Observability in AI Systems: Metrics That Matter

Deploying a model or agent in production is the beginning, not the end. Without observability, you do not know whether quality is degrading, costs are spiking, or a change in input data silently broke the system.

Technical metrics: p50/p95 latency, error rate, tokens consumed, cost per request, inference queue. Business metrics: resolution rate, satisfaction, conversion, time saved per process.

For LLMs we add quality evaluation: periodic human-reviewed samples, benchmarks with business questions, and detection of out-of-policy responses (PII, inappropriate tone, hallucinations).

Alerts must be actionable. A generic error threshold is not enough: we distinguish retrieval degradation, external tool failure, or input distribution drift.

We integrate OpenTelemetry, structured logs, and executive dashboards so technical and business teams share a common language about system performance.