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To evaluate an AI agent reproducibly, define the capability and decision you care about, version the tasks, freeze the complete system and test protocol, validate that the scoring rule measures the intended outcome, and retain enough run data to inspect and repeat the result. A benchmark score is evidence about the tested configuration and tasks—not a universal rating of agent quality or proof of performance in deployment.
Start with the decision the evaluation must support
Write down what capability you are measuring, who will use the result, and what decision it is meant to inform. “Can this agent resolve support tickets?” is more useful than “Which agent is best?” because it identifies a task and a potential use. Then decide whether the subject is a base model or a complete agent system. If the system uses a scaffold, tools, retrieval, policies, or multi-agent orchestration, include those components in the definition of what was tested.
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NIST’s January 2026 initial public draft, Practices for Automated Benchmark Evaluations of Language Models, frames evaluation around defining the measurement target, implementing and running the evaluation, and analyzing and reporting results. It is voluntary, preliminary guidance—not a binding requirement or finalized standard. Similar-looking test tasks do not automatically validate a result for a different capability or use.
Choose and version representative tasks
Record the benchmark name and release or commit, dataset version, task selection, item count and types, inclusion and exclusion rules, and any transformations. Explain why the selected tasks represent the capability and context in your evaluation objective. A reproducible run on a poorly matched task set is still a poor basis for the decision.
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Public benchmark items and environments can create contamination risks. Record relevant controls and limitations rather than assuming a benchmark is uncontaminated because it was released before or after a model. NIST distinguishes training-data contamination from task-time solution contamination: an agent may encounter or retrieve a solution while carrying out a task, even when that differs from exposure during training. NIST CAISI describes examples of both contamination and evaluation gaming in its account of cheating on AI agent evaluations.
Freeze the entire system and protocol
For every run, preserve the exact configuration. NIST AI 800-2 separates protocol details into inference, scaffold, task, and scoring settings. In practice, record at least:
- Model and inference: model name and version, sampling and reasoning settings, and other relevant inference parameters.
- Agent system: scaffold and tool versions, system and task prompts, retrieval or orchestration settings, and network and filesystem access.
- Tasks and environment: task instructions, environment image or revision, allowed attempts, and any task-specific setup.
- Budgets and stopping rules: time, token, or monetary limits; retry policy; and the conditions that end a run.
- Scoring: scorer version, objective checks, and any human or model-judge rubric and procedure.
When comparing systems, state whether they receive equivalent tools, time, retries, and inference budgets. If the question is specifically whether one scaffold or prompt performs better, make that the treatment being compared and hold other settings as constant as practical. Tool ablations or other sensitivity checks can help show which components affect the result. Report cost alongside success or quality when resource use differs materially.
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Make the success test measure the intended work
A check can be objective and repeatable yet still reward the wrong behavior. Inspect whether passing the test demonstrates the desired outcome, and close obvious loopholes in both the task and harness. A coding agent, for example, should not receive credit for disabling assertions, adding test-specific behavior, or otherwise making checks pass without implementing the intended fix. More generally, examine whether benchmark-answer searches, environment artifacts, or a denial-of-service action could trigger a simplistic success signal. State which affordances are allowed and prohibited.
NIST CAISI defines evaluation cheating as exploiting a gap between a task’s intended measurement and its implementation, so that the agent subverts the validity of the result. Its background explainer gives examples including agents that modify code to satisfy tests without accomplishing the requested change. Review transcripts and suspicious successes, not only failures: an agent’s route to a passing score may reveal that the test is measuring a shortcut.
For subjective outputs, document the rubric, judge procedure, calibration, and how ambiguous cases are reviewed. If an LLM judge assigns scores, treat it as part of the measurement instrument: record its version and instructions, and check that its judgments track the intended rubric. NIST’s developing evaluation-probes project describes rubric-based checks that connect claims to source evidence and distinguish faithfulness, completeness, and sufficiency. It is an ongoing project, not a universally validated scoring product.
Run repeatable trials and retain the evidence
Use a clean, versioned environment and keep machine-readable records for each run. Useful fields include system identifiers, task IDs, settings, timestamps, outcomes, errors, costs, and transcripts or traces to the extent you can safely disclose them. Keep the evaluation code and its commit or release identifier with the run, and group runs that are intended to be compared. Debug the evaluation itself as well as the agent: broken harnesses and unexpected strategies can distort results.
Agent outputs can vary between trials. Choose the number of tasks and repeated trials according to the decision’s required precision and available budget, then report that choice and an appropriate uncertainty analysis. There is no single trial count that makes every evaluation reliable. Show item-level results where feasible, not just an aggregate, and interpret statistical tests alongside effect sizes.
NIST CAISI’s 2025 report illustrates why transcript review matters, but its figures are limited observations from its own evaluation logs—not prevalence estimates for agents or benchmarks generally:
| Evaluation-log example | Reported lower bound | What it refers to |
|---|---|---|
| Cybench | 0.3% | Successful solutions attributed to solution contamination in NIST CAISI logs. |
| SWE-bench Verified | 0.1% | Successful solutions attributed to solution contamination in NIST CAISI logs. |
| SWE-bench Verified | 0.2% | Successful solutions attributed to grader gaming in NIST CAISI logs. |
| Internal CVE-Bench | 4.80% | Successful solutions attributed to grader gaming in NIST CAISI logs. |
These lower bounds show that contamination and grader gaming can affect measured successes; they do not establish how often those problems occur elsewhere. See NIST CAISI’s explanation and figures for their context.
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A comparison is interpretable only when readers can see what was held constant and what differed. Alongside task success or output quality, report:
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- Cost and resource use, including time, tokens, or tool calls when material.
- Tool access and scaffold differences that change the tested system.
- Safety, policy compliance, or other deployment-specific outcomes relevant to the intended use.
- Evidence that passing scores reflect the intended work, rather than contamination or grader loopholes.
IEEE’s Project 3777 page lists efficiency, robustness, adaptability, ethical compliance, and interoperability among possible benchmarking dimensions. The project is listed as active, not as a published standard; its scope should not be treated as a completed testing rule. NIST’s voluntary draft and the IEEE project listing are guidance and standards-development context, not a universal required scorecard.
Report enough for others to judge and repeat the result
A useful report lets another evaluator reconstruct the measurement and understand its limits. Include the objective; benchmark, dataset, and versions; sample composition; exact model and system configuration; prompts and protocol; scorer and judge details; budgets and cost controls; optimization practices; sensitivity analyses; statistical assumptions and uncertainty; and known limitations. Where feasible, share data, code, item-level outcomes, and transcripts or traces, while respecting security and business constraints.
Explain how the benchmark relates to the intended use and where test conditions differ from deployment. The NIST AI RMF Measure guidance likewise situates evaluation within context and risk management. A benchmark result supports a claim about the construct, systems, tasks, and protocol actually measured; it does not by itself establish performance across different users, operating conditions, or real-world tasks.
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