Evaluate the prediction and the agent that acts on it. Start by defining the task, decision, users, and operating conditions; then choose representative tests and task-appropriate metrics, estimate uncertainty, exercise the full agent workflow, and plan for monitoring after deployment. A benchmark score describes performance on a defined test—it does not by itself establish how reliably an agent will behave on future cases.
Define what the evaluation needs to establish
Before choosing a metric or benchmark, write down the prediction task and the decision the result supports. The same model can be adequate for one use and unsuitable for another if the consequences of an error or the conditions of use differ.
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- Prediction: What does the model predict, and at what point in the workflow?
- Consumer and action: Does a person or another software component receive the prediction? What action can the agent take because of it?
- Error costs: What happens after a false positive, a false negative, or an uncertain prediction? Are the costs symmetric?
- Operating context: What inputs, tools, external data, users, and constraints will be present? What might change at inference time?
- Evaluation claim: Is the aim to compare systems on a fixed suite, estimate performance on future cases, decide release readiness, discover risks, or monitor a live deployment?
NIST AI 800-2, an initial public draft issued in January 2026, puts objective definition before benchmark selection and evaluation. Its scope is automated benchmarking of language and similar general-purpose text-output models, while noting applicability to models embedded in agents and some other behavioral properties. It states: “Not all evaluation objectives can be met by automated benchmark evaluations.”
Choose a test design that fits the task
Automated benchmarks are most useful when a task can be represented as discrete examples with known or automatically verifiable outcomes, and when those examples remain relevant to the intended use. A benchmark that measures one objective should not be presented as proof of objectives it does not measure.
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When an automated benchmark fits
Use a fixed test set to measure defined outcomes under a repeatable protocol—for example, whether predictions match labels on a specified collection of cases. Preserve the benchmark version and scoring rules so comparisons can be reproduced.
When additional methods are needed
Subjective outcomes, rapidly changing tasks, and interactions with operators or users may not be captured by automated scores alone. Depending on the objective, complement the benchmark with human assessment, red teaming, user studies, field testing, or post-deployment monitoring. NIST AI 800-2 identifies these as methods for objectives that automated benchmarking cannot fully address.
NIST’s ARIA materials likewise frame evaluation as a combination of model testing, red teaming, and user testing. The September 18, 2026 ARIA Evaluation Planning Manual provides a planning approach, not a universal certification checklist; the ARIA overview also describes field testing and technical and contextual robustness.
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Build a representative and trustworthy measurement set
A score is only as relevant as the examples and measurement process behind it. Explain how cases were selected and why they reflect the deployment population, including the range of inputs and conditions the agent is expected to face.
- Check data availability, accuracy, representativeness, and suitability for the stated use.
- Verify that labels or outcomes are reliable and that the evaluation instrument measures the intended construct rather than a convenient proxy.
- Include domain experts and relevant stakeholders, including people affected by the system’s outcomes, where appropriate.
- Protect held-out test data from leakage into training, prompt development, tuning, or repeated informal experimentation.
- Document sampling, exclusions, labeling, and protocol changes so another evaluator can understand what the results cover.
OECD guidance emphasizes evaluation design, data collection and selection, trustworthiness, and construct validation. Those checks matter particularly when a benchmark’s labels or tasks are only an approximation of the real decision.
Choose metrics that match the prediction and decision
There is no universal metric bundle. Select measures based on what the prediction represents and how the agent or its operator will use it. NIST AI 800-3 says, “There is no one-size-fits-all formula for quantifying AI performance in an evaluation.”
| Prediction or decision use | Useful metric family | What to examine |
|---|---|---|
| Ordering or prioritizing cases | Discrimination or ranking measures | Whether higher-priority cases tend to rank above lower-priority ones, and whether the ranking serves the actual decision. |
| Forecasting probabilities | Calibration and proper probabilistic scores | Whether stated probabilities correspond to observed frequencies and whether probability quality is appropriate for the decision. |
| Predicting numeric values | Error measures | How far predictions are from observed values, with attention to the consequences of errors at different magnitudes. |
These are examples of metric families, not a prescribed checklist. Report the result alongside the evaluation sample, population or subgroup scope, protocol, and assumptions. Where uncertainty can be estimated, report it with the score rather than presenting a point estimate as exact.
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Separate benchmark performance from expected future performance
A score on fixed benchmark items and an estimate of performance on a wider population of future tasks answer different questions. The fixed-set score describes the tested cases under the stated protocol. A generalization estimate depends on assumptions about how those cases relate to future ones and should be reported separately, with its uncertainty.
NIST AI 800-3 distinguishes benchmark accuracy from generalized accuracy and discusses statistical modeling as a way to estimate uncertainty about generalized performance. Its 2026 report abstract describes an evaluation of 22 API-access frontier large language models on 3 popular benchmarks; that is the scale of that particular study, not a count of all available models or benchmarks.
Test the agent as a complete system
A predictive model can perform well in isolation yet contribute to a poor agent outcome if the system misreads, ignores, or over-trusts its output. Run evaluations in the actual agent loop, using the intended configuration and the same kinds of resources available in deployment.
- Include prompts, retrieval or other external data, tools, retries, and handoffs.
- Test how the agent consumes the prediction, including what it does when the output is missing, malformed, low-confidence, or inconsistent with other evidence.
- Assess downstream actions as well as prediction correctness: a locally accurate result can still lead to a harmful system-level decision.
- Include human oversight where it is part of the workflow, and test whether escalation happens when intended.
- Use red teaming and user testing to reveal failures that a fixed predictive benchmark may not expose; use field evaluation when realistic context is important.
Probe robustness, security, and impact
Average performance does not show how the model and agent behave under variation or attack. Define plausible cases from the system’s actual inputs, access levels, and deployment risks, then test them in the complete workflow.
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- Exercise tool failures, unavailable external sources, and unexpected but plausible use.
- Test adversarial examples and attack paths appropriate to the system’s interfaces and threat model.
- Assess privacy, data governance, security, and adverse-impact risks where they apply.
- Review subgroup behavior when justified by the use case and available data; aggregate results can conceal harms to affected groups.
OECD guidance highlights data suitability and construct validity, human oversight, relevant experts and stakeholders, adversarial robustness and security, and monitoring. NIST ARIA’s focus on technical and contextual robustness supports evaluating both system behavior and the conditions in which it is used. Some risks require domain experts and affected stakeholders to assess; a single aggregate metric cannot resolve them.
Compare models on a common basis
Model comparisons are meaningful only when the systems face the same task definition, data and time window, agent configuration, tool access, and scoring protocol. Do not treat scores from different tasks or evaluation settings as directly comparable.
- Compare fixed-set predictive results with uncertainty.
- Keep any estimate of performance beyond the fixed set separate, stating its assumptions and uncertainty.
- Compare calibration or error behavior relevant to the decision, not only aggregate accuracy.
- Compare robustness under realistic variation and adversarial conditions.
- Compare system-level task success, tool use, escalation, and human-oversight behavior.
- Consider justified subgroup performance, reproducibility, operational constraints, and monitoring or mitigation needs.
NIST AI 800-3’s distinction between benchmark and generalized accuracy is important here: two systems’ benchmark scores do not by themselves settle which will perform better across future tasks or in a particular agent deployment.
Make results reproducible and keep evaluating after launch
A useful evaluation report lets another person understand what was tested, how the score was produced, and what the result does not establish. Record dataset sources and selection, benchmark version, software and configuration, execution steps, scoring rules, statistical analysis, uncertainty, deviations, and known limitations. Qualify conclusions to the measured population and operating conditions.
For production, define what behavior to monitor, what thresholds or signals prompt investigation, and what mitigation actions are available. Monitor for drift, incidents, and changes in the system or its context. Revisit the evaluation when the model, agent configuration, data, tools, intended use, or operating environment changes. NIST AI 800-2 treats field testing and post-deployment monitoring as complements to benchmarks, and OECD guidance calls for monitoring elements as part of evaluation practice.
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