LLM observability is the practice of tracing, logging, and monitoring what a language model or agent actually does in production. Every prompt, tool call, retrieved document, and generated token becomes visible and searchable, so a team can debug a bad output and catch a regression before a user does.
What does LLM observability actually capture?
A full trace of the run: the prompt sent, every tool call an AI agent made, the context behind a RAG answer, the tokens generated, latency, and cost, all linked to one request. Without that trace, a wrong answer is a mystery instead of a diagnosis.
How is it different from running evals?
AI agent evals test behavior before a change ships, on a fixed dataset. Observability watches real traffic after release, catching drift and edge cases a test suite never anticipated. Most production systems run both: evals gate the release inside an AI evaluation harness, observability watches what happens once real users hit it, and guardrails act on what it surfaces in real time. The discipline sits inside LLMOps, the broader practice of running these systems reliably day to day.
Frequently asked questions
Is LLM observability the same as logging?
Logging captures individual events. Observability links every prompt, tool call, retrieved document, and generated token from one request into a single traceable run, so a bad output is diagnosable back to the step that caused it, not just a pile of separate log lines.
Do I need observability if I already run evals before release?
Yes. Evals test a fixed dataset before a change ships; observability watches real traffic after it ships. A model that passed every eval can still drift or hit an edge case in production that the dataset never covered, and observability is the only layer that catches that.