Your Production Agent Is Flying Blind (Here's the Fix)

You built the agent. It works in dev. You deploy it. Then, three days later, a user reports it's broken and you have no idea why — because you have no idea what it actually did. This is the #1 operational failure mode for production AI agents. Not hallucinations. Not prompt injection. Not model capability gaps. Lack of observability. Here's what changes when you add proper tracing. Why Standard APM Tools Fall Short Your Datadog setup catches HTTP 500s. That's not good enough for agents. LLM agents fail in ways that don't map to status codes: The model answered, just incorrectly (success by APM, failure by business) The response took 45 seconds instead of 2 (latency spike invisible without percentile tracking) The agent used $0.84 on one request instead of the expected $0.004 (cost runaway) The new prompt version degraded quality by 12% across all users (regression you can't see without evals) The five questions your observability stack must answer: What did the agent decide to do —