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What does it mean to evaluate an AI agent?
An agent run is a sequence of actions, not merely a response. It may involve model calls, tool calls, handoffs between specialists, intermediate decisions, and changes to an external system. A polished final message is not proof that the agent actually did what the user asked.
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Anthropic’s engineering article “Demystifying evals for AI agents,” published January 9, 2026, defines an evaluation as a test that gives an AI input and applies grading logic to its output. For an agent, the useful unit to inspect is often the full trajectory: what it did along the way, as well as the result.
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For example, a support agent asked to cancel an order should be checked for whether it identified the correct order, used the permitted cancellation action, and left the order in a canceled state. A message saying “Your order is canceled” is not sufficient if the action failed or applied to the wrong order.
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Build an evaluation loop around observable success
1. Define the task and its success condition
Write tasks in the user’s terms, then define success in a way you can observe. For actions, inspect the resulting system state when possible; do not grade solely on the agent’s claim. Record valid alternate ways to complete the task so the evaluator does not reject correct behavior just because it differs from one preferred path.
2. Assemble a representative test set
Use a mix of domain-expert examples, historical or sampled production cases, edge conditions, and adversarial inputs. Include ordinary tasks as well as cases that test ambiguity, missing information, tool failure, and policy boundaries. Keep examples used to tune prompts or other system behavior distinct from held-out checks when you need a fair comparison. There is no universal split ratio established for every agent.
Give domain experts a practical way to contribute cases. When a production failure reveals a new behavior or an overlooked scenario, turn it into a test after confirming what went wrong.
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3. Grade outcomes and the path taken
Use deterministic assertions for things that can be checked exactly, such as whether a requested record changed state or a prohibited action occurred. Use rubric-based grading, pairwise preference, or human review for qualities that resist exact matching, such as relevance or whether an explanation is useful. Calibrate automated graders against expert judgment: model-based judges can be wrong, too.
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Evaluate at more than one level:
- Task outcome: Did the agent accomplish the requested task, and is the resulting state correct?
- Trace or trajectory: Did it select the right tool, pass appropriate information, and hand work to the right specialist?
- Policy and safety: Did it follow instructions and guardrails, including when the request was adversarial or the answer was uncertain?
- Answer quality: Where a response matters, was it relevant, factual, and faithful to available evidence?
- Operational constraints: Were latency and cost acceptable for the product’s requirements?
For retrieval-augmented agents, consider retrieval precision and recall. For multi-agent workflows, check whether each specialist supplied trustworthy evidence and whether the overall system used it appropriately. No single metric set or production threshold fits every agent; choose measures based on the product’s requirements.
4. Repeat important cases and compare versions
Agent behavior can vary across runs, even with the same input. OpenAI’s “Evaluation best practices” documentation notes that generative AI may produce different outputs from the same input, which makes traditional software-testing methods insufficient on their own. Anthropic describes a trial as one attempt and recommends repeated trials to improve consistency of evaluation results.
Run important cases more than once and report the pattern—not just one pass or fail. For example, note whether a case succeeded on every run or exposed an intermittent tool-selection problem. Preserve the model, prompt, tools, routing, and relevant configuration used for each run so comparisons remain interpretable. A small or saturated benchmark can create false confidence, so treat its scope as part of the result.
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Run the same maintained suite when prompts, models, tools, routing, or policies change. Automate checks in your release process where practical, and investigate regressions before shipping. A passing offline suite is not a complete picture of deployed quality: production traffic can expose new inputs, shifting patterns, and failures the test set never anticipated.
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Monitor online quality signals, collect user feedback, sample traces for human review, and investigate when measures move. Turn confirmed incidents and novel production behaviors into new evaluation cases. Keep an owner responsible for reviewing ambiguous tasks, unfair graders, valid alternate solutions, and changes in product requirements.
Use traces to diagnose why a run failed
Task-level scores tell you whether the result was acceptable; trace-level review helps identify the cause. A failed specialist check may be an isolated symptom. Similar failures across traces can point to a recurring problem in tool choice, handoffs, orchestration, or business policy.
Inspect the steps around a failure: the information available to the agent, the tool it selected, the tool’s response, any handoff, and the final environment state. OpenAI’s agent-evaluation workflow describes trace grading for workflow debugging alongside datasets and evaluation runs for repeatable comparisons. Its macro-evaluation cookbook illustrates grouping local agent signals to identify patterns across workflows or populations.
Choose measures and evaluation methods that fit the task
Combine methods rather than expecting one grader to answer every question. Exact assertions are useful when outcomes or rules are observable. Rubrics and model-based grading can assess qualities that need judgment, but should be checked against human evaluations. Pairwise preference can help compare two versions when a single absolute score is hard to define. Manual transcript review can reveal problems that your existing rubric does not capture.
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OpenAI’s evaluation guidance cautions against generic metrics, unrepresentative data, and uncalibrated judgments. LangSmith also notes that LLM judges can be mistaken and describes routing samples to human reviewers. Treat automated scores as evidence to examine, not unquestionable ground truth.
How evaluation tools fit into the workflow
Tooling can help collect traces, manage datasets, run comparisons, support human review, and fit checks into CI/CD. The following are examples described in the named vendors’ own materials, not independent performance rankings:
| Example | Described use | What to verify for your workload |
|---|---|---|
| OpenAI agent-evaluation workflow | Trace grading for workflow debugging; datasets and evaluation runs for repeatable comparison. | Whether the trace detail, evaluators, and release workflow fit your implementation. |
| LangSmith | Offline dataset tests, online production evaluation, human feedback, trajectory evaluation, and CI integrations. | Framework compatibility, data handling, grader control, and current plan details. |
| Arize Phoenix and Arize AX | Project documentation describes self-hosted Phoenix for tracing, evaluations, datasets, and experiments, and AX as a managed alternative. | Deployment and governance requirements, integration effort, and current implementation details. |
| Braintrust | Anthropic describes offline evaluation, production observability, experiment tracking, and prebuilt scorers. | Whether its tracing and scoring approach suits the tasks and evidence you need to inspect. |
| Langfuse | Anthropic describes it as an open-source, self-hosted alternative relevant to data-residency needs. | Current capabilities, deployment responsibilities, and data-residency fit. |
| Promptfoo | An OpenAI cookbook example uses it as a lower-level layer for trace checks across specialist agents, policy constraints, and handoffs. | Whether its checks cover the failure modes your agent actually has. |
A simple in-house script can be a reasonable starting point. Anthropic cautions that an evaluation framework is only as useful as the quality of the tasks and graders supplied to it. Compare options against a representative workload, privacy constraints, instrumentation needs, evaluator control, and budget; vendor descriptions are not independent benchmarks.
OpenAI Evals platform timing to account for
OpenAI’s “Evaluation best practices” documentation, as reviewed on October 7, 2026, states that the Evals platform will become read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. Those dates concern the Evals platform and should not be generalized to every API surface: the broader agent-evaluation guidance discusses traces, graders, datasets, and evaluation runs. Check OpenAI’s current deprecation information before relying on a specific surface for a release workflow.
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