How do you monitor AI agents in production?
You monitor a production agent by treating its actions as first-class events. Log every tool call, decision, and outcome, track success rate, error rate, cost, and latency over time, and alert when any of them drifts from the baseline your testing established. Good monitoring turns a silent degradation into a signal you can act on before users complain.
Testing does not end at deployment
Even an agent that tested well will drift in production, because the model changes, the data changes, and the world changes. Monitoring is how you keep testing after launch, on live traffic, so you notice when real behavior diverges from what your benchmark predicted.
What to capture
- Every action: log the tool calls the agent made and their results, so you can reconstruct any run
- Outcome signals: track completion, error, and retry rates, not just uptime
- Cost and latency: watch tokens and response time, which drift as usage shifts
- Human overrides: when a person corrects the agent, capture it as a strong quality signal
Close the loop
The most valuable thing production monitoring produces is new test cases. Every real failure becomes a scenario you add to your golden dataset, so the same problem is caught automatically next time. Tools like Langfuse, Helicone, and Arize Phoenix specialize in agent tracing and let you feed production traces straight back into evaluation.