Hard crashes are easy: the agent throws an error, you fix it, and you move on. The harder problem is drift, when the agent technically succeeds but slowly stops doing what you meant. Outputs become vaguer, tool choices grow stranger, and costs start to creep up.In this talk, the speaker examines how to detect behavioral drift before users notice, borrowing from process control theory and anomaly detection in industrial systems. The session explores what the benchmark of “normal” actually means, and how to build the feedback loops needed to catch drift early.
This talk has been presented at AI Coding Summit London, check out the latest edition of this Tech Conference.





















