Why Your Existing Observability Stack Won't Save You When AI Agents Break
· 11 min read
Your Datadog dashboard shows zero errors. Latency is nominal. All services return HTTP 200. Meanwhile, your AI agent just booked a meeting in the wrong timezone, hallucinated a customer's order history, and burned $4 in tokens doing it.
This is what makes agent observability genuinely hard: the metrics you already have tell you almost nothing about whether agents are actually working.
Traditional distributed tracing was built on a set of assumptions about how software fails. LLM agents violate all of them, and the gap between "my infrastructure is healthy" and "my agent did the right thing" is where most debugging pain lives.
