How to Monitor AI Agent Drift in Production

How to Monitor AI Agent Drift in Production Your AI agent worked perfectly last week. This week, it's returning subtly wrong answers. No errors. No crashes. Just... drift. This is agent drift -- the silent degradation of AI agent behavior over time. Model updates, upstream API changes, shifted data distributions, or prompt injection can all cause an agent to produce different outputs than expected, with zero error signals. I ran into this problem running a fleet of 20+ API-backed agents. One morning, an agent that had been reliably returning structured JSON started returning markdown tables instead. No error codes. No exceptions. Just quietly wrong output that propagated downstream. Here's how I built a monitoring system to catch drift before users do. The Problem: Silent Failures Traditional monitoring catches crashes and timeouts. It does not catch: An LLM agent that starts hallucinating after a model update An API endpoint that changes its response format without bumping versions A