Observability

Observability in LLMOps is the set of practices and tooling that give operators insight into what a deployed model application is doing at runtime. Full observability requires three pillars: structured traces of every model call and tool invocation, metrics such as token counts and latency percentiles, and logs of errors and guardrail triggers. High observability reduces mean time to diagnosis when quality degrades and is a prerequisite for detecting model drift or cost anomalies early.