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To evaluate an AI agent, test the complete workflow it will perform—not just the underlying model’s answer. Define the decision the evaluation must support, test a documented version of the integrated system on representative tasks and risks, combine methods suited to the question, and report results with their limits. The framework below turns that approach into six repeatable steps.
Why evaluate the agent workflow, not only the model?
An agent can plan across multiple steps, use tools, draw on context or memory, and take actions with varying degrees of autonomy. A model’s answer in isolation cannot show whether the product selected the right tool, followed the right sequence, respected its permissions, recovered from an error, or caused an unintended consequence.
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Test the component that matches the decision. If you are deciding whether to deploy an integrated product, evaluate the model together with its tools, instructions, data sources, permissions, and operating environment. A benchmark result for the model alone may help describe one component, but it is not a substitute for evidence about the product’s behavior.
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1. Define the decision and the claims
Start with the decision: release, procurement, rollout to a particular user group, or ongoing monitoring. Then translate it into claims that can be tested. For example, a team might ask whether an agent completes a defined class of support tasks accurately, whether it stays within its authorized tools, or how it behaves when a tool returns an error.
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Separate intended user outcomes from risks. A system can complete a task while doing so in an unacceptable way, or follow its process correctly without producing a useful outcome. Make clear what evidence would change the decision and what the evaluation is not intended to establish.
2. Specify the system under test
Record enough configuration detail for another team to understand what was tested and, where possible, repeat it. Include:
- Model, agent, and application versions, plus the test date.
- System instructions and relevant configuration.
- Available tools, tool versions, permissions, and action limits.
- Memory and context settings, including what information is supplied during a task.
- Connected data sources and the operating environment.
Changes to any of these may change behavior. Treat a materially changed configuration as a new system under test rather than silently carrying results forward from an older one.
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3. Build representative tasks and risk cases
Describe the task population the results are meant to represent, then sample from it. Include routine cases as well as edge cases, adversarial inputs, long task chains, and situations where a tool fails or returns ambiguous information. If the agent can take consequential actions, include cases that examine whether it respects the intended boundaries.
Document how tasks were selected and which users, settings, or failure conditions are outside the sample. A test set is evidence about the cases it covers; it does not establish performance on every possible task or context.
4. Match the method to the question
No single evaluation method answers every question. NIST’s ARIA program describes model testing, red-teaming, and field testing as distinct levels, with an aim to assess technical and contextual robustness beyond performance and accuracy. The UK AI Safety Institute (AISI) describes automated assessments, red-teaming, and human-uplift evaluations, each serving a different purpose.
| Method | Best suited to | What it can reveal | Important limit |
|---|---|---|---|
| Automated capability assessment | Repeatable checks across a broad set of defined tasks. | Baseline signals about task performance and changes between tested versions. | Results depend on the task set and scoring method; a benchmark score alone does not establish real-world effectiveness or overall safety. |
| Expert red-teaming | Searching deliberately for failure modes, including adversarial or unusual cases. | Ways an agent may be manipulated, misuse tools, or fail under stress. | Findings depend on the probes and effort applied; not finding a failure does not show that none exists. |
| Field testing | Understanding performance in a real or realistic operating context. | Contextual factors, user interaction, and workflow effects that a controlled test may miss. | Conditions may be harder to control and reproduce than in an automated assessment. |
| Human-uplift evaluation | A specific question about whether a system changes a person’s ability to carry out a defined misuse. | Evidence about changes in human capability in that misuse domain. | It addresses a particular misuse question; it is not a general-purpose evaluation for every product. |
Use methods in combination when the decision requires different kinds of evidence. Broad automated testing, targeted red-teaming, and contextual or human-in-the-loop work are complementary, not interchangeable.
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5. Measure outcomes and process evidence
Pair task outcomes with evidence about how the agent reached them. Depending on the product and decision, useful measures may include task completion and quality, correct tool selection and use, unauthorized or harmful actions, recovery after errors, and whether factual claims are grounded in the information available to the agent.
For cited factual claims, NIST’s work on evaluation probes embedded in agent workflows highlights three useful checks:
- Faithfulness: Does the evidence support the claim?
- Completeness: Does the account represent the relevant source information fully?
- Sufficiency: Is the evidence strong enough to carry the claim’s evidentiary burden?
Preserve structured audit trails that connect important agent decisions and claims to source documents. This makes it easier to inspect not just whether an answer passed, but what information supported it and where the process broke down.
6. Report results so they can be reproduced and interpreted
Publish the test scope alongside the result. A useful report preserves the tasks or prompts, scoring rubric, system configuration, test dates, sample sizes where reported, results, uncertainty, and known blind spots. Include the audit trail needed to trace decisions to evidence, subject to appropriate privacy and security protections.
Present a score as a result for a specified system, task set, method, and set of conditions—not as a timeless property of an agent. When configurations change, identify which evidence still applies and which needs to be collected again.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare evaluation evidence
When choosing among evaluation approaches, compare what each one can support rather than ranking methods by a single score. The distinctions used by NIST ARIA and UK AISI provide a practical starting point:
- Workflow realism: Is the test an isolated model task, a simulated agent workflow, or a field setting?
- Breadth and depth: Does it cover many defined cases, or probe a smaller number of risks deeply?
- Repeatability: Can the same tasks, configuration, and scoring be run again to compare versions?
- Failure discovery: Is the method designed to search for unexpected or adversarial behavior?
- Type of robustness: Does it address technical behavior, contextual effects, or both?
- Decision relevance: Is the evidence fit for release, procurement, monitoring, or a narrower question?
For lifecycle decisions, NIST’s voluntary AI Risk Management Framework is intended to support trustworthiness considerations across design, development, use, and evaluation. NIST states that AI RMF 1.0 is being revised, so teams should identify the framework version they use rather than imply that a particular edition is final or mandatory.
What a passing evaluation does—and does not—mean
A passing result means the system met the stated criteria on the tested cases under the recorded conditions. It does not prove that an agent is safe overall or will behave the same way in every setting. The UK AISI explicitly says its evaluations are preliminary, focus on specific safety-relevant capabilities, and are not comprehensive assessments of system safety; it does not aim to designate a system as “safe.” That boundary is important for any evaluation report.
NIST’s CAISSI guidelines page, updated September 30, 2026, lists Practices for Automated Benchmark Evaluations of Language Models as an initial public draft that includes preliminary practices for language model and AI agent evaluations. Its listed March 31, 2026 public-comment deadline has passed. Treat it as draft guidance, not a settled standard. NIST ARIA’s published schedule listed a pilot analysis in February–May 2025 and a summary report in summer 2025; those dates alone do not establish what later program results or publications are available.
A practical presentation outline
- Decision: State what the evaluation will inform and the claims being tested.
- System: Show the exact agent configuration, tools, permissions, data, and environment.
- Tasks and risks: Explain the task population, sample, edge cases, and blind spots.
- Methods: Justify the mix of automated tests, red-teaming, field work, or human-uplift evaluation.
- Measures and evidence: Report outcomes, process checks, and traceable evidence.
- Interpretation: Give results with conditions, uncertainty, limitations, and the decision they support.
This six-step sequence is an editorial synthesis of NIST and UK AISI materials, not an officially endorsed NIST or AISI standard.
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