Production operation means running the system under real constraints: deployment, scaling, tracing, observability, drift detection, incident response, fallback, rollback, data freshness and version management.
AI observability captures inputs, retrieved context, tool calls, outputs, latency, token use, cost, user feedback and safety signals while respecting privacy. Traces make multi-step failures diagnosable.
Cost and latency engineering controls model choice, token volume, caching, retrieval, parallelism and number of calls. Quality, speed and cost must be evaluated as a system-level trade-off.
