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Part 5 — Summary

Part 5 treated observability, evaluation and operation as a single system. What was established:

  • Logs are not enough. Semantic tracing, with OpenTelemetry GenAI conventions, is the only honest way to diagnose an AI incident.
  • Distinct layers — model, LLM, agent, product, business — require their own dashboards and SLOs.
  • Continuous eval is part of CI/CD: golden, adversarial, regression, human review where applicable.
  • SLOs in AI combine traditional latency with quality, cost and fallback rate.
  • Runbooks are mandatory. Without a known path, every incident is improvisation under stress.
  • Observability cost must be designed, not absorbed by surprise.

Part 6 enters operational economics: costs, performance and explicit FinOps in AI.