The Tool Desk
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Agent traces show what happened in a run; evaluations judge whether that behavior met criteria you chose. If you can inspect agent activity but cannot tell whether a change made the system better, add explicit graders and repeatable test cases. Tracing helps you diagnose behavior, while evaluation lets you assess it across selected scenarios—not guarantee success outside them.
What observability tells you—and what it cannot
A trace is a record of an observed run. OpenAI’s tracing documentation says, “The tracing dashboard shows what your agent did, including each step’s recorded inputs, outputs, duration, and status.” That record can help locate a relevant model response, tool call, handoff, or final output.
But a sequence of actions is not a quality verdict. A trace can show that an agent called a tool and returned an answer; it does not, by itself, establish that the tool was appropriate or that the answer completed the user’s task. Evaluation adds criteria for judging those decisions. OpenAI describes trace grading as a way to assess workflow-level behavior and identify issues: OpenAI trace grading guide.
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How to evaluate an AI agent
Use traces to discover and diagnose behavior, then turn important scenarios into repeatable checks. OpenAI’s agent evaluation documentation puts it this way: “The OpenAI Platform offers a suite of evaluation tools to help you ensure your agents perform consistently and accurately.” The platform’s tools are one implementation; the underlying practice does not require a particular vendor.
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Inspect a representative trace
Choose a run that reflects a meaningful task or failure. Follow the relevant model call, tool call, handoff, and output to understand what occurred. Look for the point where behavior diverged from the intended workflow rather than treating the final response as the only evidence.
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Define what “good” means
Write criteria tied to the task. Depending on the agent, these might include choosing the correct tool, handing off when appropriate, following instructions, or completing the user’s goal. Make the criteria specific enough that two reviewers—or a grader and a reviewer—can explain why a result passed or failed.
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Build a dataset from important scenarios
Convert routine tasks and known failure modes into cases the team can run again. Include edge cases that matter to users, not just examples that make the agent look successful. A dataset makes comparisons possible because the same selected cases can be evaluated after a change.
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Run the cases after meaningful changes
When you change a prompt, model, routing logic, or tool behavior, evaluate against the same cases and inspect the results. OpenAI documents dataset-based evaluation runs for this repeatable workflow: OpenAI evaluation guide. A result is useful for comparing versions against your chosen tests; it is not proof that every real-world interaction will work.
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Investigate failures and disagreements
Review failed cases and cases where a grader’s judgment is uncertain or conflicts with human review. Refine criteria when they do not reflect the task, and add newly discovered, representative failures to the dataset. Update the cases as the product and its risks change.
Choose an evaluation method that matches the question
The evaluation unit might be one response, a full agent trace, or a multi-turn thread. The assessment could use deterministic checks, reference answers, structured graders, or human review. These approaches answer different questions; a simple assertion may suit a fixed requirement, while workflow quality or ambiguous responses may need a grader or reviewer.
When assessing an evaluation setup, consider whether it supports rerunning the same cases, reviewing enough trace detail to diagnose failures, and fitting into the team’s release or monitoring process. OpenAI documents trace grading and dataset-based evaluation; LangChain describes its own observability and evaluation capabilities in LangSmith documentation. Those vendor pages describe their respective products, not an independent comparison or a requirement to use either platform: LangSmith documentation.
Why passing results have a limit
An evaluation measures only the dimensions defined by its graders and the situations represented in its dataset. A high score therefore supports a narrow conclusion: the agent performed well on those cases under those criteria. It does not establish that the agent will handle unseen requests, new tools, or changed user behavior reliably.
Aggregate scores can also conceal individual failures or grader disagreement. Keep the cases and criteria visible, examine examples behind the score, and treat evaluation as evidence for a decision rather than a complete account of user experience.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How common are agent evaluations?
In its 2026 State of Agent Engineering article, LangChain reported 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 article’s reported results do not establish sample size or methodology here, so they should not be read as independently validated or representative of all organizations: LangChain State of Agent Engineering.
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