How to Set Up Agent Observability

Observability is the ability to understand what an agent is doing and why, from the outside. Because agents make many decisions across multi-step runs, you cannot improve or trust them without seeing inside their behavior. Good observability turns an opaque, unpredictable system into one you can diagnose, measure, and steadily improve. This guide covers how to set it up.

Instrument End-to-End Traces

The core of agent observability is the trace: a complete record of a single run from request to final output. A trace captures every model call, every tool invocation with its arguments and results, the agent's reasoning, and the timing of each step. With it, you can replay exactly what the agent did and pinpoint where a run went wrong.

Make tracing the foundation rather than an add-on. Instrument the agent so every run produces a trace automatically, structured so you can read the sequence of steps clearly. When something fails in production, the first move is to open the trace, so the quality of your tracing directly determines how fast you can diagnose problems.

Log With Structure and Context

Beyond traces, structured logging captures the events that matter across runs. Log tool calls, errors, decisions, and outcomes in a consistent, machine-readable format rather than free-form text, so you can search and aggregate them. Include context like the request, user, and timestamp so you can connect a log entry back to the situation that produced it.

Consistency is what makes logs useful at scale. When every component logs in the same structure, you can filter for a specific tool's failures, a particular user's sessions, or a class of error across thousands of runs. Thoughtful, structured logging is what lets you move from anecdotes about agent behavior to patterns you can actually act on.

Track Metrics That Reveal Behavior

Metrics give you the aggregate view that traces and logs cannot. Track the operational basics like latency, error rate, and volume, and add agent-specific metrics such as steps per run, tool call counts and failure rates, and token usage. These numbers reveal patterns, like an agent taking far more steps than expected or one tool failing constantly, that individual traces would never surface.

Watch metrics over time, not just in the moment. Trends expose slow degradation, cost creep, and shifts in behavior that point-in-time checks miss. Combining metrics for the big picture with traces for the detail gives you both the what and the why, which is exactly what you need to keep an agent healthy.

Connect Observability to Evaluation

Observability is most powerful when it feeds evaluation. Use the data you collect to build evaluation sets from real traces, especially the failures, so your test suite reflects what actually happens in production. Running these evaluations against changes lets you catch regressions before they reach users.

Tie quality measurement into the same pipeline. Sample real outputs and evaluate them for accuracy, format, and policy, and track the results alongside your operational metrics. When observability and evaluation are connected, every production run becomes a source of insight you can turn into a concrete improvement rather than data that just sits there.

Make It Usable for Investigation

Collecting data is only half the job; you have to be able to find what you need. Make traces and logs searchable by user, time, outcome, and other useful dimensions, so that when an issue arises you can locate the relevant run in seconds rather than scrolling endlessly. The speed of investigation depends on how well your data is organized.

Set up dashboards and alerts so problems surface on their own instead of waiting to be discovered. An alert on an error spike or cost jump, paired with quick access to the underlying traces, turns a potential incident into a fast diagnosis. Observability that is convenient to use is observability people actually rely on when it counts.