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An agent benchmark becomes reproducible when another team can identify the exact tasks, model snapshot, prompts, tools, harness, environment, budgets, scoring procedure, and run date—and then rerun the evaluation from a clean state. Freeze those inputs, reset state between trials, preserve task-level traces and outputs, validate any automated judge, and report variation and deviations. A score belongs to that complete setup, not to the model alone.
What reproducibility means for an agent benchmark
Traditional model evaluations often assume that a dataset, a model checkpoint, and a script are enough. Agents add changing components: they browse websites, call tools, write files, use memory, consume variable token and time budgets, and operate inside environments that may change between runs. The measured result is therefore a property of the model, task set, prompt, harness, tools, policies, environment, and scoring system together.
A reproducible benchmark has two practical properties:
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- Auditability: someone can inspect individual tasks, trajectories, tool calls, and scoring decisions to explain how the aggregate score was produced.
AgentCompass illustrates the second requirement by separating the model, benchmark, harness, and environment while recording trajectories, tool calls, usage, and latency. That separation makes it possible to identify whether a score changed because of the agent policy or because another layer changed.
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Freeze the benchmark and task set
Assign immutable identities
Record the benchmark name, task-set release, split, repository commit, and any local patch. A label such as “Web tasks” is not an identity: the underlying pages, instructions, exclusions, or expected outputs may change. Use a version or commit that another researcher can resolve later.
For every run, preserve:
- Benchmark and task-set version or commit.
- Task split and the exact list of task identifiers.
- Sampling method, sample size, ordering rule, and random seed.
- Documented exclusions, retries, timeouts, and failed setup tasks.
- Data-preparation code and checksums for generated files.
Keep task content stable
Store task instructions, reference files, expected artifacts, and evaluation metadata in a versioned location. If a task depends on a website, package registry, database, or other live service, capture the relevant page or dataset when licensing permits, or record a dated snapshot and the retrieval procedure. A live site can change even when the benchmark repository does not.
Do not silently remove difficult or broken tasks after seeing results. Define exclusion rules before running the comparison, report every excluded identifier, and distinguish an environment failure from an agent failure.
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Identify the evaluated agent precisely
Model and provider details
Report the model’s exact identifier, edition, context limit where relevant, and snapshot or release date. Hosted APIs may update a model in place while keeping the displayed name. AgentLab identifies these in-place API changes as a reproducibility risk. If a provider cannot guarantee a fixed snapshot, state that the rerun can be behaviorally similar but not bit-for-bit identical.
Include inference settings that can affect decisions:
- Sampling temperature, top-p or equivalent controls, and maximum output tokens.
- Reasoning, deliberation, or effort settings exposed by the provider.
- Stop sequences, response formats, tool-choice policy, and parallel-call behavior.
- System, developer, task, and repair prompts, with their version identifiers.
- Conversation truncation, summarization, memory, and persistence rules.
Describe the workflow, not just the model
Two evaluations using the same model can produce different results if one retries failed calls, summarizes history, plans before acting, or allows a different tool sequence. Document the agent workflow, planner and executor roles, reflection or self-critique steps, maximum turns, retry logic, and termination conditions.
List every tool by name and version, including browser automation, code execution, retrieval, file access, APIs, and custom tools. Record schemas, authentication mode, default parameters, and whether a tool can access the network or host filesystem. AgentCompass’s trace-oriented design is useful because a run record can show which tool calls actually occurred rather than merely listing the tools that were available.
Make the environment deterministic enough to study
Pin software and system dependencies
Publish the harness commit, language runtime, package lockfile, operating-system image, browser and driver versions, and relevant system libraries. Container or virtual-machine images should be identified by immutable digest where possible. Include locale, timezone, character encoding, and hardware or accelerator details when they can change behavior or latency.
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AgentLab notes that package upgrades, provider-side model changes, and live website changes can all alter outcomes. A requirements file alone is not sufficient if it allows floating versions; resolve and retain the exact environment used for the reported run.
Control permissions and network policy
State whether the agent had internet access, which domains were allowed, whether uploads and downloads were possible, and what credentials or secrets were mounted. Record filesystem permissions, sandbox boundaries, rate limits, and concurrency. A model that can search the open web is not being evaluated under the same conditions as one restricted to a fixture server.
FrontierHarness Eval documents a concrete comparability problem: earlier baselines had not recorded the applied egress allowlist, so later runs could not automatically be treated as comparable. The remedy is to preserve the policy with the run and execute a matched control under the same policy and environment.
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State contamination can make later tasks easier or harder. Restore a fresh environment, browser profile, database, filesystem, credentials, and memory store for every independent episode. If warm starts are part of the intended protocol, define exactly what persists and apply it consistently to every system.
Record the reset mechanism and verify it. Useful checks include a clean task directory, empty agent memory, a known database snapshot, cleared cookies, and identical initial configuration. Preserve the policy under which each run was performed; a fresh restore does not compensate for an undocumented policy change.
Handle randomness and changing external services
Use seeds, but do not overclaim what they guarantee
Set and report seeds for task sampling, agent-side pseudorandomness, tool simulators, and any framework component that supports seeding. State the random-number libraries and whether each worker receives a derived seed. Also report sampling rules for model calls.
A seed cannot freeze a remote API, a live website, server-side load balancing, or a provider’s silently updated model. Treat the seed as one control among several, not as proof of deterministic behavior.
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Run repeated trials
Repeat evaluations when sampling, tool failures, task selection, or model variability can affect the result. Report the number of runs, the aggregation rule, and the source of variation. For example, explain whether an interval reflects task sampling, independent seeds, run-to-run API variation, or all three combined.
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Do not average away operational failures. Report timeout, rate-limit, authentication, and environment-setup rates separately from task success. A system that reaches a high score only after many retries has a different cost and reliability profile from one that succeeds on the first attempt.
Define scoring before the comparison
Publish task-level criteria
For each task, specify what counts as success, what earns partial credit, and what evidence is required. An executable oracle is preferable when one can be written. Otherwise, publish a human-readable rubric and the exact artifact or trace that a reviewer must inspect.
PaperBench demonstrates a detailed approach: its benchmark contains 8,316 individually gradable rubric tasks drawn from 20 ICML 2024 Spotlight and Oral papers, with hierarchical rubrics co-developed with the original paper authors. The structure makes a total score traceable to concrete requirements rather than a single impressionistic judgment.
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If an LLM grades outputs, publish the judge model and version, prompt, rubric, context supplied to the judge, temperature and other inference settings, and tie-breaking procedure. Test the judge on a separate calibration set containing clear successes, clear failures, and borderline cases. Measure agreement with expert labels or another predefined reference, and report disagreements.
PaperBench also describes a separate benchmark for evaluating judges. The lesson is methodological: a judge is an evaluated component of the benchmark, not an invisible implementation detail. Keep the judge’s raw decisions and explanations so a disputed aggregate can be audited.
Separate completion from quality and cost
Report whether a task was completed, whether the final artifact met the rubric, and whether the run exceeded a resource or policy limit. Keep token use, wall-clock time, tool calls, retries, and compute consumption as separate measures. Combining them into one undisclosed score hides useful trade-offs.
Set and disclose resource budgets
Budgets can change an agent’s strategy and therefore its score. State the per-task and per-run limits for:
- Input and output tokens, including hidden or reasoning tokens when the provider exposes them.
- Wall-clock time and idle timeouts.
- Number of model turns, tool calls, browser actions, and retries.
- Compute, memory, concurrency, and storage.
- External-service quotas, spend limits, and rate limits.
Explain what happens at a limit: graceful termination, failure, truncation, or an allowed retry. Apply the same budget accounting to every system in a direct comparison. If one harness counts failed tool calls and another does not, their reported tool budgets are not equivalent.
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Preserve rerunnable artifacts
Minimum artifact set
A publication should provide code or a practical alternative access route, task data or documented access and preparation instructions, environment definitions, run configuration, and scripts for the main results and baselines. NeurIPS author guidance states: “The instructions should contain the exact command and environment needed to run to reproduce the results.”
Retain the following for each run:
- Resolved configuration, prompts, model identifier, tool registry, budgets, and policies.
- Task list, seed values, start and end timestamps, and software image identifiers.
- Raw model responses, tool requests and responses, screenshots or files when permitted, and final outputs.
- Per-task scores, judge decisions, error classifications, token and latency records.
- Aggregate scripts that regenerate tables, plots, intervals, and reported rankings.
Protect sensitive information without destroying provenance
Remove API keys, personal data, and confidential task content before release, but document what was redacted and how a rerunner can obtain an equivalent fixture. Hash large or restricted artifacts, publish schemas, and provide deterministic preparation scripts. If full release is impossible, offer a controlled access route and state exactly which analyses cannot be independently checked.
Buckheit and Donoho’s formulation, quoted in the CORE-Bench paper, is apt: “An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.”
Report variation, failures, and deviations
Use an explicit aggregation rule
State whether the headline number is a macro-average across tasks, a micro-average across rubric items, a median, a best run, or another statistic. Give the denominator and explain how missing, timed-out, and invalid tasks enter the calculation. If a confidence interval or error bar is shown, define what it captures and how it was computed.
Classify failures
Separate at least these categories:
- Agent error: the system acted incorrectly under a functioning environment.
- Tool or service failure: an API, browser, or dependency failed independently of the agent’s decision.
- Harness error: orchestration, logging, reset, or scoring malfunctioned.
- Task or fixture error: the task was malformed, unavailable, or inconsistent with its rubric.
- Budget or policy termination: the run exceeded a declared limit.
Publish the counts and task identifiers for each category. A rerun should not silently convert infrastructure failures into agent failures or omit them from the denominator.
Document material deviations
If a rerun changes a prompt, model snapshot, tool version, website, network policy, task split, budget, or scoring rule, mark the result as a new condition. Explain the difference and avoid presenting the number as a direct replication. Preserve the original configuration so readers can distinguish a faithful rerun from an updated experiment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide whether two scores are comparable
Before ranking systems, compare the conditions that can change behavior. The following matrix is a practical gate:
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|---|---|---|
| Tasks | Same benchmark release, split, identifiers, instructions, and exclusions? | Use a matched subset or label the scores non-comparable. |
| Model and prompts | Same model snapshot, system prompt, inference settings, and memory policy? | Attribute the result to the full configuration, not the model alone. |
| Harness and tools | Same orchestration, tool schemas, versions, retries, and call accounting? | Re-run under one harness or report a controlled ablation. |
| Environment | Same image, browser, data, permissions, locale, network policy, and reset procedure? | Disclose the environmental effect and do not claim a clean ranking. |
| Budgets | Same token, time, turn, tool, retry, and compute limits? | Normalize budgets or present separate results. |
| Scoring | Same oracle or rubric, judge model, calibration, and missing-task treatment? | Re-score shared outputs with one procedure. |
When exact alignment is impossible, run a matched control that changes only the disputed condition. This turns a vague comparability warning into an attributable experiment.
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What published benchmarks show
PaperBench
OpenAI’s 2025 PaperBench page reports 20 ICML 2024 Spotlight and Oral papers represented through 8,316 individually gradable tasks and hierarchical rubrics developed with the original paper authors. It reports a 21.0% average replication score for the best-performing tested agent. That figure applies to PaperBench’s replication tasks and scoring procedure; it is not a general estimate of agent capability.
CORE-Bench
CORE-Bench authors describe 270 tasks based on 90 scientific papers across computer science, social science, and medicine. The best reported agent reached 21% accuracy on the hardest CORE-Bench task. That result is specific to that task and benchmark and should not be generalized to all agent evaluations.
These examples differ in subject matter and rubric design, but both illustrate why task-level definitions and artifacts matter: an aggregate percentage is meaningful only when readers can determine what was attempted, how it was graded, and under which environment.
A practical reporting template
Use one machine-readable manifest and a human-readable methods section. At minimum, fill every field below for each published result:
| Area | Record |
|---|---|
| Run identity | Run ID, date and time zone, operator or automation version. |
| Tasks | Benchmark release or commit, split, task IDs, sampling rule, exclusions, seed. |
| Agent | Model identifier or snapshot, prompts, workflow, memory and persistence behavior. |
| Tools | Registry, schemas, versions, credentials mode, network and filesystem access. |
| Software | Harness commit, runtime, lockfile, container or image digest, browser and driver. |
| Environment | OS, hardware, locale, timezone, data snapshot, permissions, reset procedure. |
| Budgets | Tokens, time, turns, tool calls, retries, concurrency, compute and service quotas. |
| Scoring | Oracle or rubric, partial credit, judge configuration, calibration and tie handling. |
| Outputs | Raw responses, traces, tool logs, final artifacts, per-task scores and failures. |
| Statistics | Run count, aggregation, uncertainty method and definition of error bars. |
| Deviations | Any changed condition, unavailable artifact, redaction or non-comparable baseline. |
Have a second person perform a clean-room rerun from the published instructions. Record every point where they need undocumented knowledge. Those gaps are reproducibility defects, not merely documentation polish.
Why an agent benchmark score still may not match
The provider changed the model
Check the model snapshot or provider release history. If the displayed identifier stayed the same, compare request metadata, timestamps, and a small fixed probe set. Mark the result as a provider-version change when a fixed snapshot cannot be recovered.
The environment drifted
Compare image digests, package locks, browser versions, locale, task fixtures, website content, and network policy. Restore the original image and captured fixtures before changing the agent configuration.
State leaked between tasks
Inspect memory stores, browser profiles, databases, temporary directories, caches, and credentials. Re-run isolated tasks with verified clean-state checks, then run a deliberate warm-start condition only if persistent state is part of the research question.
The judge changed the result
Re-score retained outputs with the original judge prompt and version. Compare judge decisions with calibration labels and report disagreements. If the original judge is unavailable, present the new score as a different scoring condition rather than a repaired original.
Budgets or retries were counted differently
Reconcile token accounting, hidden reasoning usage, tool-call counts, timeout behavior, and retry rules. A small difference in termination policy can produce a large score change on long-horizon tasks.
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