Your question is Scoring Agent Actions Before Manufacturing Execution. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you mitigate hallucination or goal-divergence in agents tasked with high-precision manufacturing operations?
Treat this as a hands-on machine learning design and implementation task. Propose a tabular risk model or anomaly detector that scores proposed agent actions before execution, rejects unsupported actions, and identifies distribution shift. Explain how you would construct labels, avoid leakage, evaluate rare unsafe events, calibrate thresholds, and monitor the system after deployment. Provide production-quality Python for preprocessing, training, validation, threshold selection, and drift monitoring.