Section 051 · Chapter 8, Operating AI: Observability, Relevance, and Economics

Observability and Tracing for AI Systems

You cannot debug a final answer if you cannot see the path that produced it.

latencyobservabilityretrievalobservability tracing

What to do

  1. Use a small set that covers user-visible quality, safety, latency, tool reliability, and cost.
  2. Keep catastrophic events, such as cross-customer data exposure or an unauthorized irreversible action, out of a comforting average.
  3. Save the user-visible output, prompt assembly, model and prompt versions, policy version, retrieval snapshot, tool inputs and results, timing, permissions, judge decisions, and deployment state.
  4. Apply privacy and access controls; incident evidence can contain the most sensitive data in the system.
  5. Do not let uncertainty about the model become an excuse for silence about observed impact.

Evidence to preserve

  • Save the user-visible output, prompt assembly, model and prompt versions, policy version, retrieval snapshot, tool inputs and results, timing, permissions, judge decisions, and deployment state.
  • Preserve the smallest faithful reproduction, add nearby variants and affected slices, assign an owner, and connect the regression case to the release gate and production monitor.
  • Log the request, but also log confirmation.
  • Preserve the inputs, versions, configurations, raw outcomes, and results for latency, observability, retrieval, observability tracing needed to reproduce work on Observability and Tracing for AI Systems.

Expert note

In a real release review, traces should have stable correlation IDs, privacy-aware redaction, span-level metadata, model and prompt versions, retrieval snapshots, tool inputs and outputs, token/cost metrics, latency percentiles, judge scores, and links back to eval cases and production incidents.

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Jason Arbon. "Observability and Tracing for AI Systems." Testing AI Knowledge Edition, section 51.

https://jarbon.ai/testing-ai/knowledge/ch051-observability-tracing.html

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