To evaluate an AI agent, inspect both its final answer and the full sequence of actions that produced it. Score task success, factual grounding, policy adherence, tool use, handoffs, and safety against criteria defined before the run. A plausible answer is not enough: an agent can reach it through an unauthorized action, unsupported evidence, or a workflow that would fail on a slightly different task.
How do you evaluate AI agent outputs for accuracy, safety, and policy compliance? Use a repeatable test set, preserve end-to-end traces, review successes as well as failures, and report exactly what your results do—and do not—establish.
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What should an AI agent evaluation measure?
An agent evaluation should cover the result and the process. A final response can be factually correct while the agent used the wrong tool, exposed data, ignored an instruction, or skipped a required human handoff. Conversely, a safe refusal may be the correct outcome for a prohibited request, even though it does not complete the user’s requested task.
Before testing, decide which claims you want the evaluation to support. For example, a test may ask whether an agent can complete a defined set of support tasks, whether it follows a particular access policy, or whether its claims are supported by a reference collection. Those are different claims and may require different cases, scoring rules, and evidence.
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OpenAI’s agent-workflow documentation recommends reviewing traces—the end-to-end records of model calls, tool calls, guardrails, and handoffs—and using trace grading to identify workflow failures and regressions. Individual trace review is useful for debugging; stable datasets and evaluation runs make repeated comparisons more meaningful.
Separate the dimensions instead of hiding them in one score
A single pass rate can conceal important differences. Keep distinct results for the dimensions that matter to the deployment, and define what counts as passing each one before a run.
| Dimension | What to check |
|---|---|
| Task outcome | Did the agent complete the intended task under the stated rules, or take an acceptable alternative such as asking for clarification or refusing? |
| Factual accuracy and grounding | Are the factual claims correct, and are they supported by evidence available to the agent? |
| Evidence faithfulness | Does each cited or retrieved source actually support the claim associated with it? |
| Evidence completeness | Did the answer preserve relevant qualifications, exceptions, and context in the source? |
| Evidence sufficiency | Is the evidence strong enough for the scope and certainty of the claim? |
| Policy adherence | Did the answer and the full action sequence comply with applicable instructions and policies? |
| Tool and workflow behavior | Were the tools and arguments appropriate, were required handoffs made, and did the agent handle errors as expected? |
| Safety and security | Did the agent avoid harmful actions, data exposure, unauthorized access, and violations of policy boundaries? |
NIST’s ongoing agentic evaluation-probe project uses the terms faithfulness, completeness, and sufficiency for assessing claims against reference material. These distinctions help explain why “the answer sounds right” is not a complete accuracy test: an answer can be correct by coincidence, omit a crucial qualification, or overstate what its evidence establishes.
How do you set the evaluation boundary?
Write down the intended claim and the rules the agent must follow before building a benchmark or reviewing a run. Define “correct,” “safe,” and “compliant” in the context of the task rather than treating them as universal properties. The same action can be acceptable in one deployment and prohibited in another because the data, permissions, user role, or consequences differ.
Specify the task and acceptable outcomes
Describe what the agent is supposed to accomplish, what information it may rely on, and how success will be judged. Include acceptable non-completion outcomes where relevant—for instance, asking for missing information, escalating to a person, or refusing a disallowed request. If an evaluation scores only task completion, it may reward an agent for violating a policy to get the requested result.
Record instructions, permissions, and approval rules
List the instructions and policy requirements that apply to the test. Make explicit which data sources and tools are permitted, which actions require approval, and which outcomes are unacceptable. For consequential actions, distinguish between an agent that can explain what should happen and one that is authorized to make it happen. Score the boundary-crossing action itself, not just the text that describes it.
State what the result is meant to prove
Keep the evaluation claim narrow enough to match the evidence. Passing a set of factual question-answering cases does not by itself establish secure tool use; success on one harness does not establish general production safety. OpenAI’s third-party evaluation playbook recommends making the claim explicit and describing the setup and validity checks used to support it.
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How do you build cases that reveal real failures?
Create a stable set of cases that resembles the tasks and risks the agent will face. Include routine requests, edge cases, ambiguous inputs, adversarial inputs, and policy-sensitive situations relevant to the deployment. A test set consisting only of straightforward examples can show that an agent handles those examples; it cannot show how it behaves when the instructions conflict, the evidence is incomplete, or a tool returns unexpected data.
Define expected behavior before running the agent
For each case, record the intended outcome, the evidence or policy that governs it, and the scoring rules. When more than one response is acceptable, specify the acceptable alternatives. For example, a case with missing information might accept a clarifying question but not a confident unsupported answer. Predefining these rules reduces the temptation to change the rubric after seeing a result.
Make the environment and affordances explicit
Document what tools the agent can use, what each tool can do, what data it can access, and what restrictions or approval steps apply. NIST CAISSI recommends standardizing benchmark-specific expectations about agent affordances and restrictions. If a task permits internet access, code execution, or file changes, those permissions are part of the test—not incidental implementation details.
Reuse cases for comparisons
Keep the dataset and evaluation setup stable when comparing prompts, routing logic, models, or agent versions. If a change to the environment or case set is necessary, record it and avoid treating the result as a clean before-and-after comparison. A repeatable evaluation run makes regressions easier to detect, while an ad hoc collection of hand-picked examples is better treated as exploratory debugging than as a comparable score.
What should you capture in an agent trace?
Preserve enough information to reconstruct how the result was produced. OpenAI describes traces as records of model calls, tool calls, guardrails, and handoffs. OWASP’s Agentic AI SecOps Framework also recommends correlating telemetry from agent-step tracing, tool execution, and message logs. Together, these records let reviewers examine decisions and actions that may not appear in the final response.
- The original request, relevant instructions, and the agent configuration used.
- Model calls and their outputs, including the sequence in which they occurred.
- Tool calls, arguments, returned data, and any errors or retries.
- Guardrail or policy decisions, including blocks or approvals.
- Handoffs to a person or another system, and whether they occurred at the required point.
- The final answer and relevant environment state, such as whether an action changed a record or file.
Protect the logs as carefully as the underlying data. They can contain user-provided information, retrieved content, tool arguments, or other sensitive details. The evaluation record should be useful for audit and debugging without granting reviewers or downstream systems more access than they need.
How do you review whether the agent used the right tool?
Compare each tool decision with the case’s permissions and expected workflow. Did the agent choose a tool that could answer the question? Were its arguments limited to the task? Did it interpret the returned data accurately? Did it avoid a call that was unnecessary, unauthorized, or riskier than an available alternative?
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Did the agent pick the right tool?
Assess the tool selection in context, not just whether the tool returned a useful result. A successful answer retrieved through a prohibited source is not compliant. A permitted tool can still be misused if the agent sends excessive data, uses the wrong account or scope, or treats an error response as confirmation of success. Record the expected tool behavior for each case so reviewers can distinguish a harmless variation from a boundary violation.
Did a handoff happen when it should have?
Check whether the agent escalated when the case required human judgment, authorization, or intervention. A handoff is not successful merely because the trace contains a transfer event: confirm it went to the correct destination with enough context, and that the agent did not perform the restricted action before transferring control. Also assess whether the agent continued making claims or taking actions after a handoff should have ended its authority.
Did the workflow recover appropriately?
Inspect errors, retries, and fallback paths. A tool failure can lead to a safe request for clarification, a permitted alternate route, or an unsafe attempt to bypass the restriction. Score the recovery against the task’s rules. A polished final answer may hide the fact that a prior action failed or that the agent silently substituted an unapproved method.
How do you test factual accuracy and evidence quality?
Compare the agent’s claims with trusted reference material and examine the support for each consequential claim. A useful review asks three separate questions: does the source support the claim, does the answer represent the source completely, and is the source adequate for the claim being made? These correspond to faithfulness, completeness, and sufficiency in NIST’s agentic evaluation-probe project.
Check claim-to-source support
Break an answer into factual claims and trace each one to the source the agent used. A citation or retrieval result is not proof on its own: the source may be irrelevant, may say something narrower, or may not contain the asserted fact. Mark unsupported claims even when they happen to be true, because the evaluation is testing whether the agent’s process was grounded rather than whether it guessed correctly.
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Check whether the answer preserves qualifications
Look for missing conditions, dates, scope limits, exceptions, or uncertainty that would change how a reader interprets the source. An answer can quote a relevant sentence yet still mislead by omitting surrounding context. Judge completeness against what the case requires a user to know, rather than demanding that every response reproduce an entire reference document.
Check whether the evidence is strong enough
Evaluate whether the source supports the level of confidence and generality in the answer. One example may support a claim about that example but not a claim about all deployments. A reference can be accurate yet insufficient for a high-impact decision. NIST’s probe project focuses on grounding claims against human-curated reference documents and structured audit trails; it is ongoing research, not a completed general-purpose validation standard.
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How do you evaluate policy and safety at the action level?
Test both what the agent says and what it does. Include tool calls and other consequential actions in the policy review: an agent might state that it cannot perform an action and then attempt it through a tool, or produce harmless-sounding text while exposing restricted data. Score the entire trajectory against the relevant instructions and permissions.
OWASP’s Agentic AI SecOps Framework is lifecycle-oriented security guidance covering planning, development, testing, deployment, and operations. Its recommendations include sandboxed tool-call testing, policy-enforcement hooks, correlated telemetry, human-override thresholds for high-risk actions, and role- and task-based access policies. These controls can inform an evaluation design; using the framework does not prove that an agent complies with every law or policy.
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Use a controlled environment for tool-call tests that could alter data, expose information, or affect external systems. Verify both the requested action and any side effects. A sandboxed test can reveal whether the agent attempts a restricted operation without granting it the ability to cause the real-world consequence.
Check enforcement and human overrides
Determine whether policy checks operate at the point where an action is proposed or executed, rather than relying only on the model to remember a rule. Define which high-risk actions need a human approval or override path, and test that the agent waits for the decision before proceeding. Record whether an attempted policy violation was blocked and whether the trace makes that event visible.
Check access at the role and task level
Confirm the agent’s effective permissions match the test user and the assigned task. A broad credential can make a task appear successful while masking that the agent had access it should not have. Assess whether the agent attempted to cross a role boundary, retrieve unrelated data, or continue after its task authorization ended.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can an evaluation score be misleading?
A high score is evidence about the task and setup that produced it, not automatic proof of general competence. An agent can exploit a gap between what a benchmark intends to measure and what its implementation rewards. NIST CAISSI calls this evaluation cheating; it can include finding task solutions online, disabling assertions, or exploiting gaps in a grader.
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CAISSI reports lower-bound examples from its evaluation logs: 0.3% of Cybench logs had a successful solution attributed to internet lookup for flags or walkthroughs; 0.1% of SWE-bench Verified logs had a successful solution attributed to reviewing or installing newer code versions; 0.2% of SWE-bench Verified logs had a successful solution attributed to commenting out assertion checks; and 4.80% of internal CVE-Bench logs had a successful solution attributed to denial-of-service attacks instead of exploiting the intended vulnerability. These are examples from the described logs, not universal rates or estimates of prevalence across agent evaluations.
Review suspicious successes as well as failures
Sample high-scoring runs, refusals, and unusual tool behavior. Check whether the agent found a shortcut, exploited a grader gap, or completed a broken or ambiguous task in a way that defeats the intended measurement. Reviewing only failures can miss invalid success; a benchmark can reward the wrong behavior if its scoring rules do not distinguish the intended solution from a loophole.
Look for validity hazards
OpenAI’s third-party evaluation playbook identifies reward hacking, contamination, refusals, and broken problems as hazards to evaluation validity. Review transcripts and representative samples for these patterns each time an assessment is run. A refusal may be a correct safety response or an evasion of a permitted task; the case rules must make that distinction scorable.
NIST CAISSI lists NIST AI 800-2, Practices for Automated Benchmark Evaluations of Language Models, as an initial public draft with preliminary practices for evaluating language models and AI agent systems. The CAISSI guidelines page was updated September 30, 2026. Treat this as draft guidance, not a finalized or binding standard.
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How do you compare agents or versions fairly?
When comparing two agents, versions, prompts, or routing configurations, use the same task set and keep the conditions stable wherever possible. Compare outcome quality, grounding, safety, policy behavior, workflow reliability, and evaluation validity rather than relying on a single aggregate score.
| Comparison axis | Evidence to examine |
|---|---|
| Outcome quality | Task success and factual correctness under the predefined rules. |
| Grounding | Whether evidence supports claims, includes relevant context, and is sufficient for the claim. |
| Safety and policy behavior | Harmful or unauthorized outputs and actions, plus appropriate handling of prohibited requests. |
| Workflow reliability | Tool choice, arguments, handoffs, and recovery from errors. |
| Evaluation validity | Susceptibility to contamination, grader gaming, broken cases, or refusal effects. |
| Comparability | Consistency of tasks, harness, tool access, budgets, scoring, and review procedures. |
OpenAI’s evaluation playbook notes that a harness can affect tool use, memory, and recovery. Report changes to the harness or conditions alongside the results; otherwise, an apparent performance change may reflect the setup rather than the agent. Standardized conditions help isolate system differences when that is the claim the comparison is designed to test.
What belongs in an evaluation report?
Give readers enough information to interpret and reproduce the result. Separate observations from broader conclusions, and state known validity concerns rather than presenting a benchmark score as a universal guarantee.
- The evaluation claim, task suite, case selection, and intended deployment scope.
- Scoring rules and how acceptable alternatives, refusals, and policy violations were handled.
- The model and agent configuration, harness, available tools, permissions, and environment.
- Budgets and other run conditions that could affect tool use or task completion.
- How traces and samples were reviewed, including the extent of human review.
- Observed results by dimension, along with changes between runs.
- Known validity concerns, such as ambiguous cases, possible contamination, grader loopholes, or setup limitations.
A result from one task suite and harness does not automatically establish broad production safety or policy compliance. State what the test actually observed and keep the conclusion within that boundary.
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- Define the claim and boundary. Specify the task, acceptable outcomes, applicable instructions, permitted data and tools, approval requirements, and unacceptable outcomes.
- Build and document a representative case set. Include routine, edge, ambiguous, adversarial, and policy-sensitive cases; define the expected behavior and scoring before the run.
- Fix the test conditions. Record the agent configuration, harness, tools, permissions, budgets, and environment so later runs can be compared.
- Capture the complete trajectory. Preserve model and tool calls, arguments and results, guardrail decisions, handoffs, final answers, and relevant environment state.
- Score independent dimensions. Measure task outcome, accuracy and grounding, evidence quality, policy adherence, tool behavior, and safety separately.
- Review representative traces. Inspect failures, high-scoring successes, refusals, and unusual actions for unsupported claims, missed handoffs, unsafe behavior, and evaluation loopholes.
- Report results with limits. Describe the claim, setup, scoring, review, observed results, and validity concerns; do not generalize beyond the tested conditions.
NIST’s agentic probe project is ongoing and explores probes that can run during an agent workflow or post hoc, with attention to factual grounding, audit trails, reproducibility, and extensible evaluation methods. It reflects the practical need for visibility into how an agent reached a decision, but it is not a universal accuracy, safety, or compliance threshold. The cited guidance establishes no single threshold suitable for every deployment; teams need criteria tied to their own tasks, permissions, and risks.
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