We evaluate Paper-replication on twelve independent runs across four scientific machine learning papers. All twelve workspaces pass the completion gate, and all 158 recorded targets are matched with report coverage. Even in this completed workspace state, repeated runs differ in how papers are divided into targets, in numerical fidelity to the source papers, in elapsed replication time, in the number of intermediate executions replaced before final evidence is accepted, and in the rules used to accept evidence. Paper-replication makes completion depend on workspace evidence and validation checks rather than on the agent's final message.",Atharva Hans; Ilias Bilionis,2026-07-02,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.02134,2026-07-17T05:43:07+00:00
+ale-0484,Templates And Patterns,templates-and-patterns,Template,🧾,Resource entry template,templates/resource-entry.md,local_path,,Format for adding a single resource with evidence quality and category fit.,Format for adding a single resource with evidence quality and category fit.,Format for adding a single resource with evidence quality and category fit.,The resource is directly reusable as a starting artifact. Resource-specific angle: Format for adding a single resource with evidence quality and category fit.,Gives readers a concrete source in Resource entry template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1036,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1036,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/resource-entry.md,Resource entry template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0485,Templates And Patterns,templates-and-patterns,Template,🧾,Loop pattern template,templates/loop-pattern.md,local_path,,"Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.","The resource is directly reusable as a starting artifact. Resource-specific angle: Template for documenting an operational loop such as PR babysitting, CI repair, or feedback clustering.",Gives readers a concrete source in Loop pattern template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1037,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1037,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/templates/loop-pattern.md,Loop pattern template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0486,Templates And Patterns,templates-and-patterns,Template,🧾,Loop contract schema,schemas/loop-contract.schema.json,local_path,,Machine-readable schema for portable loop specs.,Machine-readable schema for portable loop specs.,Machine-readable schema for portable loop specs.,The contribution is machine-readable and validation-friendly. Resource-specific angle: Machine-readable schema for portable loop specs.,Gives readers a concrete source in Loop contract schema when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1038,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1038,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/schemas/loop-contract.schema.json,Loop contract schema,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0487,Templates And Patterns,templates-and-patterns,Template,🧾,Loop contract preview script,scripts/preview_loop_contract.py,local_path,,Dependency-free demo that validates and renders a loop contract JSON file.,Dependency-free demo that validates and renders a loop contract JSON file.,Dependency-free demo that validates and renders a loop contract JSON file.,The contribution is machine-readable and validation-friendly. Resource-specific angle: Dependency-free demo that validates and renders a loop contract JSON file.,Gives readers a concrete source in Loop contract preview script when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1039,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1039,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/scripts/preview_loop_contract.py,Loop contract preview script,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0488,Templates And Patterns,templates-and-patterns,Template,🧾,Translation guide,TRANSLATIONS.md,local_path,,How to add or maintain a language translation without drifting from the canonical English list.,How to add or maintain a language translation without drifting from the canonical English list.,How to add or maintain a language translation without drifting from the canonical English list.,The resource is directly reusable as a starting artifact. Resource-specific angle: How to add or maintain a language translation without drifting from the canonical English list.,Gives readers a concrete source in Translation guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1040,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1040,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/TRANSLATIONS.md,Translation guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0489,Templates And Patterns,templates-and-patterns,Template,🧾,Pattern library index,patterns/README.md,local_path,,"Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Practical loop patterns with triggers, state, verification gates, budgets, and escalation paths.",Gives readers a concrete source in Pattern library index when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1041,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1041,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;verification;state;budget;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/patterns/README.md,Pattern library index,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0490,Examples And Schema,examples-and-schema,Pattern,🔁,Example loop specs,examples/README.md,local_path,,"Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.","Repository-native artifact that makes an otherwise informal practice concrete and reusable. Resource-specific angle: Human-readable walkthroughs for PR babysitting, CI repair, and docs drift collection.",Gives readers a concrete source in Example loop specs when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1051,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1051,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md,Example loop specs,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0491,Examples And Schema,examples-and-schema,Template,🧾,Loop contract library,examples/README.md#contract-library,local_path,,"Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.","The contribution is machine-readable and validation-friendly. Resource-specific angle: Schema-validated loop contracts for every pattern-library loop, from PR babysitting to model routing.",Gives readers a concrete source in Loop contract library when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1052,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1052,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md#contract-library,Loop contract library,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0492,Examples And Schema,examples-and-schema,Template,🧾,Runnable test-repair loop,examples/runnable/test-repair-loop.sh,local_path,,"Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.","Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Dependency-light reference loop script with a verification gate, retry budget, durable progress log, repeat-failure detection, and escalation exit.",Gives readers a concrete source in Runnable test-repair loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1053,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1053,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",verification;budget;escalation;exit,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/runnable/test-repair-loop.sh,Runnable test-repair loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0493,Examples And Schema,examples-and-schema,Template,🧾,Runnable loop guide,examples/runnable/README.md,local_path,,"Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.","The resource is directly reusable as a starting artifact. Resource-specific angle: Maps the script line by line to the Loop Contract and shows how to drive it with Claude Code, Codex CLI, or any agent CLI.",Gives readers a concrete source in Runnable loop guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1054,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1054,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/runnable/README.md,Runnable loop guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0494,Community Gallery,community-gallery,Template,🧾,Loop gallery guide,gallery/README.md,local_path,,Quality bar for contributed loop examples with receipts and lessons learned.,Quality bar for contributed loop examples with receipts and lessons learned.,Quality bar for contributed loop examples with receipts and lessons learned.,The resource is directly reusable as a starting artifact. Resource-specific angle: Quality bar for contributed loop examples with receipts and lessons learned.,Gives readers a concrete source in Loop gallery guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1070,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1070,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",state,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/README.md,Loop gallery guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0495,Community Gallery,community-gallery,Template,🧾,Loop gallery template,gallery/template.md,local_path,,"Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Markdown template for sharing a loop's trigger, intake, state, verification, escalation, and safety notes.",Gives readers a concrete source in Loop gallery template when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1071,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1071,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;intake;verification;state;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/template.md,Loop gallery template,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0496,Community Gallery,community-gallery,Pattern,🔁,PR babysitter reference loop,gallery/pr-babysitter-reference.md,local_path,,Reference gallery entry for keeping a pull request moving.,Reference gallery entry for keeping a pull request moving.,Reference gallery entry for keeping a pull request moving.,Turns loop adoption into shareable cases with enough structure to compare lessons learned. Resource-specific angle: Reference gallery entry for keeping a pull request moving.,Gives readers a concrete source in PR babysitter reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1072,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1072,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/pr-babysitter-reference.md,PR babysitter reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0497,Community Gallery,community-gallery,Pattern,🔁,CI repair reference loop,gallery/ci-repair-reference.md,local_path,,Reference gallery entry for turning failing CI into a verified patch or escalation.,Reference gallery entry for turning failing CI into a verified patch or escalation.,Reference gallery entry for turning failing CI into a verified patch or escalation.,Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reference gallery entry for turning failing CI into a verified patch or escalation.,Gives readers a concrete source in CI repair reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1073,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1073,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",verification;escalation,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/ci-repair-reference.md,CI repair reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0498,Community Gallery,community-gallery,Pattern,🔁,Docs drift reference loop,gallery/docs-drift-reference.md,local_path,,Reference gallery entry for recurring docs/code consistency checks.,Reference gallery entry for recurring docs/code consistency checks.,Reference gallery entry for recurring docs/code consistency checks.,Turns loop adoption into shareable cases with enough structure to compare lessons learned. Resource-specific angle: Reference gallery entry for recurring docs/code consistency checks.,Gives readers a concrete source in Docs drift reference loop when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1074,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1074,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder;operator,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/docs-drift-reference.md,Docs drift reference loop,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0499,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Most Developers Do Not Need Agent Loops Yet,https://alphasignalai.substack.com/p/most-developers-do-not-need-agent,external,alphasignalai.substack.com,"Useful caution against adopting loops before the task, signal, and economics justify them.","Useful caution against adopting loops before the task, signal, and economics justify them.","Useful caution against adopting loops before the task, signal, and economics justify them.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Useful caution against adopting loops before the task, signal, and economics justify them.",Gives readers a concrete source in Most Developers Do Not Need Agent Loops Yet when they need to bound risk before recurring or unattended execution.,"Contextual source from alphasignalai.substack.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1082,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1082,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,operator;security,risk-analysis,ok,https://alphasignalai.substack.com/p/most-developers-do-not-need-agent,Most Developers Do Not Need Agent Loops Yet,"The patterns were documented in 2024. Here’s who it pays off for, and the four conditions that decide.",AlphaSignal AI,,,,Substack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0500,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Engineering Agentic Systems for Reliability,https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/,external,pruningmypothos.com,"Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.",Gives readers a concrete source in Engineering Agentic Systems for Reliability when they need to bound risk before recurring or unattended execution.,"Contextual source from pruningmypothos.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1083,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1083,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;verification;escalation,operator;security,risk-analysis,ok,https://pruningmypothos.com/systems/engineering-agentic-systems-for-reliability/,Engineering Agentic Systems for Reliability | Sans Serif Systems,"A practical reliability model for agentic systems built around governed steps, verification, escalation, and observability.",Shailesh Rawat,,,,Sans Serif Systems,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0501,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared",https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026,external,callsphere.ai,Compares self-correction patterns and their cost/failure tradeoffs.,Compares self-correction patterns and their cost/failure tradeoffs.,Compares self-correction patterns and their cost/failure tradeoffs.,"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Compares self-correction patterns and their cost/failure tradeoffs.","Gives readers a concrete source in Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared when they need to bound risk before recurring or unattended execution.","Contextual source from callsphere.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1084,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1084,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,operator;security,risk-analysis,ok,https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026,"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared | CallSphere Blog","Three self-correction patterns dominate 2026 agent design. Side-by-side analysis of where each one wins, where each one fails, and how to combine them.",CallSphere,2026-04-24,2026,,CallSphere,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0502,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,How to Build an AI Agent Harness: A 2026 Complete Guide,https://atlan.com/know/how-to-build-ai-agent-harness/,external,atlan.com,"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.",Gives readers a concrete source in How to Build an AI Agent Harness: A 2026 Complete Guide when they need to bound risk before recurring or unattended execution.,"Contextual source from atlan.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1085,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1085,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification,operator;security,risk-analysis,ok,https://atlan.com/know/how-to-build-ai-agent-harness/,How to Build an AI Agent Harness: Step-by-Step Tutorial (2026),"Most agent harnesses fail at the data layer, not the loop. Build one the right way in 10 steps, with code and a done test for each. Start at Step 0.",,,,,atlan.com,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0503,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Harness Engineering vs Prompt Engineering vs Context Engineering Explained,https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d,external,medium.com,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.,Gives readers a concrete source in Harness Engineering vs Prompt Engineering vs Context Engineering Explained when they need to bound risk before recurring or unattended execution.,"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1086,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1086,,Govern,govern,"Bound permissions, cost, failure, and escalation.",context,operator;security,risk-analysis,restricted,https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d,,,,,,,Medium,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0504,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,https://arxiv.org/abs/2606.17799,external,arxiv.org,"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","The work turns loop quality into a measurable task or score. Resource-specific angle: Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.",Gives readers a concrete source in Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.17799; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1087,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1087,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.17799,[2606.17799] Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,"Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration. We argue that current coding benchmarks are misaligned with agentic software engineering. A coding agent in practice is not a model: it is a system harness -- a composite of models, harnesses, contexts, environments, and feedback signals, any one of which can move the benchmark score by margins comparable to those between adjacent model generations. We discuss three symptoms: (i) benchmark scores conflate the model with the rest of the harness; (ii) grading against a single reference solution penalises equally valid alternatives; and (iii) the absence of signal at the level of individual harness components makes the end-to-end system score difficult to iterate on.",Maria I. Gorinova; Macey Baker; Amy Heineike; Maksim Shaposhnikov; Rob Willoughby; Dru Knox,2026-06-16,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2606.17799,2026-07-17T05:43:07+00:00
+ale-0505,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Understanding the Challenges in Iterative Generative Optimization with LLMs,https://arxiv.org/abs/2603.23994,external,arxiv.org,"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.",Gives readers a concrete source in Understanding the Challenges in Iterative Generative Optimization with LLMs when they need to bound risk before recurring or unattended execution.,Research source arXiv:2603.23994; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1088,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1088,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2603.23994,[2603.23994] Understanding the Challenges in Iterative Generative Optimization with LLMs,"Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the ""right"" learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Different starting artifacts determine which solutions are reachable in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches do not monotonically improve generalization on BBEH. We conclude that the lack of a simple, universal way to set up learning loops across domains is a major hurdle for productionization and adoption. We provide practical guidance for making these choices.",Allen Nie; Xavier Daull; Zhiyi Kuang; Abhinav Akkiraju; Anish Chaudhuri; Max Piasevoli; Ryan Rong; YuCheng Yuan; Prerit Choudhary; Shannon Xiao; Rasool Fakoor; Adith Swaminathan; Ching-An Cheng,2026-03-25,2026,arXiv,arXiv,,"39 pages, 17 figures",cs.LG,arxiv-api,,,,,,,2603.23994,2026-07-17T05:43:07+00:00
+ale-0506,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Illusion of Multi-Agent Advantage,https://arxiv.org/abs/2606.13003,external,arxiv.org,"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.",Gives readers a concrete source in The Illusion of Multi-Agent Advantage when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.13003; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1089,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1089,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;verification,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.13003,[2606.13003] The Illusion of Multi-Agent Advantage,"Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed decision-making. However, empirical support for this claim relies primarily on comparisons with SAS baselines using benchmarks that prioritize isolated reasoning tasks, which do not adequately assess these advantages. Focusing on automatically generated MAS that are designed for enhanced generalizability over manually-designed counterparts, we perform a rigorous, systematic evaluation against SAS, specifically Chain-of-Thought with Self-Consistency (CoT-SC). Across traditional reasoning datasets and tasks with interactive multi-step workflows (e.g., BrowseComp-Plus), we demonstrate that automatic MAS consistently underperform CoT-SC despite being up to 10x more expensive. To isolate these failures from limitations inherent to task structure, we introduce a diagnostic synthetic dataset tailored for MAS featuring explicit task decomposition, context separation and parallelization potential. We show that expert-architected MAS consistently outperforms automatically generated architectures in both raw performance and cost-efficiency on this dataset, demonstrating that existing evaluation frameworks mask critical architectural gaps and inefficiencies of complex MAS by failing to account for the marginal utility of increased computational cost. Critically, systematic deconstruction of the generated MAS architectures reveals that current automated design paradigms produce architectural bloat that prioritizes superficial complexity which does not translate into functional utility, exposing a fundamental misalignment with multi-agent principles.",Prathyusha Jwalapuram; Hehai Lin; Chuyuan Li; Fangkai Jiao; Sudong Wang; Yifei Ming; Zixuan Ke; Chengwei Qin; Giuseppe Carenini; Shafiq Joty,2026-06-11,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2606.13003,2026-07-17T05:43:07+00:00
+ale-0507,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,The Coming Loop,https://lucumr.pocoo.org/2026/6/23/the-coming-loop/,external,lucumr.pocoo.org,"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.",Gives readers a concrete source in The Coming Loop when they need to bound risk before recurring or unattended execution.,"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1090,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1090,,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation;exit,operator;security,risk-analysis,ok,https://lucumr.pocoo.org/2026/6/23/the-coming-loop/,The Coming Loop | Armin Ronacher's Thoughts and Writings,"Loops, harnesses, and why even loop skeptics may end up with them.",,2026-06-23,2026,,Armin Ronacher's Thoughts and Writings,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0508,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,"Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop",https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735,external,www.theregister.com,"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","Gives readers a concrete source in Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop when they need to bound risk before recurring or unattended execution.","Contextual source from www.theregister.com; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1091,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1091,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget,operator;security,risk-analysis,ok,https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735,"Loop engineering, latest AI buzzword, still needs humans in the loop",Prompting less and automating more comes with a price,,2026-06-24,2026,,theregister,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0509,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents,https://arxiv.org/abs/2607.01641,external,arxiv.org,"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.",Gives readers a concrete source in When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.01641; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1092,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1092,,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;delegation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.01641,[2607.01641] When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents,"LLM agents increasingly rely on iterative execution to solve tasks through planning, tool use, state updates, and agent collaboration. While this design enables flexible automation, it also creates a new class of failures: an agent may repeatedly execute model calls, tools, workflow transitions, or agent handoffs when the feedback path is not effectively bounded. We call this problem Infinite Agentic Loops (IALs). IALs are not ordinary programming loops; they arise from the interaction between agent logic, framework semantics, runtime observations, and termination mechanisms. Such failures can amplify a single request into long running model and tool execution, causing cost exhaustion, model denial of service, context growth, and repeated external side effects. We propose IAL-Scan, a static analysis tool for detecting IAL failures in real-world LLM agent projects. IAL-Scan abstracts heterogeneous agent code into a framework independent Agent IR, builds an Agentic Loop Dependence Graph (ALDG) to recover explicit and framework induced feedback paths, and checks whether these paths can repeatedly reach costly or state growing operations without an effective bound. We evaluate IAL-Scan on 6,549 LLM agent repositories. It reports 74 potential findings, among which manual review confirms 68 IAL failures across 47 projects, achieving 91.9% precision.",Xinyi Hou; Shenao Wang; Yanjie Zhao; Haoyu Wang,2026-07-02,2026,arXiv,arXiv,,,cs.SE,arxiv-api,,,,,,,2607.01641,2026-07-17T05:43:07+00:00
+ale-0510,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents,https://arxiv.org/abs/2607.07436,external,arxiv.org,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.,"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.",Gives readers a concrete source in The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07436; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1093,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1093,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07436,[2607.07436] The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents,"A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased reward, which is false for the LLM judges that reference-free tasks force upon us. We show that a biased judge does not merely add noise; it \emph{silently switches off the curator}. We make this precise with a corrupted-reward analysis and, isolating the causal channel by injecting corruption on top of a deterministic reward, a behavioral study on a reference-free report-writing testbed with a code-generation cross-check. Symmetric noise leaves retirement intact, but \emph{false-pass} bias (failures slipping through as passes) disables contribution-based retirement past a sharp threshold that no amount of data can cross. Separating genuine retirement from cap-eviction churn shows this \emph{mechanism} failure is universal, holding across domains and failure rates and sparing only near-zero-false-pass, verifier-like graders. The downstream \emph{outcome}, though, is regime-dependent: eval quality degrades only where the same corruption also starves skill synthesis, and otherwise holds steady, so the disabled curator is \emph{silent}, surfacing in no aggregate metric. The contribution is a behavioral safety result, not a performance one. A cheap defect-injection audit then tells an operator, before deployment, which side of the threshold their judge occupies.",Xing Zhang; Yanwei Cui; Guanghui Wang; Ziyuan Li; Wei Qiu; Bing Zhu; Peiyang He,2026-07-08,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.07436,2026-07-17T05:43:07+00:00
+ale-0511,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows,https://arxiv.org/abs/2607.07504,external,arxiv.org,"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.",Gives readers a concrete source in Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.07504; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1094,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1094,,Govern,govern,"Bound permissions, cost, failure, and escalation.",budget;escalation;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.07504,[2607.07504] Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows,"Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results. Reusable skill files are meant to avoid prompting from scratch by packaging guidance for a task family. Expert-written skills can encode high-quality guidance, but writing and maintaining them across many data-science task families creates a manual bottleneck. We ask whether LLM-generated skills offer a useful low-curation alternative: do they improve performance over the task prompt alone? We test this question across four lifecycle stages: data preparation, data extraction, statistical analysis, and reporting, using one generated skill per stage. We find no reliable improvement from full generated skills over No-Skill prompting. We then ask whether any part of the skill is useful by ablating different skill components. The main ablation covers 56 tasks, nine model configurations, and three providers, yielding 7,560 runs. Compared with prompting using the task alone, neither the full generated skill nor any ablated skill variant significantly improves performance; all p-values are at least 0.396, and the total spread across variants is only 1.2 pp. A supplemental token-matched control adds 1,512 runs and finds that Full skills perform similarly to task-irrelevant skill-formatted content. The results caution against using one LLM-generated skill per data-science workflow as a default single-shot prompting strategy.",Wei-Jung Huang,2026-07-08,2026,KDD 2026 Workshop on AI Data Scientist,arXiv,,KDD 2026 Workshop on AI Data Scientist,cs.AI,arxiv-api,,,,,,,2607.07504,2026-07-17T05:43:07+00:00
+ale-0512,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,The Verification Horizon: No Silver Bullet for Coding Agent Rewards,https://arxiv.org/abs/2606.26300,external,arxiv.org,"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.",Gives readers a concrete source in The Verification Horizon: No Silver Bullet for Coding Agent Rewards when they need to bound risk before recurring or unattended execution.,Research source arXiv:2606.26300; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1095,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1095,,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2606.26300,[2606.26300] The Verification Horizon: No Silver Bullet for Coding Agent Rewards,"A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult -- reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent -- manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions -- scalability, faithfulness, and robustness -- and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world agent tasks, and an automated agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks. These experiences collectively point to a core observation: no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator.",Binghai Wang; Chenlong Zhang; Dayiheng Liu; Jiajun Zhang; Jiawei Chen; Mingze Li; Mouxiang Chen; Rongyao Fang; Siyuan Zhang; Xuwu Wang; Yuheng Jing; Zeyao Ma; Zeyu Cui,2026-06-24,2026,arXiv,arXiv,,Authors are listed alphabetically by their first names,cs.AI,arxiv-api,,,,,,,2606.26300,2026-07-17T05:43:07+00:00
+ale-0513,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Critique,⚠️,Write Code Like a Human Will Maintain It,https://unstack.io/write-code-like-a-human-will-maintain-it,external,unstack.io,"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.",Gives readers a concrete source in Write Code Like a Human Will Maintain It when they need to bound risk before recurring or unattended execution.,"Contextual source from unstack.io; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1096,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1096,,Govern,govern,"Bound permissions, cost, failure, and escalation.",escalation,operator;security,risk-analysis,ok,https://unstack.io/write-code-like-a-human-will-maintain-it,Write code like a human will maintain it,"One of the best things about LLMs is that they'll write code for you, all day long. Who cares about DRY? You don't have to be the one updating the same long con...",,2026-07-10,2026,,Unstack,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0514,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Blog,📝,Claude Code Sends 33k Tokens Before Reading the Prompt,https://systima.ai/blog/claude-code-vs-opencode-token-overhead,external,systima.ai,"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.","The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. Directly quantifies the per-iteration economics that compound across recurring loops, with transparent methodology of identical-outcome tasks and a logging proxy capturing exact request payloads.",Gives readers a concrete source in Claude Code Sends 33k Tokens Before Reading the Prompt when they need to bound risk before recurring or unattended execution.,"Contextual source from systima.ai; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1097,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1097,,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;budget,operator;security,practitioner-analysis,ok,https://systima.ai/blog/claude-code-vs-opencode-token-overhead,Claude Code Sends 4.7x More Tokens Than OpenCode Before Reading Your Prompt | Systima Blog,"Claude Code vs OpenCode token overhead measured at the API boundary. Out-of-the-box baselines, instruction file weight, MCP schema tax, subagent multipliers, and cache-write behaviour.",Systima,2026-07-12,2026,,Systima,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0515,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Rethinking the Evaluation of Harness Evolution for Agents,https://arxiv.org/abs/2607.12227,external,arxiv.org,"Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Re-evaluates harness evolution on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6, finding that evolved harnesses do not consistently beat budget-matched search and transfer only weakly to held-out tasks.",Gives readers a concrete source in Rethinking the Evaluation of Harness Evolution for Agents when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.12227; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1098,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1098,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.12227,[2607.12227] Rethinking the Evaluation of Harness Evolution for Agents,"We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. To address these concerns, we conduct an extensive evaluation comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluate evolved harnesses on held-out tasks to assess whether the discovered improvements generalize. Experiments on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6 show that automatic harness evolution does not consistently outperform simple test-time scaling methods and exhibits limited generalization. Our results raise important questions about the effectiveness of automatic harness evolution and highlight the need for fairer evaluation protocols and benchmarks for automatic harness design. Our code is available at https://github.com/rethinking-harness-evolution.",Yike Wang; Huaisheng Zhu; Zhengyu Hu; Yige Yuan; Zhengyu Chen; Shakti Senthil; Hannaneh Hajishirzi; Yulia Tsvetkov; Pradeep Dasigi; Teng Xiao,2026-07-14,2026,arXiv,arXiv,,,cs.AI,arxiv-api,,,,,,,2607.12227,2026-07-17T05:43:07+00:00
+ale-0516,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes,https://arxiv.org/abs/2607.13071,external,arxiv.org,"Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.",Gives readers a concrete source in Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.13071; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1099,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1099,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",workspace;context;verification;state;exit,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.13071,[2607.13071] Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes,"Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth. This paper documents a failure mode in Claude Code where partial standard output from timed-out commands (exit code 143) is recorded in compaction summaries as confirmed results, propagating false positives across sessions and model versions without re-verification. The underlying mechanism is a conflation of observation and persistence, where information that appeared in the terminal is treated as equivalent to information written to durable storage. This finding extends the analysis of LLM self-evaluation failures reported in prior work on non-determinism in LLM-as-judge grading by showing that agentic tools exhibit analogous reliability deficits when reporting on their own operational outcomes. The failure has direct implications for any workflow that relies on agentic session continuity for data processing, scientific computation, or multi-step automation.",Hiroki Tamba,2026-07-11,2026,arXiv,arXiv,,"8 pages, companion to arXiv:2606.26185",cs.SE,arxiv-api,,,,,,,2607.13071,2026-07-17T05:43:07+00:00
+ale-0517,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0,https://arxiv.org/abs/2607.14004,external,arxiv.org,"Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.",Gives readers a concrete source in Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14004; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1100,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1100,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",verification;budget,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14004,[2607.14004] Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0,"Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimization is applied recursively as new failures and new tasks appear over time. The central question this raises is whether optimizer-driven gains compound: after an agent has been optimized once, can it be optimized again on newly arrived tasks without eroding the gains the first round produced? We study this question with a two-phase continual-learning evaluation built from hard tasks in Terminal-Bench 2.0, comparing three approaches to agent-harness optimization (GEPA, Meta Harness, and RELAI's Verifiable Continual Learning, RELAI-VCL) under identical optimization budgets. All three methods improve over the baseline agent in the conventional, static, single-phase setting. However, once new tasks are introduced, the methods diverge sharply: GEPA's optimized agent transfers below the unoptimized baseline, Meta Harness transfers well but fails to improve further once given a second optimization budget, and RELAI-VCL is the only method that both transfers positively to unseen tasks and continues improving after those tasks are folded into the optimization objective, reaching the highest pass rate at every evaluated stage and the highest lifelong average pass rate overall (76.4% vs. 66.0% for GEPA, 64.6% for Meta Harness, and 58.7% for the baseline). Our key observation was that optimization gains compounded only when regression control was built into the optimization loop, providing an inductive bias against shortcut solutions that fail to generalize.",Wenxiao Wang; Priyatham Kattakinda; Soheil Feizi,2026-07-15,2026,arXiv,arXiv,,Technical Report by RELAI (relai.ai),cs.AI,arxiv-api,,,,,,,2607.14004,2026-07-17T05:43:07+00:00
+ale-0518,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Paper,📄,Does Multi-Agent Debate Improve AI Feedback on Research Papers?,https://arxiv.org/abs/2607.14713,external,arxiv.org,"In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.",Gives readers a concrete source in Does Multi-Agent Debate Improve AI Feedback on Research Papers? when they need to bound risk before recurring or unattended execution.,Research source arXiv:2607.14713; inspect its method and evaluation before treating results as production evidence.,medium,README.md,1101,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1101,2026-07-17,Govern,govern,"Bound permissions, cost, failure, and escalation.",delegation;verification;budget;escalation,researcher;evaluator;operator;security,research-preprint,ok,https://arxiv.org/abs/2607.14713,[2607.14713] Does Multi-Agent Debate Improve AI Feedback on Research Papers?,"Probably not, at least for meta-analyses in economics. In a pre-registered, identity-masked, within-paper experiment, the authors of 44 meta-analyses ranked three AI reports on their own paper by usefulness for improving it: a single pass by a frontier model against two multi-agent debate tools we built and expected to win. All reports were held to a common length and template. The authors preferred the single pass, by 0.66 rank points over mad-research (95% CI 0.32 to 1.00) and 0.57 over paper-workshop (0.16 to 0.95), though paper-workshop spent roughly thirty times the tokens. Authors who recalled their journal referee report usually placed it first and never last; in a separate exercise, three AI judges almost always placed the real journal referee report last. Among the three AI reports, Gemini (the judge whose model family wrote none of the reports) would have ranked paper-workshop first in the authors' place, reversing the single-pass preference. The reversal warns against substituting an AI judge for the author. We measure perceived usefulness for finished papers; whether AI should referee papers is a separate question.",Tomas Havranek; Zuzana Irsova,2026-07-16,2026,arXiv,arXiv,,"29 pages, 1 figure, 6 tables. Pre-registered on OSF; data, code, judge prompts, and blinded reports in the replication package on Zenodo. Project page: https://meta-analysis.cz/debate",econ.GN,arxiv-api,,,,,,,2607.14713,2026-07-17T05:43:07+00:00
+ale-0519,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Harness Engineering by ai-boost,https://github.com/ai-boost/awesome-harness-engineering,external,github.com,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Comprehensive list for the agent harness layer that Loop Engineering builds on.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Comprehensive list for the agent harness layer that Loop Engineering builds on.,Gives readers a concrete source in Awesome Harness Engineering by ai-boost when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,104 stars; 333 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1109,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1109,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/ai-boost/awesome-harness-engineering,"GitHub - ai-boost/awesome-harness-engineering: Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. · GitHub","Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. - ai-boost/awesome-harness-engineering",,2026-03-29,2026,ai-boost/awesome-harness-engineering,GitHub,,,,github-api,ai-boost/awesome-harness-engineering,3104,333,NOASSERTION,2026-03-29T15:39:49Z,2026-07-17T04:40:20Z,,2026-07-17T05:43:07+00:00
+ale-0520,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Harness Engineering by walkinglabs,https://github.com/walkinglabs/awesome-harness-engineering,external,github.com,"High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.",Gives readers a concrete source in Awesome Harness Engineering by walkinglabs when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,635 stars; 294 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1110,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1110,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/walkinglabs/awesome-harness-engineering,GitHub - walkinglabs/awesome-harness-engineering: 🛠️ Awesome tools & guides for harness engineering. · GitHub,🛠️ Awesome tools & guides for harness engineering. - walkinglabs/awesome-harness-engineering,,2026-03-29,2026,walkinglabs/awesome-harness-engineering,GitHub,,,,github-api,walkinglabs/awesome-harness-engineering,3635,294,NOASSERTION,2026-03-29T11:29:37Z,2026-07-17T02:25:47Z,,2026-07-17T05:43:07+00:00
+ale-0521,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Agent Harness,https://github.com/AutoJunjie/awesome-agent-harness,external,github.com,"Curated tools and resources for environments, constraints, and feedback around coding agents.","Curated tools and resources for environments, constraints, and feedback around coding agents.","Curated tools and resources for environments, constraints, and feedback around coding agents.","Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Curated tools and resources for environments, constraints, and feedback around coding agents.",Gives readers a concrete source in Awesome Agent Harness when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (484 stars; 46 forks; updated 2026-07-14); popularity is context, not proof of reliability.",medium,README.md,1111,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1111,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",workspace,builder,curated-index,ok,https://github.com/AutoJunjie/awesome-agent-harness,GitHub - AutoJunjie/awesome-agent-harness · GitHub,Contribute to AutoJunjie/awesome-agent-harness development by creating an account on GitHub.,,2026-03-05,2026,AutoJunjie/awesome-agent-harness,GitHub,,,,github-api,AutoJunjie/awesome-agent-harness,484,46,,2026-03-05T13:19:10Z,2026-07-14T10:24:06Z,,2026-07-17T05:43:07+00:00
+ale-0522,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Context Engineering,https://github.com/Meirtz/Awesome-Context-Engineering,external,github.com,Survey-style list for context engineering across LLMs and agents.,Survey-style list for context engineering across LLMs and agents.,Survey-style list for context engineering across LLMs and agents.,Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Survey-style list for context engineering across LLMs and agents.,Gives readers a concrete source in Awesome Context Engineering when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,238 stars; 258 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1112,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1112,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context,builder,curated-index,ok,https://github.com/Meirtz/Awesome-Context-Engineering,"GitHub - Meirtz/Awesome-Context-Engineering: 🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. · GitHub","🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. - Meirtz/Awesome-Context-Engineering",,2025-07-02,2025,Meirtz/Awesome-Context-Engineering,GitHub,,,,github-api,Meirtz/Awesome-Context-Engineering,3238,258,MIT,2025-07-02T17:46:03Z,2026-07-17T01:18:38Z,,2026-07-17T05:43:07+00:00
+ale-0523,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Prompt Engineering,https://github.com/promptslab/Awesome-Prompt-Engineering,external,github.com,Classic adjacent list for prompt techniques and prompting resources.,Classic adjacent list for prompt techniques and prompting resources.,Classic adjacent list for prompt techniques and prompting resources.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Classic adjacent list for prompt techniques and prompting resources.,Gives readers a concrete source in Awesome Prompt Engineering when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (6,170 stars; 728 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.",medium,README.md,1113,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1113,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/promptslab/Awesome-Prompt-Engineering,"GitHub - promptslab/Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc · GitHub","This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc - GitHub - promptslab/Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc",,2023-02-09,2023,promptslab/Awesome-Prompt-Engineering,GitHub,,,,github-api,promptslab/Awesome-Prompt-Engineering,6170,728,Apache-2.0,2023-02-09T18:22:52Z,2026-07-16T21:53:00Z,,2026-07-17T05:43:07+00:00
+ale-0524,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome LLM Agents,https://github.com/kaushikb11/awesome-llm-agents,external,github.com,"General list of LLM agent papers, frameworks, and applications.","General list of LLM agent papers, frameworks, and applications.","General list of LLM agent papers, frameworks, and applications.","Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: General list of LLM agent papers, frameworks, and applications.",Gives readers a concrete source in Awesome LLM Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,533 stars; 329 forks; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1114,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1114,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/kaushikb11/awesome-llm-agents,GitHub - kaushikb11/awesome-llm-agents: A curated list of awesome LLM agents frameworks. · GitHub,A curated list of awesome LLM agents frameworks. Contribute to kaushikb11/awesome-llm-agents development by creating an account on GitHub.,,2023-04-04,2023,kaushikb11/awesome-llm-agents,GitHub,,,,github-api,kaushikb11/awesome-llm-agents,1533,329,,2023-04-04T10:22:43Z,2026-07-15T09:52:09Z,,2026-07-17T05:43:07+00:00
+ale-0525,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome AI Agents,https://github.com/e2b-dev/awesome-ai-agents,external,github.com,Broad AI agent ecosystem map.,Broad AI agent ecosystem map.,Broad AI agent ecosystem map.,Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Resource-specific angle: Broad AI agent ecosystem map.,Gives readers a concrete source in Awesome AI Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28,826 stars; 3,173 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1115,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1115,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/e2b-dev/awesome-ai-agents,GitHub - e2b-dev/awesome-ai-agents: A list of AI autonomous agents · GitHub,A list of AI autonomous agents. Contribute to e2b-dev/awesome-ai-agents development by creating an account on GitHub.,,2023-06-19,2023,e2b-dev/awesome-ai-agents,GitHub,,,,github-api,e2b-dev/awesome-ai-agents,28826,3173,NOASSERTION,2023-06-19T00:20:06Z,2026-07-17T05:44:47Z,,2026-07-17T05:43:07+00:00
+ale-0526,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome CLI Coding Agents,https://github.com/bradAGI/awesome-cli-coding-agents,external,github.com,"Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.","Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.",Gives readers a concrete source in Awesome CLI Coding Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (819 stars; 219 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1116,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1116,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",delegation,builder,curated-index,ok,https://github.com/bradAGI/awesome-cli-coding-agents,"GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. · GitHub","Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. - GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure.",,2026-02-07,2026,bradAGI/awesome-cli-coding-agents,GitHub,,,,github-api,bradAGI/awesome-cli-coding-agents,819,219,,2026-02-07T00:53:24Z,2026-07-17T02:53:08Z,,2026-07-17T05:43:07+00:00
+ale-0527,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Self-Evolving Agents,https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents,external,github.com,"Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.","Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.",Gives readers a concrete source in Awesome Self-Evolving Agents when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (328 stars; 19 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1117,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1117,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents,"GitHub - XMUDeepLIT/Awesome-Self-Evolving-Agents: A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. · GitHub","A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. - XMUDeepLIT/Awesome-Self-Evolving-Agents",,2026-02-09,2026,XMUDeepLIT/Awesome-Self-Evolving-Agents,GitHub,,,,github-api,XMUDeepLIT/Awesome-Self-Evolving-Agents,328,19,,2026-02-09T10:57:30Z,2026-07-17T03:11:44Z,,2026-07-17T05:43:07+00:00
+ale-0528,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome AI Agent Papers,https://github.com/VoltAgent/awesome-ai-agent-papers,external,github.com,"Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.","Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.",Gives readers a concrete source in Awesome AI Agent Papers when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,588 stars; 165 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1118,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1118,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",context;verification,builder,curated-index,ok,https://github.com/VoltAgent/awesome-ai-agent-papers,"GitHub - VoltAgent/awesome-ai-agent-papers: A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. · GitHub","A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. - VoltAgent/awesome-ai-agent-papers",,2026-02-10,2026,VoltAgent/awesome-ai-agent-papers,GitHub,,,,github-api,VoltAgent/awesome-ai-agent-papers,1588,165,MIT,2026-02-10T10:58:31Z,2026-07-17T02:05:21Z,,2026-07-17T05:43:07+00:00
+ale-0529,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,awesome-ralph,https://github.com/snwfdhmp/awesome-ralph,external,github.com,"Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.","Primary-source operational guidance rather than commentary. Resource-specific angle: Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.",Gives readers a concrete source in awesome-ralph when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (913 stars; 71 forks; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,1119,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1119,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://github.com/snwfdhmp/awesome-ralph,"GitHub - snwfdhmp/awesome-ralph: A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. · GitHub","A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. - snwfdhmp/awesome-ralph",,2026-01-19,2026,snwfdhmp/awesome-ralph,GitHub,,,,github-api,snwfdhmp/awesome-ralph,913,71,,2026-01-19T08:42:54Z,2026-07-13T12:09:52Z,,2026-07-17T05:43:07+00:00
+ale-0530,Adjacent Awesome Lists,adjacent-awesome-lists,List,🧭,Awesome Agent Loops,https://github.com/serenakeyitan/awesome-agent-loops,external,github.com,"Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.","The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.",Gives readers a concrete source in Awesome Agent Loops when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (194 stars; 15 forks; CC-BY-4.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1120,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1120,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective;trigger,builder,curated-index,ok,https://github.com/serenakeyitan/awesome-agent-loops,"GitHub - serenakeyitan/awesome-agent-loops: A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. · GitHub","A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. - serenakeyitan/awesome-agent-loops",,2026-06-09,2026,serenakeyitan/awesome-agent-loops,GitHub,,,,github-api,serenakeyitan/awesome-agent-loops,194,15,CC-BY-4.0,2026-06-09T01:26:51Z,2026-07-15T11:44:20Z,,2026-07-17T05:43:07+00:00
+ale-0531,Discovery And Distribution,discovery-and-distribution,Template,🧾,Landing page,https://chaoyue0307.github.io/awesome-loop-engineering/,external,chaoyue0307.github.io,SEO-friendly entry point for the repository.,SEO-friendly entry point for the repository.,SEO-friendly entry point for the repository.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: SEO-friendly entry point for the repository.,Gives readers a concrete source in Landing page when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Repository-native template, schema, checklist, or guide; signal comes from reuse inside this project.",medium,README.md,1128,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1128,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,reusable-artifact,ok,https://chaoyue0307.github.io/awesome-loop-engineering/,Awesome Loop Engineering,"509 audited resources, 15 operational patterns, loop contracts, and runnable examples for recurring, verified AI-agent systems.",Chaoyue He,,,,chaoyue0307.github.io,,,,html-meta,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0532,Discovery And Distribution,discovery-and-distribution,List,🧭,Hugging Face dataset mirror,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,external,huggingface.co,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Synced dataset repo with the full project plus generated `data/resources.csv` and `data/resources.jsonl` resource sheets.,Gives readers a concrete source in Hugging Face dataset mirror when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Contextual source from huggingface.co; useful for practice signals or boundary conditions, not independent validation.",contextual,README.md,1129,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1129,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,curated-index,ok,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,cy0307/awesome-loop-engineering · Datasets at Hugging Face,We’re on a journey to advance and democratize artificial intelligence through open source and open science.,,,,,Hugging Face,,,,domain-fallback,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0533,Discovery And Distribution,discovery-and-distribution,Template,🧾,Landing page source,docs/index.html,local_path,,Source for the static landing page.,Source for the static landing page.,Source for the static landing page.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Source for the static landing page.,Gives readers a concrete source in Landing page source when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1130,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1130,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/index.html,Landing page source,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0534,Discovery And Distribution,discovery-and-distribution,Template,🧾,Sitemap,docs/sitemap.xml,local_path,,Crawl hints for the landing page and core repository pages.,Crawl hints for the landing page and core repository pages.,Crawl hints for the landing page and core repository pages.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Crawl hints for the landing page and core repository pages.,Gives readers a concrete source in Sitemap when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1131,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1131,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/sitemap.xml,Sitemap,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0535,Discovery And Distribution,discovery-and-distribution,Template,🧾,Robots file,docs/robots.txt,local_path,,Allows indexing and points crawlers to the sitemap.,Allows indexing and points crawlers to the sitemap.,Allows indexing and points crawlers to the sitemap.,Makes the project discoverable as both documentation and machine-readable data. Resource-specific angle: Allows indexing and points crawlers to the sitemap.,Gives readers a concrete source in Robots file when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1132,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1132,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/docs/robots.txt,Robots file,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0536,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Release notes,https://github.com/ChaoYue0307/awesome-loop-engineering/releases,external,github.com,"Versioned changelog of new resources, patterns, and repository changes.","Versioned changelog of new resources, patterns, and repository changes.","Versioned changelog of new resources, patterns, and repository changes.","Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Versioned changelog of new resources, patterns, and repository changes.",Gives readers a concrete source in Release notes when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (26 stars; 4 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1149,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1149,2026-07-15,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,reusable-artifact,ok,https://github.com/ChaoYue0307/awesome-loop-engineering/releases,Releases · ChaoYue0307/awesome-loop-engineering · GitHub,"🔁 509 audited resources for recurring AI-agent systems: 15 operational patterns, reusable loop contracts, runnable templates, and an interactive Resource Atlas. - Releases · ChaoYue0307/awesome-loop-engineering",,2026-06-09,2026,GitHub Releases,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,26,4,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T05:07:37Z,,2026-07-17T05:43:07+00:00
+ale-0537,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Roadmap,ROADMAP.md,local_path,,"Near-term work, pattern priorities, gallery goals, and open questions.","Near-term work, pattern priorities, gallery goals, and open questions.","Near-term work, pattern priorities, gallery goals, and open questions.","Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Near-term work, pattern priorities, gallery goals, and open questions.",Gives readers a concrete source in Roadmap when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1150,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1150,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/ROADMAP.md,Roadmap,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0538,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Launch article,posts/launch.md,local_path,,Shareable explanation of the concept and repository.,Shareable explanation of the concept and repository.,Shareable explanation of the concept and repository.,Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Shareable explanation of the concept and repository.,Gives readers a concrete source in Launch article when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1151,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1151,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/posts/launch.md,Launch article,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0539,Roadmap And Discussion,roadmap-and-discussion,Template,🧾,Discussion guide,meta/DISCUSSIONS.md,local_path,,"Suggested discussion categories, starter prompts, and moderation standard.","Suggested discussion categories, starter prompts, and moderation standard.","Suggested discussion categories, starter prompts, and moderation standard.","The resource is directly reusable as a starting artifact. Resource-specific angle: Suggested discussion categories, starter prompts, and moderation standard.",Gives readers a concrete source in Discussion guide when they need to reuse a concrete artifact or connect it to the wider ecosystem.,Repository-native artifact maintained and validated by this project's checks.,medium,README.md,1152,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1152,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/DISCUSSIONS.md,Discussion guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T05:43:07+00:00
+ale-0540,Roadmap And Discussion,roadmap-and-discussion,Pattern,🔁,Show your Loop Engineering patterns,https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/2,external,github.com,Community discussion for real or anonymized loop examples.,Community discussion for real or anonymized loop examples.,Community discussion for real or anonymized loop examples.,Keeps community evolution and evidence gathering part of the project surface. Resource-specific angle: Community discussion for real or anonymized loop examples.,Gives readers a concrete source in Show your Loop Engineering patterns when they need to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (26 stars; 4 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1153,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1153,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",whole-loop,builder,operational-pattern,ok,https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/2,Show your Loop Engineering patterns · ChaoYue0307/awesome-loop-engineering · Discussion #2 · GitHub,Show your Loop Engineering patterns,,2026-06-09,2026,GitHub Discussions,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,26,4,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T05:07:37Z,,2026-07-17T05:43:07+00:00