agent-patrol

agent-patrol

Runtime pathology detection for AI agents. Diagnoses loops, stalls, oscillation, drift, and silent abandonment.

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The Problem

Your AI agents get stuck. They loop, oscillate, stall, drift from their task, and silently abandon their mission -- and you don't know until the token bill arrives.

Current solutions are blunt instruments:

• max_steps / timeout -- kills productive and unproductive agents equally
• Observability platforms -- trace what happened but not why it went wrong
• Framework-specific guards -- locked to one framework, not portable

agent-patrol diagnoses what kind of failure your agent is experiencing, in real time, across any framework.

Five Agent Pathologies

Installation

pip install agent-patrol

Zero hard dependencies. Works with Python stdlib alone. Optional enhancements:

# For embedding-based detection (recommended for production)
pip install agent-patrol[embeddings]

# For LLM-based semantic analysis
pip install agent-patrol[llm]

Quick Start

Option 1: Decorator

from agent_patrol import patrol

@patrol(on_pathology="log", sensitivity="medium")
def agent_step(state: dict) -> dict:
    # Your agent logic here
    return new_state

Option 2: Monitor

from agent_patrol import PatrolMonitor

monitor = PatrolMonitor(
    task_description="Research and summarize recent economic data",
    milestones=["Find GDP data", "Find inflation data", "Write summary"],
)

for step in agent_loop():
    report = monitor.observe(step.action, step.result)
    if report.pathology:
        print(f"DETECTED: {report.pathology} at step {report.step_detected}")
        print(f"Evidence: {report.evidence}")
        print(f"Action: {report.recommended_action}")

Option 3: CI Gate

# Run agent-patrol on a recorded trace file
agent-patrol check trace.jsonl --fail-on FUTILE_CYCLE,STALL

Output

PatrolReport(
    pathology="FUTILE_CYCLE",
    confidence=0.87,
    step_detected=14,
    evidence=["Steps 12-14: repeated 'search for X' with <0.05 semantic variance"],
    recommended_action="HALT",
    total_steps_observed=14,
    token_estimate_wasted=3400,
)

Framework Integration

Works with any agent framework. No lock-in.

• LangGraph -- wrap your node functions
• CrewAI -- wrap agent step callbacks
• OpenAI Agents SDK -- wrap tool execution
• Anthropic Claude Agent SDK -- wrap tool use blocks
• Raw loops -- wrap your iteration function

See examples/ for integration guides.

Configuration

from agent_patrol import PatrolConfig

config = PatrolConfig(
    # Detection sensitivity (low = fewer false positives, high = catches more)
    sensitivity="medium",  # "low" | "medium" | "high"

    # Which pathologies to detect
    detect=["FUTILE_CYCLE", "OSCILLATION", "STALL", "DRIFT", "ABANDONMENT"],

    # What to do when pathology is detected
    on_pathology="log",  # "log" | "raise" | "halt" | "callback"

    # Similarity backend
    similarity_backend="tfidf",  # "tfidf" | "embeddings" | "llm"

    # Custom thresholds
    cycle_similarity_threshold=0.92,
    stall_window=5,
    drift_threshold=0.60,
    oscillation_min_switches=3,
)

How It Works

agent-patrol maintains a rolling window of agent actions (text descriptions + optional state). For each new action, it runs five lightweight detectors:

1. Futile Cycle Detector -- Computes pairwise similarity in a sliding window. If the last N actions are all >0.92 similar, the agent is cycling.

2. Oscillation Detector -- Clusters actions by semantic similarity. If the agent alternates between 2+ clusters more than 3 times, it is oscillating.

3. Stall Detector -- If milestones are declared, computes distance to each milestone at every step. If the minimum distance is non-decreasing for 5+ steps, the agent is stalled.

4. Drift Detector -- Maintains the original task embedding. Computes cosine similarity between current action context and original task. If below 0.60, intent has drifted.

5. Abandonment Detector -- Combines drift detection with novel-cluster detection. If the agent is far from its original task AND close to a new coherent objective, it has silently abandoned its task.

Why This Exists

Every team running AI agents in production has experienced stuck agents. The token cost of a stuck agent looping for 200 steps before a timeout kills it can be $5-50 per incident. Multiply by hundreds of daily agent runs and the cost is material.

More importantly: stuck agents produce bad output. A stalled research agent returns incomplete results. A drifted coding agent implements the wrong feature. An oscillating planning agent produces an incoherent plan. These failures are silent -- the agent returns output that looks complete but isn't.

agent-patrol makes these failures visible and actionable.

License

MIT