Expand actionable future directions agenda

.github/ISSUE_TEMPLATE/direction-proposal.yml

@@ -0,0 +1,89 @@
+name: Future direction proposal
+description: Propose a measurable research study, infrastructure project, product pilot, or shared standard.
+title: "[Direction]: "
+labels:
+  - enhancement
+body:
+  - type: dropdown
+    id: workstream
+    attributes:
+      label: Workstream
+      description: Choose the closest Future Directions workstream.
+      options:
+        - 1. Factorized system evaluation
+        - 2. Long-horizon reliability and recovery
+        - 3. Verification science and false completion
+        - 4. Control policies, stopping, and abstention
+        - 5. Human oversight and decision quality
+        - 6. Durable state, memory, and provenance
+        - 7. Crash-safe execution and idempotency
+        - 8. Receipts, observability, and causal debugging
+        - 9. Security, permissions, and containment
+        - 10. Contract portability and interoperability
+        - 11. Reliability economics and resource allocation
+        - 12. Multi-agent delegation and coordination
+        - 13. Domain-grade loop use cases
+        - 14. Progressive rollout, handoff, and incident response
+        - 15. Lifecycle governance and maintainable adoption
+    validations:
+      required: true
+  - type: input
+    id: operator
+    attributes:
+      label: Intended operator and recurring task
+      description: Who uses or owns the loop, and what repeated work does it perform?
+      placeholder: Repository maintainer reviewing recurring dependency-update PRs.
+    validations:
+      required: true
+  - type: textarea
+    id: decision
+    attributes:
+      label: Decision and falsifiable claim
+      description: What decision should this work unlock, and what result would support or reject the claim?
+      placeholder: Evidence-aware retry should reduce false completion at the same model and token budget; reject if cost rises without a lower false-completion rate.
+    validations:
+      required: true
+  - type: textarea
+    id: baselines
+    attributes:
+      label: Baselines and budgets
+      description: State the current operation, single-pass, fixed-retry, and any cost-matched baseline plus the accounting boundary.
+    validations:
+      required: true
+  - type: textarea
+    id: contract
+    attributes:
+      label: Contract, verifier, state, and receipts
+      description: Link or describe the Loop Contract, independent evidence gate, persisted state, and decision receipts.
+    validations:
+      required: true
+  - type: textarea
+    id: failures
+    attributes:
+      label: Failure and abuse cases
+      description: Which interruptions, stale-state cases, verifier failures, permission attacks, or budget limits will be exercised?
+    validations:
+      required: true
+  - type: textarea
+    id: measures
+    attributes:
+      label: Primary and guardrail measures
+      description: Include quality, false completion, recovery, budget, cost, escalation, and human-effort measures as applicable.
+    validations:
+      required: true
+  - type: textarea
+    id: artifacts
+    attributes:
+      label: Public artifacts and constraints
+      description: What will be published, and what privacy, licensing, reproducibility, or redaction limits apply?
+    validations:
+      required: true
+  - type: checkboxes
+    id: checklist
+    attributes:
+      label: Checklist
+      options:
+        - label: The project tests a recurring AI-agent system, not a one-off prompt or generic automation.
+          required: true
+        - label: I will report negative results and unsuccessful runs rather than selected demonstrations only.
+          required: true

FUTURE-DIRECTIONS.md

@@ -1,73 +1,259 @@
 # Future Directions
 
-Move Loop Engineering forward through falsifiable research, dependable infrastructure, and testable product work. The strongest contributions make recurring agent loops **more measurable, portable, recoverable, economical, or governable**.
+Loop Engineering will mature when recurring agent systems can show not only that a task finished, but **why an action was allowed, which evidence accepted it, what state survived, how failure was contained, what the run cost, and when control returned to a person**.
 
-A credible result exposes the trigger, permissions, external verification gate, durable state, budget, escalation path, and failure evidence. Negative results matter when they reveal where a loop, verifier, state model, or operating assumption breaks.
+This agenda turns those gaps into fifteen research and engineering workstreams. Each workstream names the decision it should unlock, the smallest useful artifact, the measures that matter, and a completion gate. Use it to scope a paper, benchmark, runtime feature, product pilot, standards proposal, or public case study.
 
-## For Researchers
+## From Idea To Evidence
 
-| Category | High-value question | Evidence of progress | Concrete starting point |
-| --- | --- | --- | --- |
-| **Factorized evaluation** | How much performance comes from the model, harness, verifier, and outer loop separately? | Matched model and token budgets, controlled component ablations, confidence intervals, and held-out tasks | Run one fixed model across several harness and loop policies while scoring outcome, recovery, abstention, and resource use |
-| **Verification science** | Which combinations of deterministic checks, learned judges, independent agents, and humans prevent false completion? | False-accept and false-reject rates, calibration, tamper resistance, inter-rater agreement, and adversarial challenge sets | Compare a test suite, single judge, judge ensemble, and human review on the same traces with a frozen acceptance policy |
-| **State and learning across runs** | Which state representations support cold resume and improvement without stale-memory drift or compaction errors? | Longitudinal replay, state corruption tests, retention curves, recovery accuracy, state size, and maintenance cost | Build tasks that inject missing, stale, conflicting, or poisoned state between runs and measure recovery |
-| **Control, stopping, and budgets** | When should a loop retry, change strategy, escalate, abstain, or stop under uncertainty? | Quality-cost-latency Pareto curves, regret, budget adherence, failure containment, and sensitivity analysis | Compare fixed retries, evidence-aware stopping, and learned escalation under identical budgets |
-| **Human oversight** | Which handoff designs help people intervene accurately without review fatigue or automation bias? | Preregistered user studies, intervention precision, time to decision, missed failures, workload, and trust calibration | Compare raw traces, concise summaries, and structured evidence receipts on the same review decisions |
+1. **Choose one recurring task.** Name its operator, trigger, intake source, external definition of done, and consequence of a wrong action.
+2. **Freeze the Loop Contract.** Record permissions, verifier ownership, durable state, budgets, escalation, and exit before comparing systems.
+3. **Keep a meaningful baseline.** Compare with the current human workflow, a single agent run, and a bounded fixed-retry policy. Multi-agent work also needs a cost-matched single-agent baseline.
+4. **Test the failure path.** Inject interruption, stale state, unavailable tools, ambiguous requests, verifier disagreement, and exhausted budgets.
+5. **Publish the whole result.** Release unsuccessful runs, configuration versions, receipts, costs, human interventions, and the condition that would reject the approach.
 
-Research reports should publish negative results and separate task success from process discipline. A loop that reaches the answer through uncontrolled retries, hidden state, or verifier leakage is not equivalent to one that succeeds within its declared contract.
+| If you are a... | Start with | Produce first |
+| --- | --- | --- |
+| **Researcher or evaluator** | A falsifiable comparison in workstreams 1-5 | Preregistered protocol, benchmark slice, ablation table, traces, and negative results |
+| **Runtime or reliability engineer** | A missing guarantee in workstreams 6-10 | Reference implementation, fault-injection suite, conformance fixtures, and recovery report |
+| **Application or product developer** | A repeated job in workstreams 11-15 | Baseline, bounded vertical slice, domain verifier, handoff flow, and rollout report |
+| **Security or governance lead** | Permissions, adversarial cases, and ownership across workstreams 3, 8, 9, 14, and 15 | Threat model, enforceable policy, abuse tests, incident runbook, and audit evidence |
+
+## Shared Evaluation Protocol
+
+Results are comparable only when the unit under test is the **full loop configuration**: model, instructions, context, tools, permissions, runtime, verifier, state policy, retry policy, and budget.
+
+### Required Baselines
+
+- **Current operation:** the human or automated process the loop would replace or assist.
+- **Single pass:** one agent run with the same model, tools, context, and task.
+- **Fixed retry:** the same run repeated to a declared limit with unchanged policy.
+- **Candidate loop:** the proposed state, verification, control, or delegation change.
+- **Cost-matched alternative:** required whenever the candidate uses more models, agents, judges, tools, tokens, or human review.
+
+### Core Measures
 
-## For Infrastructure And Reliability Engineers
+| Measure | Operational definition | Why it matters |
+| --- | --- | --- |
+| **Verified completion rate** | Eligible runs accepted by an independent evidence gate | Separates completed work from persuasive final messages |
+| **False-completion rate** | Runs declared complete that fail an independent audit, divided by all declared-complete runs | Exposes unsafe optimism hidden by pass rate alone |
+| **Cold-resume fidelity** | Restart trials that continue from the correct objective, state, and next action | Tests whether persistence works after the conversation disappears |
+| **Recovery rate** | Injected failures followed by a valid state and bounded continuation or escalation | Measures resilience rather than happy-path success |
+| **Duplicate-side-effect rate** | Repeated external actions caused by retries, replay, or worker recovery | Detects unsafe execution semantics |
+| **Budget adherence** | Runs that stay inside declared token, cost, time, step, and concurrency limits | Makes autonomy governable and comparisons fair |
+| **Escalation precision and recall** | Necessary handoffs raised and unnecessary handoffs avoided against adjudicated cases | Measures both missed danger and review fatigue |
+| **Cost per verified outcome** | Total model, tool, compute, and review cost divided by independently accepted outcomes | Prevents quality gains from hiding uneconomic operation |
+| **Human correction load** | Review time and corrective actions per accepted outcome | Shows whether automation reduces work or merely moves it |
 
-| Category | Engineering objective | Evidence of progress | Concrete starting point |
-| --- | --- | --- | --- |
-| **Durable execution** | Resume safely after crashes, rate limits, deploys, and partial work | Idempotency tests, deterministic replay, duplicate-work rate, recovery time, and chaos results | Implement checkpointed queue processing with stable item IDs, bounded leases, and externally verified completion |
-| **Receipts and observability** | Make every decision reconstructable from evidence rather than model narration | Correlated traces, immutable receipts, state diffs, cost attribution, and replay tooling | Define a receipt envelope for trigger, input version, action, verifier result, state transition, budget, and next action |
-| **Permissions and isolation** | Enforce least privilege across tools, files, networks, credentials, and approval boundaries | Denied-action tests, capability manifests, sandbox escape tests, secret-exposure checks, and audited human overrides | Translate each Loop Contract permission into runtime policy and test both allowed and forbidden actions |
-| **Portability and interoperability** | Move one contract between session, CI, scheduled, and durable runtimes without changing its semantics | Conformance tests, adapter compatibility, migration fixtures, and equivalent receipts across runtimes | Build adapters for one contract on a local schedule, GitHub Actions, and a recoverable worker |
-| **Reliability economics** | Allocate models, tools, retries, and reviewers to maximize verified value within budget | Cost per verified outcome, latency percentiles, retry amplification, queue stability, and quality-budget frontiers | Add policy-based routing and compare it with fixed-model baselines on a replayable workload |
+Do not collapse these measures into one leaderboard score. Report distributions, uncertainty, every budget breach, and results by failure class. A higher task-success rate is not an improvement if false completion, unsafe action, human correction, or cost rises beyond the declared acceptance threshold.
 
-Operational maturity should be demonstrated with failure injection, not only happy-path demos. Useful artifacts include runbooks, service-level objectives, incident reviews, migration notes, and conformance suites.
+### Minimum Reproducibility Bundle
 
-## For Application And Product Developers
+Every study or operating report should include:
 
-| Category | Product objective | Evidence of progress | Concrete starting point |
-| --- | --- | --- | --- |
-| **Loop-worthy use cases** | Select recurring work with stable intake and a defensible external definition of done | Baseline human effort, recurrence rate, verifier coverage, exception rate, and avoided rework | Start with a narrow queue such as failing checks, stale documents, support triage, or threshold monitoring |
-| **Domain verification** | Encode completion in domain evidence rather than a generic model confidence score | Golden cases, contract tests, reviewer agreement, regression suites, and unresolved-edge-case logs | Pair every action path with a domain-specific checker and keep the agent unable to edit that checker |
-| **Human handoff experience** | Show progress, uncertainty, evidence, and safe next actions at the moment judgment is needed | Time to understand, correct intervention rate, override quality, notification burden, and user-reported clarity | Design one escalation view around objective, attempted actions, receipts, remaining budget, and recommended choices |
-| **Progressive rollout** | Move from observation to bounded production use without hiding risk | Shadow-mode precision, canary outcomes, rollback rate, budget breaches, and post-launch incidents | Roll out through read-only, suggestion, approval-required, and bounded-autonomy stages with explicit promotion gates |
-| **Maintainable adoption** | Keep loop behavior understandable as models, tools, policies, and teams change | Contract review time, configuration drift, ownership coverage, change failure rate, and onboarding success | Store versioned contracts beside code, assign an owner, and test them whenever prompts, tools, or verifiers change |
+- a versioned [Loop Contract](schemas/loop-contract.schema.json) and resolved runtime configuration;
+- task IDs, environment versions, model identifiers, harness versions, and seeds where supported;
+- baseline and candidate budgets measured on the same accounting boundary;
+- verifier code or rubric, verifier ownership, and a statement of what the acting agent can modify;
+- event receipts, state snapshots or diffs, and redacted traces sufficient to reconstruct each decision;
+- success, failure, escalation, and no-work cases rather than selected demonstrations;
+- raw per-run outcomes plus the script that produces aggregate tables;
+- privacy, licensing, and redaction decisions for data that cannot be public.
 
-Strong developer contributions are complete vertical slices: a real trigger, bounded permissions, independent gate, durable receipts, escalation UX, and a before/after operating result.
+## Priority Map
 
-## Shared Infrastructure Priorities
+The tiers describe dependency order, not prestige. Establish trustworthy state transitions before optimizing complex delegation.
 
-| Priority | Shared asset | Minimum useful outcome |
+| Tier | Workstreams | Shared proof point |
 | --- | --- | --- |
-| **Portable Loop Contract profile** | A small interoperable subset of objective, trigger, permissions, verification, state, budget, escalation, and exit fields | Two runtimes execute the same contract and produce semantically equivalent decisions |
-| **Receipt and replay format** | A stable event envelope for reconstructing each state transition and verifier decision | A third party can replay one run and identify why it retried, escalated, or exited |
-| **Factorized benchmark harness** | Evaluation that varies model, harness, verifier, and loop policy independently | Results attribute gains and costs to the component that caused them |
-| **Failure and safety challenge set** | Reusable cases for infinite retries, stale state, judge bias, prompt injection, permission escape, and budget exhaustion | Implementations report comparable containment and recovery metrics |
-| **Case-study commons** | Public or safely anonymized operating reports with contracts, receipts, budgets, and lessons | Builders can compare real failure modes and operating economics across domains |
+| **Foundation** | 3. Verification, 6. State and provenance, 7. Durable execution, 8. Receipts and replay, 9. Security | A loop can be interrupted, audited, attacked, recovered, and stopped without losing control of evidence or side effects |
+| **Scale** | 1. Factorized evaluation, 2. Long-horizon reliability, 4. Control policies, 10. Portability, 11. Economics | Improvements survive matched budgets, multiple runtimes, adverse conditions, and cost accounting |
+| **Adoption** | 5. Human oversight, 12. Multi-agent delegation, 13. Domain loops, 14. Rollout and handoff, 15. Lifecycle governance | A real operator can deploy, understand, interrupt, update, and retire the system using measured promotion gates |
+
+## Research And Evaluation Workstreams
+
+### 1. Factorized System Evaluation
+
+- **Decision unlocked:** whether an observed gain came from the model, context, harness, verifier, outer-loop policy, or extra compute.
+- **Build:** a factorial runner that changes one component at a time while holding tasks, model access, budgets, and acceptance policy fixed.
+- **Measure:** verified completion, false completion, effect size with uncertainty, latency, cost, recovery, and human review.
+- **Starter slice:** replay at least 25 public tasks through single pass, fixed retry, and evidence-aware retry using one model and two harness configurations.
+- **Completion gate:** another team can reproduce the comparison and attribute each reported gain to a declared component rather than an untracked configuration change.
+
+### 2. Long-Horizon Reliability And Recovery
+
+- **Decision unlocked:** whether a loop can preserve correct progress across sessions, interruptions, changing constraints, and partial failure.
+- **Build:** multi-stage tasks with graded checkpoints and controlled injections for process death, stale state, changed objectives, tool outages, and rollback.
+- **Measure:** milestone coverage, time to first irrecoverable error, cold-resume fidelity, rollback precision, recovery time, and cost by stage.
+- **Starter slice:** convert ten short tasks into five-stage sequences that must resume across three fresh sessions with no conversation history.
+- **Completion gate:** the benchmark distinguishes reasoning failure, state divergence, premature exit, timeout, and recovery failure instead of reporting only final pass or fail.
+
+### 3. Verification Science And False Completion
+
+- **Decision unlocked:** which evidence gates are trustworthy enough to advance state or declare completion.
+- **Build:** paired accepted and rejected outputs with hard negatives, verifier disagreement, tampering attempts, and underspecified intent; compare deterministic checks, learned judges, ensembles, and human adjudication.
+- **Measure:** false-accept and false-reject rates, calibration, inter-rater agreement, leakage sensitivity, tamper resistance, latency, and review cost.
+- **Starter slice:** collect 100 adjudicated receipts from coding or research tasks, freeze the acceptance policy, and evaluate four verifier designs without letting the actor edit its checker.
+- **Completion gate:** held-out results include confidence intervals and challenge cases, and no acceptance claim depends only on the acting model judging itself.
+
+### 4. Control Policies, Stopping, And Abstention
+
+- **Decision unlocked:** when the loop should retry, change strategy, ask for help, abstain before acting, or stop.
+- **Build:** a replay environment that applies alternative next-action policies to the same state and evidence under identical budgets.
+- **Measure:** marginal retry yield, decision regret, unnecessary escalation, missed escalation, post-action abstention, budget breaches, and cost-quality frontiers.
+- **Starter slice:** take 50 completed traces and compare fixed retries, evidence-aware rules, and a learned policy without rerunning the underlying task.
+- **Completion gate:** the proposed policy improves verified value per cost over fixed retry without increasing false completion or irreversible unsafe action.
+
+### 5. Human Oversight And Decision Quality
+
+- **Decision unlocked:** what evidence and interaction design help people intervene correctly without automation bias or review fatigue.
+- **Build:** a preregistered study comparing raw traces, narrative summaries, and structured receipts for the same approve, retry, rollback, and escalate decisions.
+- **Measure:** decision accuracy, missed failures, time to decision, override quality, confidence calibration, interruption burden, and retention after handoff.
+- **Starter slice:** anonymize 20 mixed-success cases, recruit participants who match the intended operator role, and keep the underlying evidence identical across interfaces.
+- **Completion gate:** the interface improves decision quality or time on held-out cases and reports where summaries hide evidence, induce over-trust, or add no value.
+
+## Runtime And Infrastructure Workstreams
+
+### 6. Durable State, Memory, And Provenance
+
+- **Decision unlocked:** which information may safely influence the next run and how it can be corrected, expired, compacted, or rolled back.
+- **Build:** an event-sourced state model with stable item IDs, schema versions, source lineage, confidence or authority, retention rules, checkpoints, and reversible compaction.
+- **Measure:** cold-resume fidelity, stale-read rate, contamination rate, state divergence, rollback precision, storage growth, and maintenance cost.
+- **Starter slice:** implement file, database, and object-store adapters for one contract, then inject missing, stale, conflicting, and poisoned state.
+- **Completion gate:** every derived state value points to its source event, corruption is detected before action, and a fresh worker reconstructs the same next action from persisted evidence.
+
+### 7. Crash-Safe Execution And Idempotency
+
+- **Decision unlocked:** whether work can survive worker death, retries, deploys, rate limits, and partial side effects without duplication or loss.
+- **Build:** leased work intake, stable idempotency keys, checkpointed state transitions, retry-safe tool adapters, dead-letter handling, and bounded recovery.
+- **Measure:** duplicate-side-effect rate, lost-work rate, recovery time, lease contention, poison-item isolation, and state consistency after restart.
+- **Starter slice:** run one reference worker while killing it before and after every external action, verifier call, state write, and acknowledgment.
+- **Completion gate:** repeated fault injection produces one accepted effect or a visible escalation, never silent loss, uncontrolled replay, or an unbounded retry storm.
+
+### 8. Receipts, Observability, And Causal Debugging
+
+- **Decision unlocked:** whether an operator can reconstruct why the loop acted, retried, escalated, or exited.
+- **Build:** a receipt envelope for trigger, objective version, input identity, action, tool result, verifier result, state transition, budget delta, next action, and redaction policy, mapped where practical to [OpenTelemetry GenAI semantic conventions](https://github.com/open-telemetry/semantic-conventions-genai).
+- **Measure:** receipt coverage, missing causal links, replay agreement, trace overhead, redaction failures, and time to diagnose seeded incidents.
+- **Starter slice:** instrument one validated contract end to end and build a replay view that derives the decision timeline from receipts rather than model narration.
+- **Completion gate:** an independent reviewer can identify the cause of a seeded failure and reproduce the control decision without access to the original chat session.
+
+### 9. Security, Permissions, And Containment
+
+- **Decision unlocked:** whether a compromised instruction, tool result, memory entry, dependency, or agent can exceed the authority granted to one run.
+- **Build:** machine-enforced capability manifests, short-lived credentials, egress controls, data boundaries, approval gates for irreversible actions, signed state transitions, and revocation.
+- **Measure:** unauthorized-action block rate, exfiltration success, privilege persistence, memory-poisoning survival, containment time, blast radius, and human-override auditability.
+- **Starter slice:** map one production-shaped loop to the [OWASP Top 10 for Agentic Applications](https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/), then automate at least one abuse case per applicable risk.
+- **Completion gate:** forbidden actions fail at the policy or sandbox boundary, not because the prompt politely requested restraint, and every override leaves an attributable receipt.
+
+### 10. Contract Portability And Interoperability
+
+- **Decision unlocked:** whether the same operating intent survives movement between a session tool, CI, scheduler, and durable worker.
+- **Build:** a portable Loop Contract profile, runtime adapters, capability negotiation, conformance fixtures, and explicit extension points for runtime-specific behavior.
+- **Measure:** semantic conformance, adapter effort, unsupported-field rate, equivalent state transitions, receipt compatibility, and migration defects.
+- **Starter slice:** execute the same contract locally, in GitHub Actions, and in a recoverable worker using one shared fixture set.
+- **Completion gate:** all runtimes agree on trigger, permission, verification, budget, escalation, and exit decisions, while unavoidable differences are machine-readable rather than hidden in prompts.
+
+## Application And Operations Workstreams
+
+### 11. Reliability Economics And Resource Allocation
+
+- **Decision unlocked:** which model, verifier, retry, tool, and reviewer allocation maximizes verified value inside a real operating budget.
+- **Build:** budget-aware routing with per-stage accounting, marginal-value stopping, queue priorities, and policy simulation over historical traces.
+- **Measure:** cost per verified outcome, marginal retry yield, latency percentiles, review cost, queue age, budget variance, and quality-cost Pareto frontiers.
+- **Starter slice:** replay 100 tasks across two model tiers, three retry limits, and two verification policies while preserving the same acceptance gate.
+- **Completion gate:** the policy identifies a stable Pareto improvement and publishes the workloads where a cheaper single pass or human process remains better.
+
+### 12. Multi-Agent Delegation And Coordination
+
+- **Decision unlocked:** when specialized roles, parallelism, or independent review justify coordination cost and new failure modes.
+- **Build:** tasks with explicit decomposition opportunities, role and handoff manifests, shared-state rules, attribution receipts, and single-agent ablations.
+- **Measure:** cost-matched verified completion, parallel speedup, coordination overhead, redundant work, handoff loss, disagreement resolution, and blame localization.
+- **Starter slice:** compare one expert-designed topology with a single agent using equivalent tools, total tokens, and wall-clock budget on separable and non-separable tasks.
+- **Completion gate:** the advantage survives role ablation and a cost-matched single-agent baseline; otherwise publish the negative result and simplify the topology.
+
+### 13. Domain-Grade Loop Use Cases
+
+- **Decision unlocked:** which recurring jobs are verifiable, reversible, frequent, and valuable enough to deserve a loop.
+- **Build:** a complete vertical slice for one bounded queue, such as PR checks, documentation drift, support triage, experiment monitoring, data-quality alerts, or evidence collection.
+- **Measure:** recurrence, verifier coverage, exception rate, avoided rework, operator time, false completion, rollback rate, and verified value per cycle.
+- **Starter slice:** baseline the current process for two weeks, run the candidate in read-only shadow mode, and adjudicate every disagreement.
+- **Completion gate:** repeated operating cycles show a measurable benefit over the baseline, and all unsupported or high-impact cases reach a named owner rather than disappearing.
+
+### 14. Progressive Rollout, Handoff, And Incident Response
+
+- **Decision unlocked:** when a loop may move from observation to recommendation, approval-required action, and bounded autonomy.
+- **Build:** promotion gates, a live evidence view, pause and kill controls, rollback, escalation routing, on-call ownership, and an incident runbook.
+- **Measure:** shadow precision, approval and override rates, missed escalations, time to understand, rollback success, time to containment, and notification burden.
+- **Starter slice:** define one acceptance threshold and rollback rule for each rollout stage, then rehearse ambiguous input, verifier failure, budget exhaustion, and operator unavailability.
+- **Completion gate:** each stage has objective promotion and demotion criteria, and an operator can pause, inspect, resume, or retire the loop without editing its prompt or state by hand.
+
+### 15. Lifecycle Governance And Maintainable Adoption
+
+- **Decision unlocked:** how loop behavior remains reviewable as models, prompts, tools, verifiers, data, policies, and owners change.
+- **Build:** versioned contracts beside code, ownership metadata, change review, regression suites, dependency and permission inventories, service objectives, deprecation, and retirement procedures.
+- **Measure:** configuration drift, change-failure rate, expired permissions, regression escape rate, review time, owner coverage, incident recurrence, and rollback readiness.
+- **Starter slice:** make contract changes visible in CI, require evidence for permission or verifier changes, and run one model upgrade through the same replay suite before promotion.
+- **Completion gate:** every active loop has an accountable owner, tested rollback, current permissions, declared service objectives, and a retirement path aligned with the [NIST Generative AI Profile](https://doi.org/10.6028/NIST.AI.600-1) where applicable.
+
+## Ninety-Day Execution Plans
+
+| Owner | Days 1-30 | Days 31-60 | Days 61-90 | Publish |
+| --- | --- | --- | --- | --- |
+| **Researcher or evaluator** | Select one workstream, freeze tasks and baselines, define rejection criteria | Run a pilot, repair protocol flaws, add adversarial and failure cases | Run the held-out study and one independent reproduction | Contract, task set, configs, per-run results, analysis, traces, and negative findings |
+| **Runtime or reliability engineer** | Instrument one contract and establish state plus receipt schemas | Add fault injection, idempotency tests, replay, and policy enforcement | Port to a second runtime and run conformance plus recovery tests | Reference worker, fixtures, SLOs, recovery table, and known limitations |
+| **Application or product developer** | Measure the current workflow and define the domain evidence gate | Run read-only shadowing and adjudicate every disagreement | Pilot approval-required actions with rollback and handoff drills | Before/after metrics, verifier coverage, escalation UX, costs, and rollout decision |
+| **Security or governance lead** | Map permissions, data, identities, side effects, and applicable threats | Turn threats into executable abuse cases and enforce runtime boundaries | Exercise containment, revocation, incident response, and audit reconstruction | Threat model, policy manifest, attack results, exceptions, and remediation owners |
+
+## Milestones Worth Coordinating
+
+| Field milestone | Evidence that it exists |
+| --- | --- |
+| **Portable contract profile** | Three runtime classes execute one contract against shared fixtures with declared semantic differences |
+| **Receipt and replay standard** | A third party reconstructs state transitions and control decisions from redacted receipts |
+| **Verifier reliability benchmark** | Deterministic, learned, ensemble, and human gates are compared on held-out hard negatives with calibration and cost |
+| **Long-horizon recovery suite** | Multi-session tasks include crashes, stale state, changed objectives, rollback, and dense intermediate grading |
+| **Agent security challenge set** | Applicable OWASP risks become executable tests with measured containment and recovery |
+| **Economic benchmark** | Model, verifier, retry, and review policies are compared under one accounting boundary and acceptance gate |
+| **Human handoff study** | Intended operators make blinded intervention decisions using alternative evidence interfaces |
+| **Case-study commons** | Public or safely anonymized reports include contracts, receipts, budgets, incidents, and before/after outcomes |
+
+## Proposal Template
 
-## Three Starter Projects
+Use this structure before opening an implementation or study:
 
-1. **Research:** create a matched-budget benchmark that compares fixed retry, evidence-aware retry, and human escalation while publishing every trace and negative result.
-2. **Engineering:** implement the same validated contract in a local scheduler, CI workflow, and durable worker, then publish a conformance and crash-recovery report.
-3. **Development:** ship one read-only production loop with a measurable baseline, independent checker, evidence-rich handoff, and staged rollout report.
+```text
+Title:
+Workstream and intended operator:
+Recurring task and trigger:
+Decision this project should unlock:
+Falsifiable claim:
+Current-operation baseline:
+Single-pass and fixed-retry baselines:
+Loop Contract and runtime:
+Independent evidence gate:
+State and receipt format:
+Budgets and accounting boundary:
+Failure injections and abuse cases:
+Primary and guardrail measures:
+Human escalation and rollback:
+Artifacts to publish:
+Result that would reject the approach:
+```
 
 ## Qualification Checklist
 
-Before proposing a new direction, record:
+Before calling a direction complete, confirm:
 
-- the recurring task and user or operator it serves;
-- the baseline and falsifiable claim;
-- the external evidence gate and who controls it;
-- the durable state and receipt format;
-- the time, cost, retry, and concurrency budgets;
-- the failure injection or adversarial cases;
-- the human escalation and rollback path;
-- the reproducibility, privacy, and licensing constraints;
-- the negative result that would cause the approach to be rejected.
+- [ ] The recurring task, intended operator, and consequence of error are explicit.
+- [ ] The baseline and falsifiable claim were fixed before the held-out result.
+- [ ] The external evidence gate and its owner are independent of the acting agent where practical.
+- [ ] State, receipts, budgets, escalation, rollback, and exit are part of the tested contract.
+- [ ] At least one interruption, stale-state, verifier, permission, and budget failure was exercised or marked not applicable with a reason.
+- [ ] Quality, reliability, safety, economics, and human effort are reported separately.
+- [ ] Unsuccessful runs and negative findings are included.
+- [ ] Reproduction, privacy, licensing, and redaction constraints are stated.
+- [ ] The result that would block deployment or reject the hypothesis is visible.
+- [ ] A maintainer, operator, or research owner is named for the next decision.
 
-Use [GitHub Discussions](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions) to propose a study, reference implementation, or case study. Use the [gallery checklist](gallery/README.md#minimum-useful-case-study) when reporting a loop that has actually run.
+Open a [direction proposal](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=direction-proposal.yml) with the template above, discuss cross-project questions in [GitHub Discussions](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions), or publish a run using the [minimum useful case-study checklist](gallery/README.md#minimum-useful-case-study).

README.md

@@ -126,15 +126,15 @@ Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collecti
 
 ## Future Directions
 
-Choose a role to find the highest-value open questions and build targets:
+The agenda organizes fifteen measurable workstreams in dependency order:
 
-| Audience | Priority categories |
-| --- | --- |
-| Researchers | Factorized evaluation, verification science, state across runs, stopping policies, and human oversight |
-| Infrastructure and reliability engineers | Durable execution, receipts and replay, least privilege, portability, and reliability economics |
-| Application and product developers | Loop-worthy use cases, domain verification, escalation UX, progressive rollout, and maintainable adoption |
+| Tier | Prove next | First artifacts |
+| --- | --- | --- |
+| Foundation | Verification, state, recovery, receipts, and security remain trustworthy under failure | Challenge sets, fault injection, replay, receipt schemas, and enforced permissions |
+| Scale | Gains survive ablation, long horizons, matched budgets, runtime changes, and cost accounting | Factorized benchmarks, control-policy replays, contract adapters, and economic frontiers |
+| Adoption | Operators can deploy, understand, interrupt, hand off, update, and retire a useful loop | Domain pilots, promotion gates, handoff studies, incident drills, and lifecycle controls |
 
-The complete [Future Directions agenda](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md) defines measurable outputs, concrete starter projects, shared infrastructure priorities, and a qualification checklist.
+The complete [Future Directions agenda](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md) provides a shared evaluation protocol, operational metric definitions, starter slices and completion gates for every workstream, 90-day role plans, field milestones, and a proposal template.
 
 ## Dataset Structure
 

data/resources.csv

@@ -522,25 +522,25 @@ ale-0520,"Critiques, Risks, And Limitations",critiques-risks-and-limitations,Pap
 ale-0521,"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. 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.",Use Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes 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,1197,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1197,2026-07-17,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,,,,,,,2607.13071,2026-07-17T17:58:18+00:00
 ale-0522,"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. 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.",Use Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 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,1198,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1198,2026-07-17,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,,,,,,,2607.14004,2026-07-17T17:58:18+00:00
 ale-0523,"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. 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.",Use Does Multi-Agent Debate Improve AI Feedback on Research Papers? 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,1199,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1199,2026-07-17,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,,,,,,,2607.14713,2026-07-17T17:58:18+00:00
-ale-0524,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. Comprehensive list for the agent harness layer that Loop Engineering builds on.,Use Awesome Harness Engineering by ai-boost to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,111 stars; 333 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1221,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1221,,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,3111,333,NOASSERTION,2026-03-29T15:39:49Z,2026-07-17T16:27:07Z,,2026-07-17T17:58:18+00:00
-ale-0525,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. High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.",Use Awesome Harness Engineering by walkinglabs to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,635 stars; 295 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1222,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1222,,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,3635,295,NOASSERTION,2026-03-29T11:29:37Z,2026-07-17T08:52:34Z,,2026-07-17T17:58:18+00:00
-ale-0526,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. Curated tools and resources for environments, constraints, and feedback around coding agents.",Use Awesome Agent Harness to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (488 stars; 46 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1223,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1223,,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,488,46,,2026-03-05T13:19:10Z,2026-07-17T16:21:08Z,,2026-07-17T17:58:18+00:00
-ale-0527,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. Survey-style list for context engineering across LLMs and agents.,Use Awesome Context Engineering to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,239 stars; 258 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1224,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1224,,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,3239,258,MIT,2025-07-02T17:46:03Z,2026-07-17T15:17:15Z,,2026-07-17T17:58:18+00:00
-ale-0528,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. Classic adjacent list for prompt techniques and prompting resources.,Use Awesome Prompt Engineering to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (6,172 stars; 728 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1225,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1225,,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,6172,728,Apache-2.0,2023-02-09T18:22:52Z,2026-07-17T13:34:01Z,,2026-07-17T17:58:18+00:00
-ale-0529,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. General list of LLM agent papers, frameworks, and applications.",Use Awesome LLM Agents 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,1226,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1226,,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,,2026-07-17T17:58:18+00:00
-ale-0530,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. Broad AI agent ecosystem map.,Use Awesome AI Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28,848 stars; 3,178 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1227,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1227,,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,28848,3178,NOASSERTION,2023-06-19T00:20:06Z,2026-07-17T16:42:36Z,,2026-07-17T17:58:18+00:00
-ale-0531,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. Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.",Use Awesome CLI Coding Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (820 stars; 219 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1228,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1228,,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,820,219,,2026-02-07T00:53:24Z,2026-07-17T15:10:10Z,,2026-07-17T17:58:18+00:00
-ale-0532,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. Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.",Use Awesome Self-Evolving Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (329 stars; 19 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1229,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1229,,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,329,19,,2026-02-09T10:57:30Z,2026-07-17T10:00:18Z,,2026-07-17T17:58:18+00:00
-ale-0533,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. Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.",Use Awesome AI Agent Papers to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,591 stars; 165 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1230,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1230,,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,1591,165,MIT,2026-02-10T10:58:31Z,2026-07-17T17:40:19Z,,2026-07-17T17:58:18+00:00
-ale-0534,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. 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.",Use awesome-ralph to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (913 stars; 72 forks; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,1231,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1231,,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,913,72,,2026-01-19T08:42:54Z,2026-07-13T12:09:52Z,,2026-07-17T17:58:18+00:00
-ale-0535,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. Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.",Use Awesome Agent Loops to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (194 stars; 16 forks; CC-BY-4.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1232,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1232,,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,194,16,CC-BY-4.0,2026-06-09T01:26:51Z,2026-07-15T11:44:20Z,,2026-07-17T17:58:18+00:00
-ale-0536,Explore And Reuse,explore-and-reuse,Template,🧾,Resource Atlas,https://chaoyue0307.github.io/awesome-loop-engineering/,external,chaoyue0307.github.io,"Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Turns the evidence into an interactive atlas and structured data. Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.",Use Resource Atlas to reuse a concrete artifact or connect it to the wider ecosystem.,"Reusable template, schema, checklist, or guide; signal comes from concrete adaptation and validation.",medium,README.md,1240,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1240,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective,builder,reusable-artifact,ok,https://chaoyue0307.github.io/awesome-loop-engineering/,Awesome Loop Engineering,"545 source-audited resources, 20 patterns, 20 validated contracts, and 8 runtime starters for recurring, stateful, verified AI-agent systems.",Chaoyue He,,,,chaoyue0307.github.io,,,,html-meta,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0537,Explore And Reuse,explore-and-reuse,List,🧭,Hugging Face dataset,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,external,huggingface.co,"Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.",Use Hugging Face dataset 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,1241,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1241,,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,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0538,Explore And Reuse,explore-and-reuse,Template,🧾,Dataset export guide,data/README.md,local_path,,"Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.",Use Dataset export guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1242,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1242,,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/data/README.md,Dataset export guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0539,Explore And Reuse,explore-and-reuse,Template,🧾,Runtime selection guide,meta/RUNTIME_SELECTION.md,local_path,,"Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Durable execution and replay are treated as first-class loop infrastructure. Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.",Use Runtime selection guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1243,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1243,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;workspace;state,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/RUNTIME_SELECTION.md,Runtime selection guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0540,Explore And Reuse,explore-and-reuse,Template,🧾,Future Directions agenda,FUTURE-DIRECTIONS.md,local_path,,"Choose from 15 measurable directions for research, infrastructure, and product development.","Choose from 15 measurable directions for research, infrastructure, and product development.","Choose from 15 measurable directions for research, infrastructure, and product development.","Turns open gaps into measurable research, infrastructure, and product directions. Choose from 15 measurable directions for research, infrastructure, and product development.",Use Future Directions agenda to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1244,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1244,,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/FUTURE-DIRECTIONS.md,Future Directions agenda,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0541,Shape What Comes Next,shape-what-comes-next,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.","Turns open questions and operating lessons into visible next work. Versioned changelog of new resources, patterns, and repository changes.",Use Release notes to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28 stars; 6 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1271,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1271,2026-07-15,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,🔁 545 source-audited resources · 20 operational patterns · 20 validated contracts · 8 runtime starters · interactive atlas + dataset for recurring AI-agent systems. - Releases · ChaoYue0307/awesome-loop-engineering,,2026-06-09,2026,GitHub Releases,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,28,6,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T17:42:51Z,,2026-07-17T17:58:18+00:00
-ale-0542,Shape What Comes Next,shape-what-comes-next,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.","Turns open questions and operating lessons into visible next work. Near-term work, pattern priorities, gallery goals, and open questions.",Use Roadmap to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1272,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1272,,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,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0543,Shape What Comes Next,shape-what-comes-next,Template,🧾,Launch article,posts/launch.md,local_path,,"Concise explanation of the concept, implementation kit, and evidence base.","Concise explanation of the concept, implementation kit, and evidence base.","Concise explanation of the concept, implementation kit, and evidence base.","Turns open questions and operating lessons into visible next work. Concise explanation of the concept, implementation kit, and evidence base.",Use Launch article to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1273,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1273,,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,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0544,Shape What Comes Next,shape-what-comes-next,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. Suggested discussion categories, starter prompts, and moderation standard.",Use Discussion guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1274,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1274,,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,,,,,,,,2026-07-17T17:58:18+00:00
-ale-0545,Shape What Comes Next,shape-what-comes-next,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.,Turns open questions and operating lessons into visible next work. Community discussion for real or anonymized loop examples.,Use Show your Loop Engineering patterns to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28 stars; 6 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1275,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1275,,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,28,6,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T17:42:51Z,,2026-07-17T17:58:18+00:00
+ale-0524,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. Comprehensive list for the agent harness layer that Loop Engineering builds on.,Use Awesome Harness Engineering by ai-boost to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,111 stars; 333 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1219,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1219,,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,3111,333,NOASSERTION,2026-03-29T15:39:49Z,2026-07-17T16:27:07Z,,2026-07-17T17:58:18+00:00
+ale-0525,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. High-signal harness list with strong categories for context, guardrails, specs, evals, runtimes, and benchmarks.",Use Awesome Harness Engineering by walkinglabs to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,635 stars; 295 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1220,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1220,,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,3635,295,NOASSERTION,2026-03-29T11:29:37Z,2026-07-17T08:52:34Z,,2026-07-17T17:58:18+00:00
+ale-0526,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. Curated tools and resources for environments, constraints, and feedback around coding agents.",Use Awesome Agent Harness to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (488 stars; 46 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1221,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1221,,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,488,46,,2026-03-05T13:19:10Z,2026-07-17T16:21:08Z,,2026-07-17T17:58:18+00:00
+ale-0527,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. Survey-style list for context engineering across LLMs and agents.,Use Awesome Context Engineering to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (3,239 stars; 258 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1222,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1222,,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,3239,258,MIT,2025-07-02T17:46:03Z,2026-07-17T15:17:15Z,,2026-07-17T17:58:18+00:00
+ale-0528,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. Classic adjacent list for prompt techniques and prompting resources.,Use Awesome Prompt Engineering to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (6,172 stars; 728 forks; Apache-2.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1223,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1223,,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,6172,728,Apache-2.0,2023-02-09T18:22:52Z,2026-07-17T13:34:01Z,,2026-07-17T17:58:18+00:00
+ale-0529,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. General list of LLM agent papers, frameworks, and applications.",Use Awesome LLM Agents 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,1224,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1224,,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,,2026-07-17T17:58:18+00:00
+ale-0530,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. Broad AI agent ecosystem map.,Use Awesome AI Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28,848 stars; 3,178 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1225,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1225,,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,28848,3178,NOASSERTION,2023-06-19T00:20:06Z,2026-07-17T16:42:36Z,,2026-07-17T17:58:18+00:00
+ale-0531,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. Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.",Use Awesome CLI Coding Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (820 stars; 219 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1226,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1226,,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,820,219,,2026-02-07T00:53:24Z,2026-07-17T15:10:10Z,,2026-07-17T17:58:18+00:00
+ale-0532,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. Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.",Use Awesome Self-Evolving Agents to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (329 stars; 19 forks; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1227,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1227,,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,329,19,,2026-02-09T10:57:30Z,2026-07-17T10:00:18Z,,2026-07-17T17:58:18+00:00
+ale-0533,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. Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.",Use Awesome AI Agent Papers to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (1,591 stars; 165 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1228,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1228,,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,1591,165,MIT,2026-02-10T10:58:31Z,2026-07-17T17:40:19Z,,2026-07-17T17:58:18+00:00
+ale-0534,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. 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.",Use awesome-ralph to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (913 stars; 72 forks; updated 2026-07-13); popularity is context, not proof of reliability.",medium,README.md,1229,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1229,,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,913,72,,2026-01-19T08:42:54Z,2026-07-13T12:09:52Z,,2026-07-17T17:58:18+00:00
+ale-0535,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. Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.",Use Awesome Agent Loops to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (194 stars; 16 forks; CC-BY-4.0 license; updated 2026-07-15); popularity is context, not proof of reliability.",medium,README.md,1230,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1230,,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,194,16,CC-BY-4.0,2026-06-09T01:26:51Z,2026-07-15T11:44:20Z,,2026-07-17T17:58:18+00:00
+ale-0536,Explore And Reuse,explore-and-reuse,Template,🧾,Resource Atlas,https://chaoyue0307.github.io/awesome-loop-engineering/,external,chaoyue0307.github.io,"Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.","Turns the evidence into an interactive atlas and structured data. Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.",Use Resource Atlas to reuse a concrete artifact or connect it to the wider ecosystem.,"Reusable template, schema, checklist, or guide; signal comes from concrete adaptation and validation.",medium,README.md,1238,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1238,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",objective,builder,reusable-artifact,ok,https://chaoyue0307.github.io/awesome-loop-engineering/,Awesome Loop Engineering,"545 source-audited resources, 20 patterns, 20 validated contracts, and 8 runtime starters for recurring, stateful, verified AI-agent systems.",Chaoyue He,,,,chaoyue0307.github.io,,,,html-meta,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0537,Explore And Reuse,explore-and-reuse,List,🧭,Hugging Face dataset,https://huggingface.co/datasets/cy0307/awesome-loop-engineering,external,huggingface.co,"Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.","Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.",Use Hugging Face dataset 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,1239,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1239,,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,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0538,Explore And Reuse,explore-and-reuse,Template,🧾,Dataset export guide,data/README.md,local_path,,"Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.","Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.",Use Dataset export guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1240,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1240,,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/data/README.md,Dataset export guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0539,Explore And Reuse,explore-and-reuse,Template,🧾,Runtime selection guide,meta/RUNTIME_SELECTION.md,local_path,,"Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.","Durable execution and replay are treated as first-class loop infrastructure. Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.",Use Runtime selection guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1241,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1241,,Apply,apply,"Reuse, adapt, and contribute concrete loop artifacts.",trigger;workspace;state,builder,repository-native,local_ok,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/RUNTIME_SELECTION.md,Runtime selection guide,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0540,Explore And Reuse,explore-and-reuse,Template,🧾,Future Directions agenda,FUTURE-DIRECTIONS.md,local_path,,"Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.","Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.","Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.","Turns open gaps into measurable research, infrastructure, and product directions. Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.",Use Future Directions agenda to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1242,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1242,,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/FUTURE-DIRECTIONS.md,Future Directions agenda,,,,2026,GitHub,GitHub,,,,repository,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0541,Shape What Comes Next,shape-what-comes-next,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.","Turns open questions and operating lessons into visible next work. Versioned changelog of new resources, patterns, and repository changes.",Use Release notes to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28 stars; 6 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1269,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1269,2026-07-15,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,🔁 545 source-audited resources · 20 operational patterns · 20 validated contracts · 8 runtime starters · interactive atlas + dataset for recurring AI-agent systems. - Releases · ChaoYue0307/awesome-loop-engineering,,2026-06-09,2026,GitHub Releases,GitHub,,,,github-api,ChaoYue0307/awesome-loop-engineering,28,6,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T17:42:51Z,,2026-07-17T17:58:18+00:00
+ale-0542,Shape What Comes Next,shape-what-comes-next,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.","Turns open questions and operating lessons into visible next work. Near-term work, pattern priorities, gallery goals, and open questions.",Use Roadmap to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1270,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1270,,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,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0543,Shape What Comes Next,shape-what-comes-next,Template,🧾,Launch article,posts/launch.md,local_path,,"Concise explanation of the concept, implementation kit, and evidence base.","Concise explanation of the concept, implementation kit, and evidence base.","Concise explanation of the concept, implementation kit, and evidence base.","Turns open questions and operating lessons into visible next work. Concise explanation of the concept, implementation kit, and evidence base.",Use Launch article to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1271,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1271,,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,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0544,Shape What Comes Next,shape-what-comes-next,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. Suggested discussion categories, starter prompts, and moderation standard.",Use Discussion guide to reuse a concrete artifact or connect it to the wider ecosystem.,Local artifact maintained with automated validation checks.,medium,README.md,1272,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1272,,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,,,,,,,,2026-07-17T17:58:18+00:00
+ale-0545,Shape What Comes Next,shape-what-comes-next,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.,Turns open questions and operating lessons into visible next work. Community discussion for real or anonymized loop examples.,Use Show your Loop Engineering patterns to reuse a concrete artifact or connect it to the wider ecosystem.,"Inspectable GitHub source (28 stars; 6 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.",medium,README.md,1273,https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1273,,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,28,6,CC0-1.0,2026-06-09T16:17:27Z,2026-07-17T17:42:51Z,,2026-07-17T17:58:18+00:00

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@@ -1,3 +1,3 @@
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+size 437352

docs/index.html

@@ -642,6 +642,23 @@
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       .future-output strong { display: block; margin-bottom: 3px; color: var(--ink); }
+      .future-steps {
+        display: grid;
+        grid-template-columns: repeat(4, minmax(0, 1fr));
+        border-bottom: 1px solid var(--line-strong);
+      }
+      .future-step { min-width: 0; padding: 20px 20px 20px 0; }
+      .future-step + .future-step { border-left: 1px solid var(--line); padding-left: 20px; }
+      .future-step span {
+        display: block;
+        color: var(--blue);
+        font-family: "IBM Plex Mono", ui-monospace, monospace;
+        font-size: 0.63rem;
+        font-weight: 700;
+        text-transform: uppercase;
+      }
+      .future-step strong { display: block; margin-top: 6px; font-size: 0.88rem; }
+      .future-step p { margin: 5px 0 0; color: var(--muted); font-size: 0.76rem; line-height: 1.5; }
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@@ -891,6 +908,10 @@
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         .future-track + .future-track { border-top: 1px solid var(--line); border-left: 0; padding-left: 0; }
+        .future-steps { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+        .future-step:nth-child(n) { border-left: 1px solid var(--line); padding: 18px; }
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@@ -1789,43 +1813,49 @@
           <div class="section-head">
             <div>
               <p class="section-label"><i class="icon" data-lucide="compass" aria-hidden="true"></i><span>07 / Future directions</span></p>
-              <h2>Decide what the field should prove next.</h2>
+              <h2>Turn open problems into evidence.</h2>
             </div>
-            <p class="section-intro">A credible agenda separates claims to test, infrastructure to build, and product loops to validate. Each direction should end in external evidence, reproducible artifacts, or measured operating results.</p>
+            <p class="section-intro">Fifteen workstreams define a decision, minimum artifact, metrics, starter slice, and completion gate. Build the foundations first, then prove scale and adoption.</p>
           </div>
           <div class="future-grid">
             <article class="future-track">
-              <h3><i class="icon" data-lucide="book-open" aria-hidden="true"></i>For researchers</h3>
+              <h3><i class="icon" data-lucide="shield-check" aria-hidden="true"></i>Foundation</h3>
               <ul>
-                <li>Separate model, harness, verifier, and loop effects under matched budgets.</li>
-                <li>Measure false completion, calibration, tamper resistance, and recovery.</li>
-                <li>Study state drift, stopping policies, and human oversight across runs.</li>
+                <li>Challenge verifiers with hard negatives and tampering.</li>
+                <li>Recover state and side effects after injected crashes.</li>
+                <li>Reconstruct every decision from receipts under least privilege.</li>
               </ul>
-              <p class="future-output"><strong>Useful outputs</strong>Benchmarks, ablations, longitudinal datasets, preregistered studies, and negative results.</p>
+              <p class="future-output"><strong>Completion signal</strong>The loop can be interrupted, audited, attacked, recovered, and stopped without losing control of evidence or effects.</p>
             </article>
             <article class="future-track">
-              <h3><i class="icon" data-lucide="wrench" aria-hidden="true"></i>For infrastructure engineers</h3>
+              <h3><i class="icon" data-lucide="chart-no-axes-combined" aria-hidden="true"></i>Scale</h3>
               <ul>
-                <li>Build crash-safe, idempotent, replayable, and observable runtimes.</li>
-                <li>Turn contract permissions into enforceable least-privilege policy.</li>
-                <li>Standardize receipts and preserve semantics across runtime adapters.</li>
+                <li>Separate model, harness, verifier, and loop effects.</li>
+                <li>Test long horizons, stop rules, and runtime portability.</li>
+                <li>Compare quality, recovery, latency, cost, and human effort.</li>
               </ul>
-              <p class="future-output"><strong>Useful outputs</strong>Reference workers, conformance suites, receipt schemas, replay tools, SLOs, and incident reports.</p>
+              <p class="future-output"><strong>Completion signal</strong>Gains survive matched budgets, adverse conditions, multiple runtimes, and full cost accounting.</p>
             </article>
             <article class="future-track">
-              <h3><i class="icon" data-lucide="code-2" aria-hidden="true"></i>For application developers</h3>
+              <h3><i class="icon" data-lucide="badge-check" aria-hidden="true"></i>Adoption</h3>
               <ul>
-                <li>Choose recurring jobs with stable intake and a defensible definition of done.</li>
-                <li>Design domain checkers and evidence-rich human handoffs.</li>
-                <li>Progress from read-only shadowing to bounded autonomy with promotion gates.</li>
+                <li>Baseline one frequent, verifiable, reversible domain job.</li>
+                <li>Shadow first, then promote through measured approval gates.</li>
+                <li>Rehearse handoff, rollback, incident response, and retirement.</li>
               </ul>
-              <p class="future-output"><strong>Useful outputs</strong>Runnable vertical slices, domain evals, escalation UX, rollout studies, and failure write-ups.</p>
+              <p class="future-output"><strong>Completion signal</strong>A named operator can understand, interrupt, update, and retire a useful loop from external evidence.</p>
             </article>
           </div>
+          <div class="future-steps" aria-label="Four steps from question to evidence">
+            <div class="future-step"><span>01 / Scope</span><strong>Choose one recurring task</strong><p>Name the operator, trigger, evidence gate, and consequence of error.</p></div>
+            <div class="future-step"><span>02 / Compare</span><strong>Freeze fair baselines</strong><p>Test current operation, single pass, fixed retry, and a cost-matched candidate.</p></div>
+            <div class="future-step"><span>03 / Stress</span><strong>Exercise failure paths</strong><p>Inject interruption, stale state, verifier conflict, attack, and budget exhaustion.</p></div>
+            <div class="future-step"><span>04 / Publish</span><strong>Release the whole result</strong><p>Share contracts, receipts, costs, unsuccessful runs, and rejection criteria.</p></div>
+          </div>
           <div class="future-shared">
-            <strong>Shared milestones</strong>
-            <p>Portable contracts and receipts, factorized benchmarks, replayable state, failure-injection suites, and public or safely anonymized case studies.</p>
-            <a class="button" href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md"><i class="icon" data-lucide="compass" aria-hidden="true"></i>Open the agenda</a>
+            <strong>Start a 90-day project</strong>
+            <p>Choose the researcher, runtime, application, or security plan, then use the shared protocol and proposal template to make the result comparable.</p>
+            <a class="button" href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md"><i class="icon" data-lucide="compass" aria-hidden="true"></i>Open 15 workstreams</a>
           </div>
         </div>
       </section>

meta/hf_card_body.md

@@ -93,15 +93,15 @@ Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collecti
 
 ## Future Directions
 
-Choose a role to find the highest-value open questions and build targets:
+The agenda organizes fifteen measurable workstreams in dependency order:
 
-| Audience | Priority categories |
-| --- | --- |
-| Researchers | Factorized evaluation, verification science, state across runs, stopping policies, and human oversight |
-| Infrastructure and reliability engineers | Durable execution, receipts and replay, least privilege, portability, and reliability economics |
-| Application and product developers | Loop-worthy use cases, domain verification, escalation UX, progressive rollout, and maintainable adoption |
+| Tier | Prove next | First artifacts |
+| --- | --- | --- |
+| Foundation | Verification, state, recovery, receipts, and security remain trustworthy under failure | Challenge sets, fault injection, replay, receipt schemas, and enforced permissions |
+| Scale | Gains survive ablation, long horizons, matched budgets, runtime changes, and cost accounting | Factorized benchmarks, control-policy replays, contract adapters, and economic frontiers |
+| Adoption | Operators can deploy, understand, interrupt, hand off, update, and retire a useful loop | Domain pilots, promotion gates, handoff studies, incident drills, and lifecycle controls |
 
-The complete [Future Directions agenda](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md) defines measurable outputs, concrete starter projects, shared infrastructure priorities, and a qualification checklist.
+The complete [Future Directions agenda](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md) provides a shared evaluation protocol, operational metric definitions, starter slices and completion gates for every workstream, 90-day role plans, field milestones, and a proposal template.
 
 ## Dataset Structure