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Test an AI agent by checking whether its full workflow achieves an observable goal—not just whether its final message sounds right. Define success, run representative cases more than once, inspect tool calls and handoffs, and combine repeatable pre-release checks with monitoring and human review of live runs.
What counts as evaluating an AI agent?
An agent run is a sequence of actions: it may call tools, pass work between specialists, make intermediate decisions, and change state in another system before producing a final response. A confident claim that an action was completed is not proof that it happened. When possible, evaluate the resulting state—for example, whether the requested record was actually changed—alongside the agent’s explanation.
Anthropic defines an evaluation, or “eval,” as a test that gives an AI system an input and applies grading logic to its output to measure success. For an agent, the output to assess can include its trajectory and effects, not only its last message.
Build an evaluation loop that follows the agent into production
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Define the task and observable success
Describe the task in the user’s terms, then state what evidence would count as success. For an action, prefer a check against the relevant system state. Record important constraints too: what the agent must not do, when it should ask for clarification, and when it should hand off to a person.
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Create a representative, maintained test set
Include domain-expert examples, historical cases, representative production cases, edge conditions, and adversarial inputs. Keep examples used to tune prompts or workflows distinct from held-out checks when you need a fair comparison. There is no universal split ratio established for every agent; the set should reflect the product’s tasks and risks.
When a production trace reveals a meaningful failure or a new behavior, turn it into a test case where appropriate. Review cases over time: product requirements change, some tasks become ambiguous, and valid solutions may take more than one form.
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Grade the outcome and the path
Use deterministic assertions when a result or rule can be checked exactly. Add rubric-based or model-assisted grading for qualities that cannot be reduced to an exact match, and calibrate those graders against human judgment. Check the final task outcome as well as relevant tool choices, handoffs, instruction adherence, and policy behavior.
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Repeat important cases and compare versions
Generative AI is variable: the same input can produce different outputs. OpenAI makes this point in its “Evaluation best practices” documentation and cautions that traditional software testing alone is insufficient for AI systems. Run multiple trials for important cases, and examine the distribution of outcomes or the distinct failure modes—not just one pass or fail.
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Keep the system configuration associated with each run, including the relevant prompt, model, tools, routing, and policies. Re-run the same suite when one of those changes so that comparisons are meaningful. A small or saturated benchmark can conceal regressions or make improvement look larger than it is.
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Gate changes, then watch live behavior
Automate repeatable evaluations around changes where that fits your release process. After deployment, monitor task outcomes and other signals tied to your product requirements. When a signal moves, inspect traces to find where the workflow went wrong; sample runs for human review, collect user feedback, and add confirmed novel failures to the maintained test set.
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Assign ownership
Give a product or engineering owner responsibility for the suite, and make it practical for domain experts to contribute cases. Review graders and test cases when requirements change, when reviewers disagree, or when the agent finds a valid solution the rubric wrongly rejects.
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Measure the workflow at three levels
Choose measures from the task’s requirements rather than adopting a generic scorecard. A useful evaluation separates the end result from the local decisions that produced it and from patterns across many runs.
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| Level | What to inspect | Questions it answers |
|---|---|---|
| Task outcome | Whether the user’s goal was achieved; whether relevant state changed correctly; answer relevance or factuality when applicable. | Did the task succeed, and was the result usable? |
| Trace or step | Tool selection and call correctness, handoff behavior, instruction and safety-policy adherence, and whether a specialist supplied trustworthy evidence. | Where did this run go wrong, and what should the agent have done instead? |
| Workflow or population | Patterns across runs, such as repeated tool errors or failures concentrated around a particular handoff or task type. | Is this an isolated miss or a systemic orchestration, tool, or policy problem? |
For retrieval-augmented generation, retrieval precision and recall may matter. Latency and cost can be operational constraints, while user ratings or expert review can assess qualities that automatic checks miss. None of these measures has a universally correct production threshold: set thresholds based on the product’s requirements, risks, and acceptable trade-offs.
Choose graders that fit the claim being tested
- Exact assertions: Use them for observable facts such as a required field value, a permitted tool, or a forbidden action. They are usually easier to reproduce than subjective judgments.
- Rubrics: Define what acceptable performance looks like for qualities such as relevance or completeness. Make room for valid alternate answers rather than requiring identical wording.
- Model-based graders: Use them where flexible interpretation helps, but inspect their judgments. An LLM judge can be wrong, and a generic metric or unrepresentative test set can create false confidence.
- Human review: Use expert reviewers to calibrate graders, examine disagreements, and assess sampled production traces. Feedback should be systematic enough to reveal recurring patterns, not limited to anecdotes.
OpenAI’s evaluation guidance emphasizes representative data and careful judgment rather than reliance on generic metrics. LangSmith’s documentation likewise notes that LLM judges can be wrong and describes routing samples to human reviewers. Treat an automated grade as evidence to validate, not as ground truth by default.
Use production traces to find what the test set missed
Offline tests make comparisons repeatable, but they cannot represent every real user, context, or interaction. Production monitoring, user feedback, A/B testing, transcript review, and systematic human evaluation add evidence about how the deployed system behaves. A trace can show whether the agent selected the wrong tool, made an inappropriate handoff, or violated a policy even when its final response looks plausible.
Look across traces as well as within them. One failed specialist check may be local; repeated failures around the same tool or transition can indicate a workflow-level issue. OpenAI’s macro-evaluation cookbook illustrates aggregating local signals to identify patterns at workflow or population scale.
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Where evaluation tools fit
Evaluation platforms can help capture traces, maintain datasets, run comparisons, review samples, or connect checks to release workflows. The following are examples described in their publishers’ or project documentation, not independently benchmarked recommendations.
| Example | Documented role | Useful consideration |
|---|---|---|
| OpenAI agent-evaluation workflow | Trace grading, datasets, and evaluation runs. | Useful for inspecting workflow behavior and repeatable comparisons. |
| LangSmith | Offline dataset tests, online evaluation, human feedback, trajectory evaluation, and CI integrations. | Its documentation describes framework-agnostic use; check the integration and operational details for your stack. |
| Arize Phoenix and Arize AX | Phoenix documentation describes tracing, evaluations, datasets, and experiments in a self-hosted open-source project; Arize AX is described as a managed alternative. | Consider self-hosting, data governance, and the operational work each deployment model requires. |
| Braintrust | Anthropic describes offline evaluation, production observability, experiment tracking, and prebuilt scorers. | Check whether its workflow and evaluator controls suit your evaluation tasks. |
| Langfuse | Anthropic describes it as an open-source, self-hosted alternative relevant to data-residency needs. | Confirm current capabilities and implementation details against your requirements. |
| Promptfoo | An OpenAI cookbook example uses it for lower-level trace checks across specialist agents, policy constraints, and handoffs. | It can be one layer in a workflow; it does not replace well-designed tasks and grading logic. |
Compare options against the same representative workload. Trace depth, repeatable offline tests, online monitoring, human-review and grader-calibration support, framework and provider compatibility, data residency, and CI/CD fit are practical decision axes. A simple in-house script can be a reasonable starting point; a framework is only useful to the extent that its tasks and graders are sound.
One time-sensitive platform detail: the OpenAI “Evaluation best practices” page reviewed on October 7, 2026 stated that the Evals platform would become read-only for existing users on October 31, 2026 and was scheduled to shut down on November 30, 2026. That statement concerns the Evals platform; it does not establish that every API surface or agent-evaluation workflow follows the same lifecycle.
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