Observability shows what an AI agent did in a particular run; evaluation tests whether it met criteria you set, across cases you can repeat. If you can inspect traces but cannot tell whether a change improved your agent, the missing piece is a repeatable evaluation practice: define what good behavior means, test representative scenarios, and use traces to diagnose failures.
What observability tells you—and what it cannot
A trace is a record of an observed run. Depending on the instrumentation, it can expose inputs and outputs, duration, status, model responses, tool calls, and workflow steps. OpenAI’s tracing documentation says, “The tracing dashboard shows what your agent did, including each step’s recorded inputs, outputs, duration, and status.” OpenAI’s tracing guide describes this as a way to inspect agent activity.
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That record helps answer questions such as which tool the agent called, where a handoff occurred, or what output followed a model call. It does not, by itself, establish whether the agent completed the user’s task correctly. A sequence of visible actions is evidence of behavior, not a quality verdict. To judge quality, your team must specify the intended outcome and the criteria that count as success.
What an evaluation adds
An evaluation applies chosen criteria to selected examples. The unit might be one run, a full trace or workflow, or a multi-turn conversation. Assessment might use deterministic assertions or reference answers, structured graders, or human review. The right combination depends on what can be judged reliably for your task.
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OpenAI’s agent evaluation documentation describes evaluations as a way to assess agent performance against criteria. It states: “The OpenAI Platform offers a suite of evaluation tools to help you ensure your agents perform consistently and accurately.” That is a description of OpenAI’s tools; the underlying practice does not require a particular platform. A structured trace grader can judge workflow-level behavior, while a dataset-based evaluation can apply selected checks to a set of cases. OpenAI’s evaluation guide covers these approaches.
The key distinction is what each practice answers: tracing helps explain what happened in a run, while evaluation helps assess behavior against your chosen criteria across one or more cases. Used together, traces can reveal and explain problems; evaluations can show whether those problems recur after a change.
A practical workflow for evaluating an agent
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Inspect a representative trace
Choose an observed run that illustrates normal behavior or a meaningful failure. Follow the relevant model call, tool call, handoff, and final output. Identify where the behavior diverged from what the task required. Trace inspection is especially useful for diagnosing workflow-level issues, but one trace should not stand in for a broader assessment.
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Define what “good” means for the task
Write criteria that can be applied consistently. Depending on the agent, these might include choosing an appropriate tool, handing off when necessary, following instructions, or completing the user’s goal. Make the criteria specific enough that a grader or reviewer can distinguish success from failure; “the answer seems good” is not a dependable test.
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Build a dataset from meaningful scenarios
Turn important situations into test cases. Include routine requests as well as known failure modes and edge cases, with whatever inputs and expected outcomes or grading criteria your method requires. Traces are a useful source of real scenarios: when a run exposes a failure worth guarding against, add a case that captures it.
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Rerun cases after changes
Run the same dataset after meaningful changes to prompts, routing, models, or tools. Compare results against the same criteria so you can see whether particular cases improved or regressed, rather than relying on a few remembered runs. OpenAI describes moving from trace inspection toward datasets and evaluation runs when repeatability is needed.
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Investigate failures and revise the suite
Read failed cases and inspect their traces to understand what happened. Review cases where graders disagree or where a score conflicts with human judgment. Add cases when product behavior or known risks change, and revisit criteria that no longer represent the task. Evaluation is a continuing feedback loop, not a one-time certification.
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How to interpret evaluation results
A passing result supports only a bounded claim: the agent met the criteria applied to the cases tested, under the conditions of that run. It does not show that the agent will succeed on unseen situations, nor that the criteria capture every part of a user’s experience. A high aggregate score can also conceal a specific failure that matters, so inspect case-level results and the underlying traces.
For the same reason, treat grader output as evidence rather than ground truth. Deterministic checks work where outcomes can be stated precisely; structured graders can assess more nuanced behavior but still reflect the rubric they were given; human review can help resolve ambiguity. Review the dimensions each method covers and keep meaningful failures in the dataset.
Why observability and evaluation belong together
Observability without evaluation can leave a team with detailed explanations of individual runs but no consistent way to judge progress across changes. Evaluation without useful traces can flag a failure without making its cause clear. Pairing them creates a practical cycle: inspect behavior, define criteria, preserve important scenarios, rerun after changes, and use traces to diagnose what the results reveal.
OpenAI’s documentation describes its own evaluation and tracing tools; these examples do not establish a requirement to use a specific vendor. The transferable approach is to retain enough run detail to investigate failures and to test the behaviors that matter against repeatable cases.
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LangChain reported in its 2026 State of Agent Engineering survey article that 89% of organizations had implemented observability, 52% ran offline evaluations on test sets, and 37% ran online evaluations. These are vendor-reported survey figures; the available reporting does not state the sample size or methodology, so they should not be treated as independently validated or representative of all organizations. They illustrate a reported gap between adopting observability and running evaluations, not a universal benchmark. LangChain’s survey article provides the figures.
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