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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDo not treat an agent’s “done” message as evidence that a research task succeeded. Define an independently checkable outcome, run the agent in a controlled and logged environment, and have a separate evaluator inspect the answer, sources, files, or final application state. Repeat the task when results can vary, measure confidence and robustness, and publish the setup with the score. This method tells you what the agent actually accomplished instead of what it claimed.
What counts as proof that an AI agent completed research?
An agent’s narration is an observation about its own behavior, not an outcome measure. A reliable evaluation asks whether an externally checkable condition is true: the answer matches a reference, the cited sources support the claims, a required file has the right contents, an experiment can be reproduced, or a computer workflow leaves the intended final state.
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Use a separate evaluator whenever possible. OSWorld, for example, describes a sequence of task initialization, agent interaction, post-processing and an evaluation function, rather than accepting the agent’s account of what it did. Its project page also separates team-evaluated official results from self-reported progress and results: OSWorld methodology and results.
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An evidence hierarchy
- Final-state or answer check: A deterministic test confirms the exact answer, database value, file, UI state or other required result.
- Independently reviewed artifact: A human or validated rubric checks a report, code, calculations, citations or replication package.
- Trace and provenance: Logs show the prompts, tool calls, retrieved documents, URLs, files and timestamps needed to reproduce the work.
- Agent explanation: The agent describes its actions and confidence. This can help diagnose failures, but it is the weakest evidence of success when unverified.
Collect the first three even when the fourth sounds convincing. A polished explanation can accompany a wrong answer, an unsupported citation or an unchanged application state.
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1. Turn “good at research” into a bounded claim
Start by stating exactly what you want to learn. “This agent is good at research” is too broad to test. A useful claim names the task family, the environment and the pass condition.
Define the task and its boundary
Write the task as a self-contained request with a known starting state. Specify whether the agent may browse the open web, use a provided document set, execute code, edit files or interact with desktop applications. State what information is hidden from the agent and what it may learn during development.
Then define measurable criteria. Depending on the job, these may include:
- an exact answer or a set of required facts;
- source quality, with each important claim linked to supporting evidence;
- completeness against a human-authored rubric;
- a reproducible artifact such as code, a dataset transformation or a report;
- a successful final state in a browser, desktop application or file system; and
- an acceptable cost, latency or tool-use budget.
Decide in advance what counts as full credit, partial credit and failure. For example, a report might receive partial credit for identifying the right conclusion but lose points for missing required sources; a file-editing task might fail if the final file is saved in the wrong location even when the agent’s text claims success.
2. Use representative, held-out tasks
A score is meaningful only for tasks resembling the work you care about. Build a test set that covers the distribution of requests your users will make, not just tasks that are easy to grade or flattering to one model.
Cover the relevant task families
- Web research: finding obscure facts, checking conflicting sources and preserving provenance;
- Document and file work: extracting information, transforming files and producing a required output;
- Synthesis: combining several sources into a structured, complete answer;
- Computer use: carrying out multi-step workflows in real applications; and
- Experimental research: running code, recording settings and producing results that another person can reproduce.
Keep evaluation tasks held out from prompts, demonstrations and iterative development feedback where possible. If examples leak, an agent may memorize answers or tailor its behavior to the benchmark rather than acquire the target capability. OpenAI’s BrowseComp design emphasizes difficult questions that are comparatively easy to verify and asks users not to reveal task examples in ways that contribute to dataset leakage: BrowseComp benchmark design.
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Sample across difficulty and ambiguity
Include straightforward, difficult and adversarial cases. Vary source quality, document length, tool availability and the number of steps. Include requests with incomplete or conflicting evidence so you can test whether the agent reports uncertainty instead of inventing a definitive answer. Keep a fixed test split for final reporting and a separate development split for prompt or scaffold changes.
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3. Build an independent grader
The grader must be separated from the agent’s narrative and, ideally, from the agent’s execution process. Use the strongest checking method available for each criterion.
Prefer deterministic checks
When an exact answer or state exists, write a deterministic evaluator. Examples include comparing a normalized answer to an accepted set, checking that required files exist and contain specified values, validating a schema, running unit tests, or inspecting whether an application reached a target state. Deterministic checks are repeatable and expose fewer disagreements than subjective scoring.
Use explicit rubrics for open-ended work
Research reports and replications often cannot be reduced to one string comparison. Decompose the judgment into individually gradable criteria: factual accuracy, coverage of required points, source support, methodological correctness, clarity and reproducibility. Assign points and define what evidence earns each point. Ask graders to cite the exact passage or artifact that supports every awarded point.
PaperBench illustrates this approach. It asks agents to replicate 20 ICML 2024 Spotlight and Oral papers and divides the work into 8,316 individually gradable subtasks. Its hierarchical rubrics make a large, open-ended replication task inspectable at a finer level. The study reports an average replication score of 21.0% for the best-performing setup it tested; that is a benchmark- and study-specific result, not a universal ranking of research agents: PaperBench description and results.
Validate an LLM judge before relying on it
An automated language-model judge can reduce review time, but its score is another model output that requires validation. Create a separately labeled set of examples, compare the judge with qualified human judgments, measure disagreement and manually inspect disputed cases. Test whether the judge favors fluent prose, a particular citation style or the model family that generated the answer.
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PaperBench reports assessing its judging system with a separate judge benchmark. That extra assessment is important: a judge score should not be accepted merely because a judge generated it. For high-stakes decisions, retain human review for disagreements, unusually high scores, and claims that cannot be checked mechanically.
4. Run every task in a controlled, logged setup
Record enough information for another evaluator to reproduce the run. The agent should not be able to rewrite or delete the evidence used to score it.
Capture the complete configuration
- Exact task text and any attached files;
- model name and version, system instructions, prompt template and scaffold;
- available tools, tool versions, network and data permissions;
- initial browser, desktop, file-system or container state;
- random seed or sampling settings when exposed;
- start and end times, timeout and token or API limits;
- all tool calls, retrieved URLs or documents, intermediate files and produced artifacts; and
- grader version, rubric, thresholds and aggregation method.
Store raw traces and artifacts before generating a summary. A final report should link each important claim to the retrieved document or file that supports it. For computer-use tasks, score the post-run state with an evaluator independent of the agent’s message. OSWorld’s task design is a concrete example of separating initialization, interaction, post-processing and evaluation: OSWorld.
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Do not expose hidden answers, grader code or held-out task examples through error messages. Prevent the agent from editing the reference files or submitting its own score. If the environment permits network access, log requests and decide whether search-engine caches, private accounts or external APIs are allowed. Report these choices because they can change difficulty as much as the model does.
5. Repeat runs and measure reliability
One successful run can be luck. Repeat each task when sampling, tool timing, web results or other stochastic factors can change the outcome. Report per-task results, not only a single average.
Report variation, not just a mean
For repeated runs, show the number of runs, success rate, variation and a confidence interval where appropriate. A model that succeeds on 7 of 10 runs is operationally different from one that succeeds on 70 of 70, even if both are described as “70% accurate” in a headline. Preserve the individual outcomes so readers can see whether failures cluster on a small set of tasks or occur randomly.
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Check calibration
Ask the agent to state a probability or confidence category before grading, then compare confidence with actual correctness. A calibrated system is more useful than an overconfident one at the same raw accuracy because users can route uncertain cases to review. Report calibration separately from accuracy; confidence statements are not evidence that an answer is true.
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Probe robustness
Apply small, pre-specified perturbations: rephrase the request, reorder irrelevant documents, change a filename, alter a harmless UI layout detail or vary the available tool description. The intended answer should remain stable when the task’s substance is unchanged. The HAL GAIA reliability dashboard discusses consistency across repeated runs, confidence calibration and robustness to task-format perturbations: GAIA reliability dashboard.
6. Publish a reproducible scorecard
A score without context invites overclaiming. Publish the information a reader needs to reproduce or appropriately limit the result.
| Scorecard field | What to state | Why it matters |
|---|---|---|
| Task set | Benchmark or internal name, version, domains, sample size, difficulty and held-out policy | Defines what the score represents and exposes leakage risk |
| Environment | Applications, files, network access, tools, scaffold and initial state | Different environments can make the same task easier or harder |
| Agent | Model/version, prompt, sampling settings and tool limits | Prevents a result from being attributed to an unspecified system |
| Grading | Deterministic checks or rubric, judge model, human review and disagreement policy | Shows how correctness was established |
| Runs | Runs per task, randomization, aggregation and confidence intervals | Separates repeatable ability from a lucky run |
| Resources | Total API cost, latency and compute or browsing limits | Reveals the operational trade-off behind the score |
| Failures | Representative traces, missing evidence, fabricated results and grader disagreements | Shows where the agent breaks instead of hiding failures in an average |
The HAL GAIA leaderboard labels results reproduced by the HAL team as verified and displays accuracy confidence intervals for agents with multiple runs, along with total API cost: GAIA leaderboard. Follow that model by distinguishing independently reproduced results from claims supplied by a system vendor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What major benchmarks actually measure
Benchmarks are task families, not interchangeable IQ scores. Their scores should not be placed on one league table because they use different objectives, environments, graders and leakage risks.
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|---|---|---|---|
| BrowseComp | Finding hard-to-locate web answers that are comparatively easy to verify | Its design uses an “asymmetry of verification”: questions are difficult to solve but have answers that can be checked reliably. The authors caution against revealing task examples in ways that enable leakage. | It does not measure desktop manipulation, file workflows or full experimental replication. |
| OSWorld | Computer-use outcomes across real applications | It describes 369 tasks involving web and desktop applications, operating-system file I/O and workflows across multiple applications. Evaluation includes initialization, interaction, post-processing and an evaluation function. The site distinguishes official team-evaluated results from self-reported results. | It is not a comprehensive measure of research quality, source synthesis or scientific validity. |
| PaperBench | Research replication against detailed subtasks | It covers 20 ICML 2024 Spotlight and Oral papers and 8,316 individually gradable subtasks. The cited study reports a 21.0% average replication score for its best tested setup and describes hierarchical rubrics plus a separate judge assessment. | The 21.0% figure is specific to that study, benchmark version and setup; it is not a current universal ranking. |
| GAIA | General assistant tasks with reliability reporting | The HAL leaderboard marks reproduced results as verified and presents confidence intervals when multiple runs are available, as well as total API cost. | A leaderboard position without task, environment, grader and run details cannot predict performance on your workload. |
| MLR-Bench | Machine-learning research tasks reviewed with rubrics | The preprint describes 201 tasks drawn from machine-learning workshops and uses rubric-guided language-model review. Its authors report that, in their tested cases, coding agents frequently produced fabricated or invalid experimental results. | That observation is a finding from the benchmark study, not a failure rate that generalizes to every research agent. |
How to compare benchmark claims responsibly
Use the same comparison axes for every result:
- Capability: browsing, synthesis, computer interaction, coding or experimental replication;
- Checkability: deterministic answer, final-state test, rubric or human judgment;
- Representativeness: similarity to your users’ tasks, documents and tools;
- Leakage resistance: whether task examples or answers could have entered training or prompting;
- Reproducibility: availability of tasks, environment, grader and artifacts for independent reruns; and
- Cost and repeatability: resources required to obtain the reported score and whether repeated runs were performed.
Two agents can have different strengths without either score being “higher quality” in general. A strong BrowseComp result may indicate effective web retrieval while saying little about editing a spreadsheet. A strong OSWorld result may show reliable UI actions while saying little about whether a literature review is complete. A PaperBench score tests a demanding replication distribution, not every form of research assistance.
Failure modes your evaluation should expose
Confidently wrong or unsupported answers
Check every material claim against its cited source. Penalize citations that do not contain the claimed fact, sources that cannot be retrieved, and conclusions that exceed the evidence. Include tasks where the correct response is uncertainty or a request for clarification.
Fabricated experiments
An agent can produce plausible tables, charts or statistical language without running the claimed analysis. Require executable code, input data, environment details, logs and output files, then rerun the procedure independently. MLR-Bench reports fabricated or invalid experimental results in the tested coding-agent cases, which is why artifact checks matter: MLR-Bench preprint.
Partial completion hidden by fluent narration
Compare the final artifact and application state with the task rubric. A report that mentions ten required sources but contains evidence for only six should receive partial credit, regardless of its confident summary. For UI tasks, inspect the actual saved file or state rather than the agent’s description of clicks.
Grader errors
Deterministic checks can encode the wrong normalization or miss a meaningful edge case. Human rubrics can be inconsistent, and language-model judges can reward style over substance. Version the grader, test it on known examples and retain adjudicated disagreements.
Benchmark overfitting and leakage
Unexpectedly high performance on a public task set may reflect memorized examples or a scaffold tuned to its quirks. Hold out private tasks, vary wording and inspect traces for answer retrieval rather than problem solving. Do not publish hidden examples merely to make a result easier to understand.
Evaluation tooling and what it can—and cannot—prove
Evaluation platforms can organize datasets, traces and score calculations, but using a platform does not make a result independent automatically. LangSmith documents offline evaluations on curated datasets, reference outputs, version comparisons and heuristic checks of expected agent tool calls: LangSmith evaluation types. Braintrust describes defining agent tasks and test cases and running evaluation experiments: Braintrust agent evaluation.
These are vendor-described workflow capabilities, not evidence that one platform or agent is superior. You still need a defensible task sample, a grader tested against human judgments, protected held-out data, independent artifact checks and a transparent scorecard. A platform can make those practices easier to operate; it cannot replace them.
A practical decision rule
Approve an agent for a research workflow only when its independently checked success rate, calibration, robustness, cost and failure modes fit that workflow’s risk. For low-stakes information gathering, source verification and a human spot check may be sufficient. For experiments, regulated decisions or irreversible computer actions, require reproducible artifacts, deterministic state checks and escalation of uncertain cases. The question is never simply whether the agent says it finished. The question is whether an evaluator can verify the requested outcome after the agent is done.
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