Watch · narrated walkthroughs
An autonomous agent that quietly stops pursuing the goal it was given — while still looking busy and authorized — is one of the hardest failures to catch. This series builds runtime goal-drift detection from the ground up: what drift is and why non-determinism makes it inevitable, how to specify the intended behavior and reference policy to drift from, how to detect divergence at runtime with trajectory distance, reward-model monitors, and statistical change detection, how to survive the base-rate problem with drift budgets and calibrated escalation, and how to close the loop with correction, rollback, and human re-grounding without halting the fleet. Grounded in NIST AI RMF, OWASP LLM Top 10, the OWASP Agentic Security Initiative, MITRE ATLAS, the AI-safety literature on reward hacking, and classical statistical change-point detection.