Make an agent benchmark rerunnable by treating the result as a property of the entire evaluation system—not just the model. Freeze and identify the task set, model snapshot, prompts, tools, harness, software, environment, budgets and run date; reset state between trials; publish auditable scoring rules; retain task-level outputs and traces; report variation and deviations; and make direct comparisons only when the relevant conditions match.
Why an agent score is difficult to reproduce
An agent benchmark score is produced by a model operating through a prompt, tool registry, harness, software stack and environment against a particular task set. Changing any one of those components can change the result. AgentCompass makes this separation explicit by recording trajectories, tool calls, usage and latency rather than treating the final percentage as a complete description of the experiment.
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AgentLab identifies several sources of drift that are easy to miss: package updates, in-place changes to an API model that keeps the same displayed name, and live websites whose content or behavior changes after the original run. A random seed can make your own sampling repeatable, but it cannot freeze a provider-side model update or an external website.
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Define a reproducibility contract before running agents
Write down the fields below before the first run. The contract becomes the authoritative description of what was evaluated and prevents details from being reconstructed from memory after an experiment.
| Component | Record at minimum | Why it matters |
|---|---|---|
| Tasks | Benchmark and task-set version, repository commit, split, sampling method, exclusions and seed | A changed task, hidden test or sampling rule changes the population being measured. |
| Model | Provider, exact model identifier or snapshot, region if relevant, inference parameters and run date | A stable display name does not guarantee a stable API model. |
| Prompts | System, developer and task prompts, prompt version or commit, formatting and truncation rules | Small wording or context-window changes can alter tool use and answers. |
| Tools and workflow | Tool names, schemas, versions, permissions, retry behavior, memory, state persistence and orchestration logic | Tool availability and agent policy are part of the tested system. |
| Harness | Harness name, version, repository commit and configuration file | Loop logic, timeout handling and parsing can affect success independently of the model. |
| Environment | Image or snapshot, operating system, dependency lockfile, browser or website versions, locale, geography, network policy and permissions | External services and dependency changes can invalidate an otherwise identical rerun. |
| Budgets | Token, time, tool-call, retry and compute limits | An agent allowed more attempts or context has a different task. |
| Scoring | Success definition, partial-credit rules, oracle or judge prompt and model, and aggregation | Different graders can produce different scores from the same trajectory. |
| Execution | Run date, number of repetitions, seeds, reset procedure, logs, outputs and traces | These details let others reproduce and diagnose the observed variation. |
Freeze the benchmark and task set
Pin an immutable task definition
Give the benchmark a release or commit identifier and archive the exact task files used. Record the split, task order, sampling algorithm, sample count, excluded tasks and exclusion reasons. If tasks are generated, save the generator version and the generated manifest; do not rely on regenerating the same files from a moving source.
For every task, retain a stable identifier and the inputs presented to the agent. If a task depends on a website, repository, package index or other external data, capture a snapshot or document the retrieval date and access policy. A live endpoint is not a fixed benchmark unless its state is restored or its responses are recorded.
Make sampling explicit
State whether the run covers the complete split or a sample. For a sample, report the sampling frame, sample size, replacement rule and seed. List any failures during task loading and explain whether they were retried, replaced or counted as failures. Without these details, two teams can both claim to have run the same benchmark while evaluating different tasks.
Version preparation and preprocessing
Publish scripts and instructions for downloading, cleaning and transforming data. Include checksums or equivalent identifiers for large files. Document normalization, filtering, truncation and secret removal, because a preprocessing change can alter both the agent input and the scoring target.
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Identify the model, prompts, tools and harness precisely
Model identity and provider behavior
Report the exact model identifier or snapshot, provider endpoint, inference parameters and date for every run. Include temperature, top-p or other sampling controls, maximum output tokens, stop conditions and any reasoning or tool-use settings. If the provider can update a model in place, state that bit-for-bit repetition is not guaranteed and preserve the response metadata returned by the API.
Prompt and context configuration
Store the complete system and developer messages, task prompt, tool descriptions and any examples. Record message ordering, injected policies, context truncation, conversation history and whether previous tasks can influence later prompts. A prompt version should change whenever wording, formatting or tool instructions change.
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List every callable tool, its schema and version, the permissions granted, authentication mode, timeout, retry and error behavior. Describe the control loop: when the agent may call a tool, how observations are inserted, how many turns are allowed and what happens after malformed output. Record memory stores, caches and state persistence. A benchmark that silently carries memory between episodes is measuring a different system from one that starts each episode empty.
Harness and software
Pin the harness repository commit and package versions with a lockfile or container image. Record operating-system details, runtime versions, wrappers, parsers and any local patches. Save the configuration used to launch the run, not merely the default configuration shipped with the harness.
Isolate and reset task state for every trial
State contamination is one of the most common causes of inflated or irreproducible results. Use a fresh or consistently restored state for each episode whenever a task can modify files, databases, browser profiles, accounts, caches or network resources.
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- Build a known baseline. Create an environment image or snapshot containing the operating system, dependencies, browser and task data. Record its identifier and checksum.
- Provision credentials and policies. Apply the same permissions, locale, geography and network rules for each run. Keep secrets outside logs while retaining a redacted description of their capabilities.
- Restore before each episode. Revert the filesystem, database, browser profile, working directory, service state and task-specific accounts to the baseline. Do not assume a process restart clears persistent state.
- Run one task in isolation. Give the agent only the tools and budget defined in the contract. Record all tool calls, responses, errors, timestamps and resource usage.
- Collect artifacts before cleanup. Save the task output, trajectory and relevant environment logs, then destroy or restore the state so the next episode cannot observe it.
- Verify the reset. Run automated checks for leftover files, database rows, cookies, tokens, processes and network-side effects. Treat a failed reset as an invalid episode rather than silently continuing.
FrontierHarness Eval describes a comparable workflow using identical fresh restores and preserving the policy under which each run was performed. That policy record matters when an agent has network access or other capabilities that can change the environment.
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Control randomness and resource budgets
Set and report documented seeds for task sampling, agent-side random choices and any stochastic simulator. Use deterministic settings where they exist, but do not describe a seeded run as fully deterministic when it calls an external API or live site. Provider sampling, service-side routing, latency, rate limits and changing content can still produce different trajectories.
Report every limit that can affect success:
- Input and output token limits, context-window handling and truncation.
- Wall-clock and per-step timeouts, including what happens on timeout.
- Maximum tool calls, retries, parallel calls and failed-call handling.
- Compute allocation, concurrency and queue limits when they influence latency or availability.
- Any stopping rule, early termination condition or human intervention.
Use the same budgets across systems when making a direct comparison. If one result used a larger budget, present it as a different experimental condition rather than as a superior model under identical evaluation.
Make scoring auditable
Define success and partial credit before execution
Publish the task-level success condition, required outputs, forbidden shortcuts and partial-credit rules before running the comparison. Prefer an executable oracle for machine-checkable outcomes. For open-ended work, provide a rubric with observable criteria and examples of passing, partially correct and failing outputs.
Validate an LLM judge
If an LLM evaluates responses, publish the judge model identifier or snapshot, prompt, rubric, temperature and parsing rules. Measure judge reliability on a separate calibration set with human or expert labels. Report agreement, disagreements and how borderline cases are resolved. A judge benchmark should not be treated as evidence that the evaluated agent is reliable; it measures the grader.
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PaperBench illustrates this separation. It 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, and it uses a separate benchmark for evaluating judges. Its published best-performing tested agent achieved a 21.0% average replication score on that benchmark in 2025. That figure is specific to PaperBench and must not be generalized to agent evaluations as a whole.
Preserve the evidence behind each score
Store the task identifier, raw agent output, final answer, tool trajectory, judge input and judge output, oracle result, error category and resource usage. Keep enough information to recompute the aggregate from task-level records. If privacy or licensing prevents publishing raw data, provide a documented access route and release redacted traces or hashes where possible.
Report variation instead of a single number
State how many runs and episodes were completed, the aggregation rule and the unit of analysis. Report a mean or median only with an appropriate uncertainty summary. Explain what the interval captures: task sampling, random seeds, run-to-run API variation, or another source. NeurIPS author guidance asks for an appropriate statistical-significance account and clear definitions of error bars; follow that principle rather than adding an unexplained confidence bar.
For heterogeneous tasks, show the distribution or per-task results in addition to the overall score. Separate infrastructure failures, invalid resets, judge failures and genuine task failures. If you rerun only a subset, label it as a replication sample rather than implying that the entire benchmark was repeated.
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A reader should be able to move from the paper or article to an exact command, environment and data-preparation procedure. NeurIPS author guidance states: “The instructions should contain the exact command and environment needed to run to reproduce the results.”
Release, or provide an access route for, the following:
- Benchmark manifest, task files or retrieval instructions, preparation scripts and checksums.
- Source code, harness commit, dependency lockfile or container image and configuration files.
- Prompt files, tool schemas, model settings, seeds, budgets and policy configuration.
- Scripts for the main results, baselines, scoring and table generation.
- Raw or task-level outputs, trajectories, logs, judge decisions and error classifications.
- Environment and network documentation, including reset scripts and required permissions.
- A versioned changelog that records deviations from the original run.
The CORE-Bench paper quotes Buckheit and Donoho (1995): “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.” For agent work, that environment also includes the external tools and policies through which the model acts.
Decide whether two scores are directly comparable
A direct ranking requires alignment on task definitions and versions, prompts, model settings, harness, environment, state reset, tools, permissions, budgets, scoring and relevant network or safety policies. If one condition differs, describe the result as a different evaluation condition and explain the likely effect; do not place the numbers in a single leaderboard without qualification.
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FrontierHarness Eval documents a concrete failure mode: earlier baselines had not recorded the applied network egress allowlist. New runs therefore defaulted to non-comparable status until a control was run under the same policy and environment. The practical remedy is a matched control: rerun the baseline with the new policy, or rerun the new system under the recorded baseline policy, then report both the policy difference and the matched result.
| Condition | Can you claim a direct comparison? | What to do |
|---|---|---|
| All material fields match and artifacts are available | Yes, subject to reported uncertainty | Compare task-level and aggregate results with the same scoring procedure. |
| Task version or split differs | No | Report separate results and explain the population change. |
| Model snapshot or provider behavior is unknown | No | Identify the uncertainty and avoid bit-for-bit or causal claims. |
| Network, permissions or environment policy differs | Not without a matched control | Run the control under aligned policy or mark the scores non-comparable. |
| Budgets or judge procedure differ | No | Normalize the condition or present a qualified, non-ranking analysis. |
What published benchmarks teach about scale and difficulty
Published figures are useful only with their scope attached. PaperBench covers 20 ICML 2024 Spotlight and Oral papers and 8,316 individually gradable tasks; its 21.0% average replication score for the best-performing tested agent was reported on PaperBench in 2025. CORE-Bench contains 270 tasks based on 90 scientific papers across computer science, social science and medicine. Its authors reported 21% accuracy on the hardest CORE-Bench task for the best reported agent. Neither percentage is a general measure of agent capability, and neither should be transferred to a different task distribution or scoring system.
A practical reporting template
The following compact record can accompany a paper, benchmark release or internal evaluation. Replace each value with the recorded fact, not an unverified default.
Quick Recap
benchmark:
name: benchmark-name
version: release-or-commit
split: split-name
task_manifest: manifest-hash
sampling: complete-or-sampled
seed: integer-or-not-applicable
model:
provider: provider-name
identifier: exact-model-id-or-snapshot
run_date_utc: date-and-time
inference: parameter-values
prompts:
version: prompt-commit
files: prompt-file-list
tools:
registry_version: version
permissions: documented-policy
harness:
commit: source-commit
environment: image-or-lockfile-id
workflow: state-and-memory-description
budgets:
tokens: limit
time: limit
tool_calls: limit
retries: limit
scoring:
oracle_or_judge: procedure-and-version
partial_credit: documented-rule
runs:
repetitions: count
reset: restore-procedure
artifacts: access-route
deviations: none-or-listed-changesWhy you cannot reproduce a score: symptoms and fixes
| Symptom | Likely cause | Diagnostic or fix |
|---|---|---|
| The same model name gives a different score weeks later | In-place provider update, changed routing or sampling | Compare model metadata and dates; use a pinned snapshot where available and disclose residual provider drift. |
| Only later tasks fail | Files, cookies, database rows or memory leaked between episodes | Restore a fresh snapshot per task and add automated contamination checks. |
| Two teams report different averages from the same release | Different task samples, exclusions or preprocessing | Compare manifests, seeds, split definitions and preparation checksums. |
| Scores change when the judge is rerun | Unpinned judge model, prompt or parsing rule | Pin judge settings, publish the rubric and measure agreement on a calibration set. |
| A baseline appears better only in one lab | Different network policy, permissions, locale or website state | Record policy and environment; run a matched control before ranking. |
| Aggregate score cannot be recomputed | Only a final percentage was saved | Retain task-level outputs, oracle or judge decisions and the aggregation script. |
| Reruns time out or exhaust quotas | Unreported token, time, tool-call or retry budgets | Publish limits and concurrency, then reproduce them exactly. |
Minimum checklist before publishing a result
- The task manifest, split, sampling, exclusions and preparation are versioned.
- The model snapshot or exact identifier, prompts, tools, harness commit and run date are recorded.
- The environment, dependencies, browser or website state, locale, network policy and permissions are documented.
- Each episode starts from a verified clean state.
- Seeds, stochastic settings and all resource budgets are stated.
- Success, partial credit and judge reliability are defined before comparison.
- Task-level outputs, traces, scores and failures are retained.
- Repeated runs, aggregation and uncertainty are reported with their interpretation.
- Any deviation from a baseline is explicit, and direct comparisons are limited to aligned conditions.
- Exact commands, environment instructions and artifact access are available to a new evaluator.
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