What does a production-grade observability stack for AI agents look like? What metrics, logs, and traces are essential? How would you debug a scenario where an agent produces correct outputs 95% of the time but fails unpredictably?

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A production-grade observability stack would combine metrics (latency, cost, success rates), structured logs (inputs, outputs, prompts, tool calls), and distributed traces that capture full execution paths across agents. Debugging would rely on replaying failing traces, comparing successful versus failed runs, and using automated evaluations or shadow deployments to isolate regressions.

What does a production-grade observability stack for AI agents look like? What metrics, logs, and traces are essential? How would you debug a scenario where an agent produces correct outputs 95% of the time but fails unpredictably?

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