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:

Search the web for recent papers and summarize the findings.

Expected category:

research

Input:

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

Expected category:

coding

Input:

Require human approval before sending the final message.

Expected category:

control

Usage

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

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