I've been deploying AI agents for the past year and kept hitting the same wall: agents that worked perfectly in demos would fail silently in production.

Not because the model was bad. Because the infrastructure wasn't designed for agents.

Here's what I learned:

The Problem: Traditional DevOps assumes deterministic behavior run the same test twice, get the same result. But AI agents have 63% execution path variance. Your unit tests catch 37% of failures at best.

Traditional APM (Datadog, New Relic) was built for binary failures crashes, timeouts, 500 errors. But agents fail semantically: wrong tool selection, stale memory, dropped context in handoffs. Nothing alerts.…