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Evaluate an LLM agent as a complete system: the model, its harness, tools, prompts, resources and task environment. Define an observable pass condition before a run, preserve both the execution trace and the resulting environment state, use a grader suited to the outcome, and publish the configuration needed to reproduce the result. A score without that context can measure a benchmark’s quirks or infrastructure noise instead of useful capability.
What an agent evaluation actually measures
A conventional model test can hold the prompt and answer format fixed. An agent evaluation adds a scaffold that decides when to call tools, how to represent state, which files or APIs are available, how errors are retried and when the run stops. The same model can therefore receive different scores under different harnesses.
Anthropic states the scope plainly in Demystifying evals for AI agents (January 9, 2026): “When we evaluate ‘an agent,’ we’re evaluating the harness and the model working together.” Treat that sentence as a design constraint. Your reported system should identify the model and version, but it should also identify the scaffold, tool implementations, prompts, limits and environment that made the run possible.
Keep two outputs separate:
- Transcript or trace: the chronological record of messages, tool calls, tool results, retries, errors, token usage and termination.
- Outcome: the final state that exists outside the agent’s claim, such as a committed code change, a modified database row, a generated report or a correctly configured service.
An agent can produce a confident completion message while leaving the environment unchanged. Conversely, it can reach the correct state while giving a poor explanation. Recording both prevents those cases from being conflated.
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Start with an observable definition of success
Write the pass condition before selecting a model or running a baseline. Begin with the real user task, then translate “good” into facts an independent checker can observe. A useful specification names the starting state, permitted actions, required final state, forbidden side effects and acceptable evidence.
Use exact checks when the result is directly verifiable
For a coding task, an execution-based test can check behavior in a clean environment. For an operations task, query the resulting configuration rather than trusting the agent’s summary. For a data task, compare records against an expected schema and value set. Exact checks are fast and repeatable, but they must test the intended behavior rather than an incidental implementation detail.
Use a rubric for open-ended work
Research, planning and synthesis rarely have one exact string as an answer. Define separate criteria for required facts, source quality, grounding, reasoning quality, completeness and harmful omissions. State what earns full, partial and zero credit, and include disqualifying errors. A rubric should let two trained reviewers reach similar judgments without guessing what the task author meant.
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Separate the agent’s assertion from the acceptance test
Do not award success because the final message says “done.” Make the acceptance test read the artifact or environment. If the task asks for a report, verify that required claims appear and that citations support them. If it asks for a change to a service, inspect the service after the run. This distinction is the practical difference between measuring communication and measuring task completion.
Build a task set that represents intended use
A repeatable suite is a sample of the work you care about, not a collection of convenient demos. Cover the domain, the difficulty range and the failure modes that matter in production. Include routine cases, edge cases, ambiguous inputs that a real user would clarify, and adversarial cases only when they are part of the intended threat model.
Make task instructions complete but realistic
Read every prompt as if you were an agent with no private knowledge. Remove requirements that depend on an unstated library, naming convention, directory or deployment assumption. If a detail is intentionally left open, say how multiple valid solutions will be judged. A test that silently expects one implementation can penalize a correct solution.
Choose coverage before scale
One hundred near-duplicate tasks produce less information than a smaller set spanning the behaviors you need. Tag each task by capability, difficulty, tool dependency and expected outcome. Keep a held-out portion for checking whether a model has learned the benchmark’s patterns rather than the underlying skill.
PaperBench illustrates the value of explicit task design: OpenAI’s 2025 announcement describes 20 ICML 2024 Spotlight and Oral papers broken into 8,316 individually gradable tasks. Its reported 21.0% average replication score for the best-performing tested setup is meaningful only with that task construction and setup attached; it is not a general estimate of research-agent ability.
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Capture enough information to audit every run
Store an immutable record for each trial. A minimal record contains a unique run ID, task and dataset version, model identifier, harness version, prompt and tool configuration, resource limits, random seed where applicable, start and end times, transcript, grader output and final environment snapshot.
Record the trace, not just the final answer
At minimum, log each model turn, tool name, sanitized arguments, tool response, error and retry, along with token counts and latency. Preserve ordering and timestamps so you can tell whether a failure came from a bad decision, a tool timeout or an infrastructure interruption. Redact secrets before storage, but retain enough structure to reproduce the decision path.
Snapshot the resulting state
Use a clean, versioned environment for each task. Save the files, database fixture, API state or other artifact that the grader inspected. If the environment is too large to archive, record a content hash plus the exact image, fixture and migration versions needed to rebuild it. A transcript without the state it acted on is not an auditable evaluation.
Keep a machine-readable manifest
A manifest makes hidden differences visible when two teams compare scores. For example:
{"run_id":"2026-10-16T12:00:00Z-task-042-rep-03","task_set_version":"support-v7","model_id":"model-and-version","harness_version":"agent-scaffold-2.4.1","prompt_version":"system-18","grader_version":"grader-5","resource_profile":"cpu-4-mem-16g-timeout-900s","seed":7319,"status":"completed"}The identifiers in this example are placeholders for your own versioned components; the important property is that every variable used by the run is named and retained.
Choose a grader that fits the outcome
Grading is part of the measurement system. A perfect task with a weak grader still yields a misleading score.
Deterministic graders
Use assertions, parsers, test execution, schema validation or state queries when the expected property is directly checkable. Report which checks passed and failed, not only a single aggregate. Deterministic checks are reproducible, but incomplete tests can give false confidence.
Rubrics and human review
For open-ended outputs, define dimensions such as factual coverage, source quality, groundedness, clarity and compliance. Have reviewers practice on calibration examples that include borderline cases. Track agreement and discuss disagreements; a rubric that produces persistent disagreement needs revision.
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LLM-based judges
An LLM judge can scale qualitative grading, but it should follow a narrowly written rubric and receive the same evidence a human would inspect. Evaluate the judge on a separate set labeled by experts. Periodically compare judge decisions with human decisions, check for position or style bias, and inspect examples where the score changed. Do not let a judge grade its own unverified assumptions about the task.
Combine signals deliberately
For an open-ended research task, a composite can include required-fact checks, groundedness, source quality and a calibrated rubric score. Document the weighting and any minimum gate, such as failing the task when a safety-critical fact is wrong. Changing weights changes the meaning of the benchmark, so version the grader alongside the task set.
Freeze the evaluation setup before comparing systems
Write down the complete configuration and hold it constant across systems whenever possible. The controlled variables should include:
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- Task, fixture and dataset versions.
- Model name, release or snapshot identifier and inference parameters.
- System and task prompts, tool descriptions, tool versions and retry policy.
- Agent scaffold, context-window handling, stopping rules and sandbox permissions.
- CPU, memory, GPU, network, timeout and concurrency limits.
- Grader code, rubric and reference answers.
- Random seeds, scenario-generation parameters and sampling settings.
Track cost and latency per task as first-class outcomes. A system that reaches the same success rate with half the calls or a lower tail latency may be preferable, while a cheaper run that uses a weaker resource profile is not an apples-to-apples comparison.
Anthropic’s infrastructure analysis of agentic coding evaluations argues that resource configuration is an experimental variable and recommends publishing setup specifications. If a comparison omits those specifications, treat a small score advantage as unconfirmed rather than as a capability breakthrough.
Repeat stochastic evaluations and report variation
Agent behavior can vary because of model sampling, tool timing, retries, generated scenarios and hidden state. Run multiple repetitions when any of those factors can change the outcome. Report the number of trials, aggregation method, per-task results and observed variation.
Use seeds without treating them as magic
Fix a seed when the framework supports deterministic scenario generation, and record it with the result. A seed makes a particular sample reproducible; it does not remove variation from a nondeterministic model, network or external service. If you change the seed, you have changed the evaluation sample.
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Select an aggregation that matches the question
Use a mean when every task contributes equally and a median when a few extreme tasks dominate cost or latency. Report task-level pass rates for safety or reliability gates. For small samples, show the raw distribution or confidence interval instead of implying more precision than the data supports.
Audit the benchmark before trusting its score
Validate prompts, fixtures, tests and graders independently of the agent. Ask reviewers who did not author the task to solve it, inspect the expected answer and attempt valid alternatives. Look for ambiguous instructions, hidden implementation requirements, incomplete tests, stale dependencies and prompts that conflict with the behavior the grader rewards.
OpenAI’s July 8, 2026 audit of SWE-Bench Pro demonstrates why this step matters. On the 731-task public split, its automated pipeline flagged 200 tasks as broken, or 27.4%; a human annotation campaign identified 249, or 34.1%. OpenAI summarized the estimate as roughly 30% broken. The reported failure types included overly strict tests, underspecified prompts, low-coverage tests and misleading prompts. Those percentages describe that audit and dataset, not a universal defect rate for benchmarks.
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- False positive: the grader passes a task even though the requested behavior is absent.
- False negative: the grader rejects a valid solution because it assumes an unnecessary detail.
- Specification mismatch: the prompt asks for one behavior while the tests reward another.
- Environment defect: a dependency, credential, fixture or network service prevents a valid attempt.
Mark defective tasks, remove them from the headline score or publish corrected and original results separately. Never silently change a task after seeing model outputs.
Interpret leaderboard differences with uncertainty
A decimal score is not automatically precise. Infrastructure, task sampling, grader noise and configuration mismatches can be larger than the displayed difference.
In an analysis of Terminal-Bench 2.0, Anthropic found that raising the resource ceiling to three times the per-task specification reduced infrastructure error rates from 5.8% to 2.1% (p < 0.001). The associated score lift was modest and within noise (p = 0.40). Its conclusion was that “small score differences on agentic evals carry more uncertainty than the precision of the reported numbers suggests—especially as some confounders are simply too hard to control for.”
As a practical reading rule from that analysis, be skeptical of leaderboard gaps below three percentage points until the task set, grader, model version, harness and resource profile are documented and matched. This is not a universal statistical threshold; it is a caution derived from the cited infrastructure study. Prefer paired task-level comparisons, repeated runs and confidence intervals when deciding whether a change is real.
Compare evaluation approaches by the decision they support
No single evaluation style wins on every dimension. Choose the mix that answers your operational question.
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| Approach | Objective verifiability | Coverage and realism | Grader reliability | Variance and infrastructure sensitivity | Cost and latency visibility | Maintenance burden |
|---|---|---|---|---|---|---|
| Execution or state checks | High when the target state is specified | Can be narrow unless tasks cover realistic workflows | Usually high; depends on test completeness | Low grader variance, but tool and environment failures still matter | Easy to measure per run | Fixtures and tests require ongoing updates |
| Expert rubric review | Medium; depends on reviewer agreement | High for nuanced, open-ended work | Requires calibration and adjudication | Model and sampling variance remain visible | Human time can dominate | Rubrics and reviewer training need maintenance |
| LLM judge with rubric | Medium; validate against expert labels | Scales broad qualitative coverage | Can drift or show bias without monitoring | Judge-model changes add another variable | Lower marginal cost, but judge calls add latency | Judge prompts, model versions and calibration sets need versioning |
| Stochastic scenario suite | Varies by embedded checks | High variation and strong realism when scenarios are well designed | Depends on each scenario’s grader | Run-to-run variance is a central measurement concern | Many rollouts increase spend and runtime | Seeds, generators and scenario distributions must be maintained |
Use execution checks for hard guarantees, calibrated human or judge rubrics for synthesis, and stochastic scenarios when you need robustness across changing conditions. Keep their results in separate dimensions rather than hiding incompatible signals in one number.
A repeatable evaluation workflow
The following sequence turns the principles into an operating procedure.
- Write the task contract. State the user goal, starting fixture, allowed tools, required final state, forbidden side effects and acceptance evidence.
- Classify each criterion. Label it as deterministic, rubric-based or safety-critical, then select the corresponding checker and escalation path.
- Assemble representative tasks. Tag capability and difficulty, include realistic edge cases and reserve held-out tasks for leakage checks.
- Version every input. Assign immutable versions to prompts, fixtures, tools, scaffold, model snapshot and graders.
- Provision clean environments. Reset state between trials and record image, dependency, network and resource settings.
- Run with full tracing. Capture messages, tool calls, responses, errors, retries, timing, token counts and termination reason.
- Grade the external outcome. Inspect the resulting state or artifact first; use the agent’s final message only as an additional communication signal.
- Repeat where needed. Fix and record seeds for generated scenarios, run enough repetitions to expose variation and publish the aggregation rule.
- Review examples. Read successful and failed traces, investigate surprising scores and label grader or task defects separately from agent failures.
- Publish the context. Report task counts, exclusions, model and harness versions, resources, grader versions, costs, latency and uncertainty alongside the score.
Diagnose failures from traces, not just scores
When a run fails, classify the first causal break. A useful taxonomy is:
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Store the classification with the run. It prevents a grader defect from being counted as a model regression and shows which engineering change is likely to improve the outcome.
Maintain the eval as production software
Assign an owner and a review cadence. Re-run calibration examples after changing a rubric, judge model or test. Retire tasks when they become saturated, leaked or irrelevant, and add replacements before the suite loses coverage.
Separate regression testing from broad quality benchmarking. Regression tests should be small, stable and fast enough for every change. A quality benchmark can be larger, stochastic and more expensive because its purpose is to estimate capability across a distribution. Mixing the two encourages teams either to avoid useful variation or to slow every deployment.
Track contamination and data leakage. If an agent or model may have seen a task, mark the result as compromised and use a fresh held-out example. Preserve historical scores with their original configuration instead of rewriting them after the suite changes.
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