Hugging Face's logo

ai-systems
/
agent-task-classifier

Text Classification
Scikit-learn
Joblib
ai-systems
agents
agent-tasks
routing
automation
tool-use
multi-agent
Model card Files
xet
 Community
agent-task-classifier
File size: 2,429 Bytes
53aeebd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
---
license: apache-2.0
library_name: sklearn
pipeline_tag: text-classification
tags:
- ai-systems
- agents
- agent-tasks
- routing
- automation
- tool-use
- multi-agent
---

# Agent Task Classifier

**Agent Task Classifier** is a lightweight reference model that maps short agent-task descriptions to practical operational categories.

It is published under the **ai-systems** handle as a transparent example for agent routing, task decomposition, and AI-system prototyping.

## Task Labels

- `browser`
- `coding`
- `communication`
- `control`
- `data-analysis`
- `memory`
- `multi-agent`
- `planning`
- `research`
- `retrieval`
- `tool-use`
- `verification`
- `workflow-automation`

## Examples

Input:

```text
Search the web for recent papers and summarize the findings.
```

Expected category:

```text
research
```

Input:

```text
Fix a bug in a Python project and run the tests.
```

Expected category:

```text
coding
```

Input:

```text
Require human approval before sending the final message.
```

Expected category:

```text
control
```

## Usage

```python
from joblib import load

classifier = load("agent-task-classifier.joblib")

text = "Delegate subtasks to specialized agents and merge their results"
prediction = classifier.predict([text])[0]

print(prediction)
```

## Model Architecture

The model uses:

- TF-IDF text features
- unigram and bigram features
- logistic regression classification

It is intentionally small and easy to inspect.

## Training Data

The reference model was trained on a small curated set of agent-task descriptions covering:

- research
- coding
- browser interaction
- data analysis
- retrieval
- communication
- workflow automation
- planning
- verification
- memory
- tool use
- multi-agent coordination
- control and approval

## Intended Use

Suitable for:

- agent-routing demos
- task taxonomies
- educational examples
- workflow prototypes
- lightweight agent orchestration experiments
- AI-system documentation

## Limitations

This is a **reference model**, not a production-grade task router.

The training set is intentionally small, so ambiguous or out-of-domain requests may be misclassified.

It should not be used to make medical, legal, financial, safety-critical, or other high-impact decisions.

## Related Dataset

`ai-systems/ai-system-patterns`

## Related Models

- `ai-systems/system-router`
- `ai-systems/capability-classifier`

## License

Apache-2.0