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Evaluate an AI agent as a complete system, not as a fluent answer generator. Test whether it completes representative tasks, supports important claims with relevant evidence, uses tools within its permissions, handles failure safely, and can be monitored and corrected. Set acceptance criteria for the specific use and consequences of error: NIST and OWASP provide useful evaluation guidance, but neither establishes a universal score that makes every agent trustworthy.
What should an AI-agent evaluation cover?
An agent’s result depends on more than its underlying model. It may plan across steps, retrieve information, call tools, and take actions. A persuasive final answer can hide a bad source, an unauthorized action, or a failure earlier in the workflow. Evaluate complete runs and the controls around them.
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NIST’s AI Risk Management Framework (AI RMF) identifies trustworthiness characteristics to consider throughout design, development, deployment, use, and testing. They are related but distinct; a strong result in one area does not establish trustworthiness in another.
| Evaluation area | What to examine |
|---|---|
| Task performance | Whether the agent completes the intended task, how often it fails, and how serious its errors are. |
| Evidence and traceability | Whether sources support material claims, preserve relevant context, and are strong enough for the conclusions drawn. |
| Tool use and security | Whether tools are selected appropriately, actions remain within authorized scope, and failures do not lead to unsafe behavior. |
| Reliability and resilience | Whether performance holds across relevant conditions and the system responds safely to disruption or unavailable tools. |
| Accountability and interpretability | Whether people can understand what the agent did, review the result, and identify who is responsible for oversight. |
| Privacy and harmful bias | Whether the agent handles data appropriately and creates unfair or otherwise harmful outcomes for affected people. |
| Operational recourse | Whether users and affected people can report problems and seek review or correction. |
Choose the depth of testing according to the task, users, data, permissions, and potential harm. NIST describes its AI RMF as voluntary, context-sensitive risk-management guidance—not a certification or pass/fail test for agents.
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How to evaluate an AI agent before deployment
1. Define the intended use and what failure means
Write down the task and the conditions under which the agent will operate. Include intended users, people affected by its outputs or actions, permitted data, available tools, and the actions it is allowed to take. Describe concrete failure consequences: for example, an incorrect summary may be inconvenient, while an unauthorized change to a record may be costly or harmful.
Use that description to determine which risks need stronger controls and which failures must block deployment. Do not substitute a general claim such as “high accuracy” for criteria tied to the actual task.
2. Build cases that resemble real work
Test complete multi-step tasks, not just isolated prompts or final answers. Include ordinary requests and difficult conditions that reveal whether the agent knows when to proceed, ask for clarification, or stop:
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- Ambiguous instructions or missing information.
- Incomplete, conflicting, or weak evidence.
- Edge cases and requests outside the intended scope.
- Failed, unavailable, or unexpected tool responses.
- Situations where a human should review or approve an action.
Record what was tested, how success and errors were defined, and uncertainty around the results. NIST’s measurement guidance supports quantitative, qualitative, or mixed-method approaches, documentation, uncertainty measures, and comparison with appropriate benchmarks. It does not prescribe a universal test-set size.
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3. Check evidence behind material claims
For claims that matter to a decision, inspect the source rather than relying on the agent’s confidence or the presence of a citation. NIST’s agent-probe work organizes evidence checks around three questions:
- Faithfulness: Does the source actually support the claim?
- Completeness: Does the answer retain relevant meaning and context, rather than selectively quoting or omitting important information?
- Sufficiency: Is the evidence strong enough to justify the claim?
NIST describes prototype probes that compare agent outputs with a human-curated reference corpus and can produce machine-readable audit trails. Those records are material to review, not proof by themselves: check that the source is relevant and that the reasoning follows from it. NIST’s probe project began in April 2026 and is an ongoing research effort, not a general certification or evidence that every probe is production-ready.
4. Test tools, permissions, and security controls
Exercise the integrated application, including orchestration and monitoring—not only the model in isolation. Check whether the agent chooses appropriate tools, stays within authorized scope, and leaves records useful for review. Test how it behaves when a tool fails or returns unexpected information, and whether actions with meaningful consequences receive the required human oversight.
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OWASP’s AI Security Verification Standard (AISVS) is a vendor-neutral catalogue of testable security requirements across the AI lifecycle, including agent orchestration and monitoring. Its version 1.0 page reports 191 requirements across 12 chapters and three appendices; OWASP announced the release in June 2026. Use the requirements that fit the system and its risks as a verification aid, not as proof that a particular deployment is safe.
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- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
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5. Assess human oversight, privacy, and fairness
Review the trust dimensions that matter for the intended deployment: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and management of harmful bias. Ask who can review questionable outcomes, how users or affected people can report problems, and how decisions can be appealed or corrected. NIST’s AI RMF Measure guidance specifically describes feedback processes for end users and impacted communities.
6. Document the decision and continue measuring
Compare observed results with criteria defined for this use case. Document limitations, uncertainty, unresolved hazards, the agent’s permission scope, and the plan for human oversight. NIST’s AI RMF Core Measure guidance states: “AI systems should be tested before their deployment and regularly while in operation.” Where practical, independent review can help identify testing blind spots or conflicts of interest.
Reassess after material changes to the model, tools, prompts, data, or operating context. An evaluation describes performance under the conditions tested; it does not automatically remain valid when those conditions change.
How to compare two or more agents fairly
Run candidates against the same tasks and conditions, then compare more than a headline success rate. A useful comparison records:
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- Task completion and the severity of failures.
- Evidence grounding, including citation faithfulness, completeness, and sufficiency.
- Consistency across representative and difficult cases, with uncertainty documented.
- Tool selection, permission compliance, and behavior when tools fail.
- Security and resilience of the integrated system.
- Transparency, auditability, and how readily a person can review the work.
- Privacy and fairness risks for the intended users and affected population.
- Monitoring, feedback, and recovery arrangements when the agent fails.
These are comparison axes, not a published universal ranking formula. Weight them according to the consequences of error in the intended use; an agent suited to a low-impact drafting task may not be suitable for actions that affect people or important records.
How to decide whether an agent is ready to trust
Make a documented, use-specific deployment decision: identify which criteria the agent met, which remain uncertain, and what safeguards are required. If important risks are unresolved, narrow its permissions, require human approval, limit the use, or defer deployment. No benchmark, checklist, audit trail, or single score settles trust for every context.
The NIST AI RMF was released on January 26, 2023, and NIST’s Resource Center says version 1.0 is being revised. Consult the current NIST materials when using the framework. OWASP AISVS can help turn security concerns into testable requirements, while NIST’s agent probes illustrate ways to inspect evidence and traces; each supports evaluation, but neither replaces judgment about the deployment’s specific risks.
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