To evaluate an LLM agent reliably, measure the complete system that performs the task: the model, its prompts and tools, the agent harness, and the task environment. Define a pass condition in observable terms, capture both the execution trace and the resulting environment state, use a grader suited to the outcome, freeze the run configuration, repeat stochastic trials, and audit the benchmark for unfair tasks or tests.
A single score without that context is not a capability measurement. It may reflect an overly strict test, a lucky trajectory, hidden infrastructure advantages, or a grader that rewards the wrong behavior. The workflow below is designed to make agent results inspectable, comparable, and repeatable.
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What an agent evaluation actually measures
An agent evaluation is not a model-only test. Anthropic puts the scope plainly: “When we evaluate ‘an agent,’ we’re evaluating the harness and the model working together.” The harness includes the system prompt, tool definitions, planning loop, memory, retries, context management, stopping rules, permissions, and any code that translates model output into actions. The environment includes the files, APIs, databases, browser session, credentials, time limits, and other state the agent can affect.
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Separate two records for every trial:
- Transcript or trace: the chronological record of prompts, model messages, tool calls, tool results, errors, retries, and termination.
- Outcome: the final state of the task environment, such as a changed file, a passing test, a correctly updated record, or a supported research answer.
An agent can claim success in its final message while leaving the environment unchanged. Conversely, it can complete the required change while describing it poorly. Grade the outcome directly and retain the transcript so you can explain how that outcome happened.
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Keep the model identifier and the harness version together in reports. Changing a tool schema, retry policy, context window, sandbox image, or timeout changes the system being evaluated even when the model name is unchanged.
1. Define observable success before running anything
Start with the real user task, then write what a passing result looks like in terms another evaluator can verify. Avoid goals such as “be helpful” or “solve the issue well” unless they are decomposed into checks.
Turn the request into a pass specification
- State the required end state: for example, “the configuration contains exactly three approved entries and no unapproved entries.”
- List essential facts or constraints the response must contain.
- Identify disallowed behavior, such as inventing a source, exposing a secret, or modifying files outside the assigned directory.
- Set operational limits when they matter, including maximum tool calls, elapsed time, token budget, or cost.
- Decide whether partial credit is meaningful or whether one missing condition makes the task fail.
For environment-changing tasks, make the final state authoritative. A shell command that exits successfully is not proof that the desired artifact is correct; inspect the artifact or run a verification test. For research and synthesis tasks, define the required claims, source quality, grounding, and uncertainty handling before judging prose.
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Write a rubric for open-ended work
Open-ended outputs need a task-specific rubric rather than a vague quality score. Define criteria such as factual correctness, coverage of required points, source grounding, adherence to format, and safety. Give each criterion observable anchors. “Excellent” should describe evidence a grader can point to, while “poor” should identify a concrete omission or error.
Keep the rubric independent of the agent’s self-report. If the agent says it checked a database, the grader should verify the database state or tool trace rather than award credit for the assertion.
2. Build a task set that represents intended use
A repeatable eval is only useful when its tasks resemble the work you care about. Sample the domain and difficulty range deliberately instead of collecting only easy demonstrations or spectacular edge cases.
Cover realistic variation
- Include routine, ambiguous, and failure-prone requests from the target workflow.
- Vary input length, data quality, permissions, and available tools.
- Include tasks that require clarification when the request is genuinely underspecified.
- Represent both common paths and high-impact rare cases.
- Keep a held-out set for final comparison so repeated tuning does not overfit the public tasks.
For coding and other objectively verifiable work, execution-based checks are usually stronger than judging the final explanation. For research tasks, specify which facts must be present, what counts as an authoritative source, and how unsupported claims are penalized.
Check task wording and tests for hidden assumptions
A task can be unfair even when the model behaves correctly. Look for prompts that conflict with the expected behavior, tests that demand an unstated implementation detail, incomplete assertions, and fixtures that differ from the task description. Have someone who did not write the task attempt it using the published instructions alone.
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OpenAI’s July 8, 2026 audit of SWE-Bench Pro shows why this step matters. In the 731-task public split, its automated pipeline flagged 200 tasks (27.4%) as broken, while a human annotation campaign identified 249 (34.1%). OpenAI summarized the estimate as roughly 30% broken. Reported failure types included overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. Those figures describe that audit and dataset; they are not a general defect rate for all benchmarks.
3. Match the grader to the outcome
Use the most direct reliable check available. A sophisticated language-model judge is not automatically better than a deterministic assertion, and a deterministic assertion is not sufficient for every open-ended task.
| Grader approach | Best fit | Strengths | Risks and controls |
|---|---|---|---|
| Deterministic state or execution check | Files, database rows, API responses, test suites, schemas, and exact facts | Reproducible and fast; directly tied to the required state | Can miss quality outside the assertion; audit coverage and avoid implementation-specific requirements |
| Rule-based content checks | Required fields, prohibited strings, citation presence, format, and policy constraints | Transparent and inexpensive | Surface matching can reward meaningless text; combine with semantic or state checks |
| LLM rubric grader | Open-ended explanation, synthesis, style, or nuanced adherence | Handles language variation and qualitative criteria | Judge inconsistency, position bias, and rubric gaming; calibrate against expert-labeled examples |
| Expert human review | High-stakes, novel, or ambiguous outcomes | Can detect errors and context a fixed test misses | Cost and disagreement; use a written rubric, multiple reviewers where needed, and agreement monitoring |
| Hybrid grader | Tasks combining a verifiable state with open-ended communication | Separates objective completion from explanation quality | More maintenance; document how component scores combine and which failures are fatal |
Calibrate a subjective judge
Create a calibration set containing clear passes, clear failures, borderline cases, and adversarial examples. Have experts label it independently, run the proposed judge, and inspect disagreements. Revise rubric language until the judge’s decisions are consistent with the intended standard. Recheck the judge periodically because a prompt change, model update, or new task style can alter its behavior.
When practical, evaluate the judge on a separate set from the examples used to tune its rubric. Inspect transcripts for low scores: a low result may indicate a genuine agent failure, a missing fixture, or a grader that misunderstood a valid solution.
4. Preserve an auditable run record
Store enough information for another engineer to reproduce a trial and explain its score. A final answer and one scalar are not an audit trail.
| Record | What to capture |
|---|---|
| Task identity | Task ID, dataset and fixture version, task text, expected state, and any seed used to generate it |
| Agent configuration | Model name and version, system and developer prompts, tool schemas, harness or scaffold commit, memory settings, retry and stopping rules |
| Inference settings | Sampling parameters, context limits, response mode, timeout, and any provider-side options |
| Environment | Container or image version, operating-system dependencies, network and permission policy, starting state, and injected secrets or test credentials (record identifiers, not secret values) |
| Trace | Every model message, tool call and result, error, retry, timing, token count where available, and termination reason |
| Outcome | Final environment snapshot or artifact hash, test outputs, rubric decisions, and links between checks and evidence |
| Resources | Wall-clock time, compute allocation, API cost, rate limits, and any queue or service failures |
Version these records rather than overwriting them. If a later run uses a new prompt, task fixture, grader, or container, give it a new configuration identifier.
5. Freeze the setup and control infrastructure variables
Record task and dataset versions, the agent scaffold, model identifier and version, prompt and tool configuration, grader version, resource settings, and seed or sampling parameters. Comparisons are meaningful only when these variables are matched or deliberately varied and reported.
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Report the resource policy with the score: CPU, memory, disk, network access, timeout, parallelism, rate limits, and any service quotas. If one system receives more retries, a larger context window, or a faster tool backend, label the comparison as unmatched.
6. Repeat runs when behavior is stochastic
Agents can vary because of sampling, tool timing, concurrent services, randomized fixtures, or model-side nondeterminism. One successful trajectory is not a stable estimate.
Use seeds and a declared aggregation method
When the framework supports it, fix the evaluation seed for scenario generation and record it with the result. Anthropic’s Bloom framework generates different scenarios across runs while targeting a behavior and ties results to an evaluation seed for reproducibility. Its example suites cover four alignment-relevant behaviors on 16 models, use 100 rollouts, and show standard deviation across three repetitions.
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Separate scenario variance from agent variance
If each run receives a new generated scenario, vary the scenario seed while keeping the agent configuration fixed. If you want to study agent stochasticity, hold the scenario fixed and repeat the agent run. A factorial design that varies both can show whether changes come from the task sample or the agent trajectory.
7. Audit the benchmark before trusting its score
Review prompts, fixtures, tests, graders, and environments as a single measurement instrument. Ask whether a shortcut can earn points without completing the task, whether a valid solution can fail for an irrelevant implementation choice, and whether the test data leak into prompts or model training.
Run a preflight audit
- Have an independent reviewer solve a sample using only the task instructions.
- Compare the expected answer or state with the actual acceptance test.
- Try compliant alternative implementations to detect overfitting to one design.
- Inject common error cases and confirm that the grader catches them.
- Check that every required behavior is tested and every tested behavior is required.
- Review tool permissions and fixtures for accidental advantages or impossible constraints.
OpenAI’s audit described its goal as benchmarks that are “hard to game, easy to trust, and genuinely reflective of model capability or alignment.” Treat that as a design criterion, not as evidence that any particular benchmark is automatically sound.
8. Inspect successful and failed transcripts
Aggregate scores tell you that behavior changed; traces help explain why. Sample high-scoring and low-scoring runs, then categorize the failure mode.
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- Planning error: the agent chose an unsuitable strategy or stopped before checking its work.
- Tool-use error: it selected the wrong tool, supplied invalid arguments, or ignored an error.
- Environment error: a dependency, permission, timeout, or service failure prevented completion.
- Knowledge or reasoning error: it misunderstood the task or produced an unsupported conclusion.
- Grader error: the expected state or rubric does not represent the user’s requirement.
Track these categories separately. Improving a retry loop may reduce tool-use failures without improving reasoning; loosening a test may raise the score while reducing measurement validity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Compare evaluation approaches on the dimensions that matter
No framework wins on every axis. Choose a design that fits the decision you need to make.
| Dimension | Questions to answer |
|---|---|
| Objective verifiability | Can the required state be checked exactly, or is expert judgment unavoidable? |
| Coverage and realism | Do tasks reflect actual users, difficulty levels, edge cases, and tool constraints? |
| Grader reliability | Does the grader agree with calibrated experts, and are disagreements logged? |
| Variance and infrastructure sensitivity | How much do seeds, retries, resource ceilings, and service failures move the result? |
| Cost and latency | Are token usage, tool calls, wall time, compute, and API charges measured per task? |
| Maintenance and saturation | Who updates stale tasks, fixes broken tests, detects leakage, and replaces tasks the model has memorized? |
For release gating, a smaller deterministic regression suite is often appropriate. For broad capability measurement, use a larger, realistic benchmark with documented sampling and uncertainty. Keep the two purposes distinct when one task set cannot serve both without compromise.
10. Interpret scores and leaderboard gaps cautiously
Attach the configuration, task set, grader, and uncertainty to every reported score. A score from one harness cannot be treated as a pure ranking of model intelligence.
Anthropic’s infrastructure analysis recommends skepticism toward leaderboard differences below three percentage points until the evaluation setup is documented and matched. That threshold is guidance from the cited analysis, not a universal margin of error. Your own repeated runs may show larger or smaller uncertainty.
When publishing a comparison, include:
- Task and split names, versions, and sample counts.
- Model and harness identifiers.
- Prompt, tool, resource, and sampling settings.
- Grader version and calibration method.
- Seed, repetition count, aggregation rule, and variance.
- Infrastructure failures, retries, exclusions, and missing runs.
- Cost and latency measurements when they affect deployment decisions.
11. Use published benchmark examples as design lessons
PaperBench and replication difficulty
OpenAI’s 2025 PaperBench announcement described tasks drawn from 20 ICML 2024 Spotlight and Oral papers, totaling 8,316 individually gradable tasks. The best-performing tested setup reported an average replication score of 21.0%. The setup qualifier matters: the number describes that tested configuration and should not be presented as a universal measure of research-agent ability.
The lesson for your own suite is to decompose a large goal into individually gradable requirements while preserving the real integration challenge. A score can reveal which components are routinely completed and which ones fail, instead of hiding all errors behind one pass/fail label.
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The SWE-Bench Pro audit demonstrates that benchmark validation is part of evaluation engineering. Broken or misleading tasks can dominate a score, so publish defect counts, correction policies, and the version of the task split used for each result.
12. Maintain the eval as software
Assign an owner and a change process. Every task, grader, fixture, and harness update should have a version, review record, and reason for change.
- Run a smoke suite whenever prompts, tools, dependencies, or model versions change.
- Review transcripts on a schedule, not only after a score regression.
- Add a regression task for every confirmed production failure that belongs in scope.
- Retire or refresh tasks when models saturate them, data leakage is discovered, or the user workflow changes.
- Recalibrate subjective graders after rubric, judge-model, or task-distribution changes.
- Keep a changelog so score shifts can be attributed to the agent, the benchmark, or the infrastructure.
Maintain separate dashboards for regression pass rate, broad benchmark quality, cost, latency, and infrastructure reliability. Combining them into one unexplained number makes trade-offs invisible.
How do I evaluate an AI agent? An end-to-end procedure
- Write the task contract. Define the user request, required final state, prohibited behavior, operational limits, and what constitutes partial credit.
- Assemble representative tasks. Cover normal, ambiguous, difficult, and failure-prone cases; reserve a held-out split for comparison.
- Implement the strongest direct checks. Verify files, records, test results, citations, or required facts from the environment rather than trusting the final message.
- Add a rubric only where necessary. Specify anchors for groundedness, coverage, quality, and format; prepare expert-labeled calibration examples.
- Version the complete configuration. Freeze model, harness, prompts, tools, environment, grader, resources, and seed.
- Run a preflight fairness audit. Attempt alternate valid solutions, inject known failures, and look for hidden assumptions or test gaps.
- Execute repeated trials. Hold variables fixed for the question being studied, record every trace and outcome, and classify infrastructure failures.
- Inspect representative traces. Sample passes, failures, and borderline cases; distinguish agent errors from grader or environment defects.
- Report uncertainty and resources. Publish sample count, aggregation, variance, retries, exclusions, cost, latency, and the exact setup.
- Maintain the suite. Refresh saturated or leaked tasks, recalibrate judges, and assign an owner for changes and incident response.
Common failure modes and fixes
The agent’s final message is graded as success
Why it fails: self-reported completion can diverge from the environment. Fix: verify the artifact or state directly and retain the trace as supporting evidence.
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Why it fails: valid alternative solutions are rejected. Fix: test the required behavior or interface, not an incidental file layout or algorithm.
An LLM judge gives unstable scores
Why it fails: vague criteria and judge variation create noisy labels. Fix: add anchored rubric examples, use a calibration set, measure agreement with experts, and inspect disagreements.
A resource limit looks like a reasoning failure
Why it fails: timeouts, memory limits, rate limits, or flaky services prevent completion. Fix: record resource errors separately, publish the limits, and compare matched configurations.
A benchmark rewards shortcuts
Why it fails: leaked data, weak tests, or predictable fixtures let an agent score without solving the user’s problem. Fix: add held-out tasks, vary fixtures, test adversarial cases, and audit for leakage.
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A tiny leaderboard lead is treated as decisive
Why it fails: task sampling, stochastic trajectories, grader noise, and infrastructure differences may exceed the gap. Fix: repeat runs, report uncertainty, and withhold strong claims until setups are documented and matched.
Bottom line
A repeatable LLM-agent eval is a controlled experiment and an audit trail, not a leaderboard number. Define success in observable terms, judge the final environment state, preserve the full transcript, match the grader to the task, freeze prompts and resources, repeat stochastic trials, and challenge the benchmark itself. Only then can a score support a credible decision about an agent’s capability, reliability, cost, or safety.
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