Agent observability
Last verified: June 2026· definition
Agent observability captures what an agent actually did: each model request, tool call, input, and output, plus latency and cost. Without it, a misbehaving agent in production is a black box.
Beyond tracing, mature observability adds prompt versioning, regression detection, and alerting on quality or cost drift — turning incidents into data you can act on.
It pairs naturally with evals: observability surfaces real-world failures, and those failures become new eval cases.
The trace is the unit, not the request
Conventional logging records individual calls, which is close to useless for an agent. What you need to reconstruct is the whole loop: the initial goal, each model turn, every tool call with its arguments and result, and the reasoning connecting them. Without that full sequence, diagnosing why an agent looped, chose a wrong tool, or stopped early is guesswork. This is the single biggest difference between observability for agents and observability for ordinary services.
Record what the model actually saw
Debugging agent behaviour almost always comes down to a gap between what you believe was in context and what genuinely was. Prompts assembled from templates, retrieved documents, and accumulated history are easy to get subtly wrong, and the failure looks like a model problem. Logging the resolved prompt — not the template — is what turns those investigations from speculation into inspection, and it is frequently the missing piece when a bug resists explanation.
Cost and latency belong in the same trace
Agents make many model calls per task, so per-call metrics tell you little about what a task costs or how long it takes. Attributing tokens, spend, and duration to the whole trace is what surfaces the expensive patterns: a loop retrying quietly, a tool returning far more than expected, a route where a small number of tasks consume most of the budget. Keeping these in the trace rather than a separate system means the expensive case and its cause are visible together.
Sampling versus full capture
Traces are large, particularly for long agent runs, so capturing everything indefinitely becomes expensive. The usual compromise is full capture for failures and a sample of successes, with retention shorter for the routine cases. What matters is deciding deliberately rather than by default, because the trace you did not keep is invariably the one needed when someone reports a problem from last week.
Common misconceptions
MythApplication logging is sufficient for agents.
RealityConventional logs capture individual calls without the sequence connecting them. Agent failures are usually properties of the loop, which per-call logs cannot show.
MythObservability is something to add once agents are in production.
RealityIt is what makes development tractable. Debugging agent behaviour without traces is slow enough that teams often build the tooling anyway, just later and under pressure.
MythLogging the prompt template is enough.
RealityThe template is not what the model saw. Bugs typically live in how variables, retrieved content, and history were resolved, which only the final assembled prompt reveals.
Frequently asked questions
What should I log for an AI agent?
The full trace of each task: the goal, every model turn, each tool call with arguments and results, token counts, cost, and latency, all tied to one trace identifier. Critically, log the fully resolved prompt rather than the template, since most confusing agent behaviour turns out to be a difference between the context you intended and the context that was actually assembled. Trace-level cost attribution is what later lets you find the expensive patterns.
How do I debug an agent that behaves inconsistently?
Compare complete traces from a successful run and a failing one. The divergence is usually visible and usually earlier than expected — a tool returning something different, retrieved context that varied, history truncated at a different point. Inconsistency in agents is rarely randomness in the model; it is almost always a difference in the inputs, which is only apparent when you can see both sequences side by side.
Do I need a dedicated agent observability tool?
Not necessarily to start. Structured trace logging into whatever you already run covers a great deal, and building it yourself clarifies what you actually need. Dedicated tools earn their place when you want trace visualisation, eval integration, and cost analysis without building those yourself — which typically becomes worthwhile once several people are debugging agents regularly rather than one person occasionally.