Part 6: Observability for AI Agents: Tracing, Metrics, and Drift
Part 6 of a series building a support-ticket agent with no framework. Previous: Part 5 (guardrails). Repo: github.com/akash-pal/agent-from-scratch "Run the eval set" and "is this agent healthy right now" are different questions, and it's easy to only build infrastructure for the first one. Eval sets run offline, on cases you already thought of. Production traffic doesn't ask permission to send…
Part 6 of a series on building an AI-powered support-ticket agent delves into the importance of observability for AI agents. While evaluation sets are useful for testing offline, production traffic can introduce unexpected inputs that observability helps to detect. The minimum trace payload consists of structured records for each tool call, including hashed arguments to protect sensitive data, duration, result summary, model used, and token usage.
The trace log serves as CLI output, formatting logs for easy readability in terminals. Three monitoring layers are built atop this foundation: trace logging, online metrics tracking aggregate numbers over time, and drift detection comparing metrics over time to identify model changes causing performance shifts. The trace payload is designed to accommodate future monitoring systems without requiring modifications to the tracing code.
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