Observability and Incident Evidence

Capture enough context to explain behavior without leaking sensitive data.

◷ Estimated reading time: 5 min

Trace the Whole Request

A production trace should connect the user request to prompt and policy versions, retrieved sources, model calls, tool calls, state transitions, approvals, final outcome, latency, and cost. Correlation IDs let engineers reconstruct a distributed path across services.

Core Signals

Signal	Questions it answers
Logs	What event occurred and with which safe metadata?
Traces	Which sequence of retrieval, model, policy, and tool steps occurred?
Metrics	How often, how long, how expensive, and how successful?
Feedback	Where did users correct, reject, escalate, or abandon?

Observe Without Creating a New Data Leak

Do not log secrets or unrestricted full prompts by default. Apply redaction, access control, retention limits, tenant separation, and sampling. Store enough provenance to reproduce the decision while minimizing sensitive payloads.

Use an Error Taxonomy

Classify failures—retrieval miss, model refusal, schema error, authorization denial, tool timeout, unsafe output, user correction—rather than reporting one generic "AI error." A useful taxonomy turns traces into actionable engineering work and targeted evaluation cases.

Feedback loop

Important production incidents should produce three artifacts: a corrected design or control, a new regression case, and an operational signal that would detect recurrence.

Key Takeaways

Trace model behavior together with retrieval, policy, tools, state, outcome, latency, and cost.
Privacy-aware observability uses redaction, access control, retention, and tenant isolation.
An error taxonomy converts vague failures into actionable improvements and regression tests.

Which telemetry best explains why an agent chose a particular action?

What should happen after a meaningful production failure?

4.5 Runtime Guardrails and Recovery 4.7 Governance and Responsible AI

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Observability and Incident Evidence

Capture enough context to explain behavior without leaking sensitive data.

◷ Estimated reading time: 5 min

Trace the Whole Request

Core Signals

Signal	Questions it answers
Logs	What event occurred and with which safe metadata?
Traces	Which sequence of retrieval, model, policy, and tool steps occurred?
Metrics	How often, how long, how expensive, and how successful?
Feedback	Where did users correct, reject, escalate, or abandon?

Observe Without Creating a New Data Leak

Use an Error Taxonomy

Feedback loop

Important production incidents should produce three artifacts: a corrected design or control, a new regression case, and an operational signal that would detect recurrence.

Key Takeaways

Trace model behavior together with retrieval, policy, tools, state, outcome, latency, and cost.
Privacy-aware observability uses redaction, access control, retention, and tenant isolation.
An error taxonomy converts vague failures into actionable improvements and regression tests.

Which telemetry best explains why an agent chose a particular action?

What should happen after a meaningful production failure?

4.5 Runtime Guardrails and Recovery 4.7 Governance and Responsible AI