How to Monitor AI Agents in Production

Uptime monitoring is not enough. Here's what you actually need to track, why agent failures are mostly silent, and which tools the industry uses today. Why monitoring an AI agent is different Traditional monitoring is built around a simple contract: the system either works or it doesn't. A server is up or down. An API returns 200 or 500. Alerts fire, someone fixes it. AI agents break this contract. An agent can be fully available — no crashes, no timeouts, no error codes — while producing wrong answers, calling the wrong tool, or fabricating information. From an infrastructure perspective, everything looks healthy. From a user perspective, the agent is broken. The silent failure problem. The biggest production incidents with agents don't throw exceptions. They look like: a confident answer that's factually wrong, a tool call that partially succeeded, a workflow that loops until it hits a timeout. None of these trigger a standard alert. This is why the AI industry has converged on a bro