How do you set up observability and tracing for AI agents?
Agent observability means capturing every step of a run, the prompts, tool calls, results, and decisions, as a structured trace you can inspect and search. The practical way to do it is to instrument with the OpenTelemetry GenAI conventions, which give you a vendor-neutral format, and send the traces to a platform built for LLM runs. Good tracing is what lets you debug a bad run and turn it into a test case.
Why tracing agents is different
A single agent task can involve dozens of model calls and tool invocations. When it goes wrong, a plain log tells you little. A trace that captures the full tree of steps, with inputs and outputs at each node, lets you see exactly where the agent went off course.
Start with a standard
The OpenTelemetry GenAI conventions define a common way to record model and agent activity, so your traces are portable across tools rather than locked to one vendor. OpenLLMetry is an open-source implementation that instruments popular agent frameworks for you.
Where to send the traces
Several platforms are built for LLM and agent traces, such as Langfuse. They give you searchable traces, cost and latency breakdowns, and a path from a bad production run to a new evaluation case. The tools guide lists the main options side by side.