Evaluate an AI agent with a layered test, not a single score: run representative tasks in a controlled environment, verify the resulting state with executable checks, inspect the complete tool trajectory, collect blinded and rubric-based human judgments, repeat every task to measure reliability, and publish the conditions behind each result. This combination tells you whether an agent merely produces plausible text or actually completes work safely and consistently.
What an agent evaluation must establish
An agent can fail even when its final message sounds correct. It may call the wrong tool, use an unsafe argument, modify the wrong record, abandon a recoverable error, or succeed once and fail on the next run. A useful evaluation therefore separates several questions:
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- Did the requested state change occur? For example, was the correct ticket updated, file created, experiment completed, or reservation recorded?
- Was the result factually and technically correct? A polished explanation cannot compensate for an incorrect calculation or unsupported claim.
- Did the agent follow policy and safety constraints? This includes authorization boundaries, privacy rules, confirmation requirements, and safe-stop behavior.
- How did it get there? Tool selection, arguments, planning, recovery, and unnecessary actions are part of performance.
- Would a user trust the interaction? Clarity, completeness, effort, and expectation-setting require human judgment.
- Is the behavior repeatable? A single successful run is not reliability.
Report these dimensions separately. A weighted aggregate can be useful for a release decision, but it should never replace the underlying per-task and per-dimension results.
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Write a complete task contract
Each test case should specify the user goal and the exact starting state. Record the data the agent may access, the tools and APIs it may call, time and cost limits, authentication context, and the conditions under which it must stop or escalate. Include what the user did not authorize. “Resolve the customer issue” is too vague; “refund the duplicate charge, leave the original charge untouched, and ask for confirmation before any additional credit” is testable.
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Define the expected end state independently of the agent’s wording. A task contract can include database fields, file contents, test results, citations, messages sent, or an explicit no-action state. Also list prohibited outcomes, such as exposing another customer’s data, deleting production records, or claiming completion after a failed tool call.
Sample the cases users actually encounter
Build a representative set from de-identified production logs, support transcripts, incident reports, and expert-authored scenarios. Include ordinary cases, boundary values, ambiguous requests, missing data, adversarial instructions, permission conflicts, tool outages, and recovery opportunities. Keep a held-out set that the development team does not use while tuning prompts, tools, or rubrics; use it only for final comparisons.
Vary the initial state as well as the wording. An agent that succeeds on a clean account may fail when a previous attempt left a partial update. Include duplicate records, stale information, concurrent changes, rate limits, and empty search results when those conditions exist in the target workflow.
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Turn goals into a rubric that can be graded
Separate hard requirements from quality dimensions
Hard requirements are binary or near-binary conditions that protect correctness and safety: the correct entity was changed, required fields are valid, a policy was obeyed, no prohibited action occurred, and the agent escalated when authorization was missing. Quality dimensions cover clarity, completeness, helpfulness, explanation quality, user effort, and interaction tone. Keep them distinct so a friendly but unsafe result cannot outscore a safe, slightly terse one.
Use hierarchical criteria for long tasks
Break a multi-stage task into individually gradable requirements. A research workflow might require source discovery, evidence extraction, code execution, error handling, analysis, and a final report. Grade each requirement and then calculate task-level results from the declared rule, such as all critical requirements passing plus a minimum proportion of noncritical requirements. Mark partial credit explicitly rather than hiding it inside an informal impression.
PaperBench demonstrates the value of this approach: it evaluates reproduction of 20 ICML 2024 papers through 8,316 individually gradable tasks, with hierarchical rubrics co-developed with paper authors. In the reported 2025 setup, the best tested agent averaged a 21.0% replication score, while top machine-learning PhD participants remained ahead. The figure is evidence about that benchmark and setup, not a universal estimate of research-agent ability.
Define unsafe-action conditions in advance
Specify failures that trigger an automatic severe-failure flag regardless of other points. Examples include unauthorized data disclosure, irreversible deletion without confirmation, fabricated completion, bypassing an approval gate, or continuing after a safety-critical tool error. A severe-failure rate should be reported separately from ordinary task success.
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Run every test in a controlled, observable harness
Freeze the conditions
Pin the model identifier and version, system and developer instructions, tool schemas, tool versions, retrieval corpus or data snapshot, temperature and other sampling settings, timeouts, and random seeds where supported. Use isolated accounts and deterministic fixtures when possible. If the task depends on a live service, record the service region, date, availability assumptions, and any simulated components.
Capture the complete trajectory
Store the initial prompt, conversation messages, model and tool versions, every tool call and argument, tool observations, retries, errors, latency, token usage, dollar cost, and human interventions. Preserve the order and timestamps. A final transcript alone cannot reveal that the agent made a dangerous call and later corrected it, or that a human quietly repaired the outcome.
Redact secrets while retaining enough structure to reproduce the decision. Keep a stable run identifier linking the trajectory to its task, rubric version, judge version, and outcome. This trace becomes the evidence for debugging as well as the audit record for a release.
Verify outcomes with executable checks
Prefer deterministic validators over a reviewer guessing from the final answer. Check database state, API records, file diffs, test suites, schema validity, numerical tolerances, citation presence, or an expected artifact manifest. A validator should distinguish pass, partial pass, ordinary failure, and severe failure, and should explain which requirement failed.
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Collect human feedback for what code cannot judge
Use blind pairwise preference for comparisons
For preference comparisons, have two agents complete the same task under identical conditions. Present outputs in randomized order without revealing which system produced which result, and let a trained judge select the better result or call a tie. HumanEval.org describes this as the measurement unit: “The unit of measurement is a blind pairwise comparison: two models complete the same task under identical conditions and a human judge who cannot see which model is which picks the better result, or calls a tie.”
That vote measures preference, not correctness. The same methodology states, “Preference, not correctness. A vote records which of two outputs a person preferred for the task at hand.” Use executable checks and anchored rubrics for factual accuracy, policy compliance, and safety rather than treating a preference win as proof of those properties.
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Use anchored rubrics for absolute judgments
For usefulness, clarity, completeness, trust, interaction quality, and user effort, give reviewers concrete anchors and examples for each score. State what a 1, middle score, and top score mean. Train reviewers on a calibration batch, discuss disagreements, and measure agreement on a continuing sample. HumanEval.org notes that ratings are anchored per category and are not comparable across categories; do not combine unrelated category scores as if they shared one scale.
Design the review queue carefully
Randomize task and system order, hide model identity, prevent one reviewer from seeing both labels for the same item when that could create bias, and include duplicate calibration items. Track abstentions and the reason for them. For high-risk workflows, require a second review of every severe-failure candidate rather than relying on an average rating.
Validate automated and agentic judges
Model-based judges can reduce cost and provide intermediate feedback, but they are evaluators that need evaluation. Create a human-labeled calibration set covering normal, borderline, adversarial, verbose, terse, and stylistically different outputs. Compare the automated judge with the human labels, inspect disagreements, and measure whether it favors a particular position, writing style, verbosity, or model family.
The Agent-as-a-Judge work describes agentic evaluation that supplies intermediate feedback during the task process. Its DevAI set contains 55 realistic code-generation tasks and 365 hierarchical solution requirements; the authors report reliability comparable to a human evaluation baseline in their setup. Treat that result as conditional evidence and recalibrate when prompts, models, tools, or task distributions change.
Keep the judge prompt, model version, temperature, parsing rules, and scoring thresholds under version control. Never allow the evaluated agent to edit the judge’s inputs or validator results. For safety-critical requirements, retain a human gate even when automated agreement is high.
Measure reliability with repeated runs
Run each task multiple times under the production sampling settings. Report single-run success (often called pass@1) alongside repeated-run reliability. If a task passes in at least one of k independent attempts, pass@k answers an “any successful attempt” question; it does not mean the agent is dependable. To measure dependable behavior, report the proportion of tasks that pass all k attempts (pass^k), plus per-task variance and severe-failure frequency.
State how runs are sampled. Different random seeds, fresh sessions, and reset environments test stochastic reliability; replaying an identical trace does not. Include confidence intervals or bootstrap intervals around aggregate estimates, especially for small task sets. Show the distribution: an 80% average can conceal a group of tasks that never succeeds.
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Choose benchmarks that match the workflow
Public benchmarks are useful reference points, not substitutes for a domain-specific suite. Match the benchmark’s interaction model, tools, data, and success definition to your agent.
| Benchmark or suite | What it exercises | Published evidence and scope | How to use it |
|---|---|---|---|
| PaperBench | End-to-end reproduction of research papers | 20 ICML 2024 papers and 8,316 individually gradable tasks; best tested agent averaged 21.0% replication in the reported 2025 setup | Use for long-horizon planning, coding, experimentation, and hierarchical grading; do not generalize the score to all agents. |
| DevAI with Agent-as-a-Judge | Realistic code generation and intermediate process evaluation | 55 tasks and 365 hierarchical solution requirements, Proceedings of Machine Learning Research, 2025 | Use to test requirement-level grading and judge calibration for software tasks. |
| BrowseComp | Difficult, multi-step web research | 1,266 problems; reported accuracy ranged from 0.6% for GPT-4o to 51.5% for Deep Research under the documented 2025 setup | Use when source discovery, verification, and persistence matter; preserve the documented browsing conditions. |
| AstaBench | Scientific literature understanding, coding, execution, data analysis, and research output | Includes an end-to-end benchmark that measures experiment design, execution, analysis, and reporting | Use for scientific workflows where intermediate artifacts and final communication both matter. |
| MLAgentBench | Machine-learning experimentation with files, code execution, and output inspection | 13 tasks; the best tested Claude 3 Opus agent averaged 37.5% success in the 2024 paper’s setup | Use to expose failures that text-only coding tests miss, especially environment and artifact errors. |
| AgentQuest | Modular, extensible progress and failure analysis | Argues against reducing every run to one overall success number | Use modular metrics to locate architectural failure points and track improvements. |
| τ-bench | Interactive service conversations with simulated users, policies, APIs, and annotated goal states | Supports end-state, policy, user-experience, tool-validity, and repeated-run evaluation | Use for support or operations agents; score policy and consistency, not only the final message. |
Benchmark versions can change task definitions and scoring. AstaBench is an evolving suite, and versions in the τ-bench family may alter tasks or metrics. Record the exact release and configuration with every result.
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Use a practical evaluation workflow
- Write the task contract. Define goal, starting state, authorized data, tools, limits, safe-stop behavior, and prohibited actions.
- Assemble and split tasks. Include ordinary, boundary, ambiguous, adversarial, and recovery cases; reserve a held-out set.
- Version the rubric. Mark critical requirements, quality dimensions, partial-credit rules, and severe failures before running agents.
- Build the harness. Reset fixtures, pin versions, isolate credentials, and make tool outcomes observable.
- Instrument runs. Capture prompts, traces, observations, errors, interventions, latency, tokens, and cost.
- Run executable validators. Check final state and artifacts, classify failure types, and retain validator output.
- Collect blinded human judgments. Use pairwise preference for relative choice and anchored scores for absolute qualities.
- Calibrate automated judges. Compare them with human labels and audit style, position, verbosity, and self-preference bias.
- Repeat tasks. Calculate pass@1, pass^k, per-task variance, and severe-failure rate under realistic sampling.
- Publish and review. Release a benchmark card, investigate failures, refresh hidden cases, and rerun after model, tool, prompt, or policy changes.
Report metrics that support a decision
| Metric | What it answers | Required qualification |
|---|---|---|
| Verified task success | Did the agent satisfy the declared end-state requirements? | Show the validator, partial-credit rule, task count, and confidence interval. |
| Pass@1 | How often does one attempt succeed? | State sampling settings and whether tasks were reset between attempts. |
| Pass^k | How often do all of k attempts succeed? | Report k, independence assumptions, and per-task distribution. |
| Severe-failure rate | How often does the agent take a prohibited or dangerous action? | Define severe failures before testing; do not average them away. |
| Human preference | Which output do blinded reviewers prefer? | Report ties, reviewer instructions, sample size, and that preference is not correctness. |
| Policy adherence | Does the trajectory obey authorization and domain rules? | Use requirement-level labels and identify violations by type. |
| Tool validity and recovery | Are calls valid, and does the agent recover from errors? | Count invalid calls, retries, successful recovery, and unnecessary actions. |
| Latency, cost, and intervention | What operational burden accompanies success? | State hardware or service conditions, token or dollar accounting, and human-help rules. |
Use a decision threshold that reflects the deployment risk. A low-risk drafting assistant may tolerate occasional clarification, while an agent that changes financial or production state needs near-zero severe failures and a tested escalation path. Keep the threshold explicit rather than choosing it after seeing the results.
Publish a benchmark card so results are reproducible
Every score should travel with a compact benchmark card containing:
- Task source, inclusion and exclusion rules, geography, language, and collection dates.
- Public, development, and held-out splits, plus the sampling policy.
- Initial states, allowed tools, data snapshots, simulator behavior, and reset procedure.
- Model name and version, prompts, tool and retrieval versions, sampling parameters, and resource limits.
- Validator and rubric versions, judge model and prompt, human-review instructions, and agreement statistics.
- Number of runs per task, seeds or session policy, intervention policy, latency and cost accounting, and confidence-interval method.
- Known exclusions, missing capabilities, contamination checks, and defenses against benchmark shortcuts.
When an agent improves, refresh tasks, hidden tests, and rubrics. Otherwise an agent may learn the benchmark’s quirks instead of the workflow. Track score changes alongside task-level failure changes so a higher aggregate cannot hide a new safety regression.
Common evaluation mistakes and their fixes
Scoring only the final text
Problem: A fluent answer receives credit despite an incorrect state change or unsafe tool call.
Fix: Validate external state and grade the trajectory separately.
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Using one aggregate number
Problem: Strong performance on easy tasks masks failures on rare but consequential cases.
Fix: Publish per-task results, requirement-level failures, edge-case slices, severe failures, and reliability across runs.
Treating preference as truth
Problem: Reviewers prefer a confident or verbose answer that contains an error.
Fix: Pair preference judgments with deterministic correctness checks and anchored safety rubrics.
Trusting an uncalibrated judge
Problem: An automated grader rewards style, length, or a favored position.
Fix: Compare it with blinded human labels, inspect disagreements, and recalibrate after every material change.
Testing only clean, repeatable demos
Problem: The agent fails on partial state, ambiguity, outages, or a second attempt.
Fix: Add recovery and adversarial cases, reset environments, and report repeated-run reliability.
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Problem: Scores from different versions appear comparable when task definitions changed.
Fix: Version tasks, rubrics, validators, judges, and benchmark releases; report each version separately.
How to interpret the result
A benchmark score is conditional evidence about a defined task distribution, environment, and scoring process. It is not a general intelligence rating. A high score with poor repeated-run reliability indicates a stochastic agent; high preference with low factual correctness indicates persuasive presentation; high task success with policy violations indicates an unsafe deployment candidate. Read the slices and trajectories before making a release decision.
The strongest evaluation package combines verified completion, reliable repetition, safe and policy-compliant behavior, useful human-rated interaction, valid tool use, recovery from failure, reasonable cost and latency, and resistance to contamination or shortcutting. If any one of these is material to the workflow, make it a reported gate rather than an unmeasured assumption.
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