I Built a Tool That Stops AI Agents From Going Off the Rails

The Problem Nobody Talks About

Every AI agent operator knows this feeling: you set up a multi-step task, go grab coffee, and come back to find your agent has ordered 47 fidget spinners or replied to a customer with something... creative.

AI agents are powerful, but they're also notoriously prone to what I call "go-mode" behavior — executing without properly evaluating context, risk, or reversibility first.

The Stop-Decision Trainer Was Born

I built a checkpoint-based judgment system that evaluates four key factors before any agent action:

Context — Does the agent have enough information?
Risk — What's the worst-case outcome?
Reversibility — Can we undo this if it's wrong?
Signal Quality — Is the input clear enough to act on?

Here's the core scoring logic:

def evaluate_stop_decision(context, risk, reversibility, signal_quality):
    """Returns a stop-decision score from 0-100.
    Scores below 50 suggest the agent should NOT execute."""

    weights = {"context": 0.3, "risk": 0.25, "reversibility": 0.25, "signal": 0.2}

    score = (
        context * weights["context"] +
        risk * weights["risk"] +
        reversibility * weights["reversibility"] +
        signal_quality * weights["signal"]
    )

    return {"score": score, "decision": "STOP" if score < 50 else "GO"}