The Tool Desk
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Do not judge a research agent by its “done” message. Judge a separately checkable answer, artifact, or final state produced under a logged test, then have an independent evaluator verify it. Repeat the task when results can vary, report failures and costs, and treat benchmark scores as evidence about a defined task distribution—not proof of general research ability.
What a credible evaluation actually measures
An agent can claim that it searched sources, ran code, or completed a workflow without having done any of those things correctly. A success message is a narration, not an outcome measure. A defensible evaluation starts by defining an outcome that exists outside the agent’s own report:
- Exact answer: a value, classification, citation, or set of facts that can be checked against an independent reference.
- Evidence-backed synthesis: a response whose claims, source quality, coverage, and reasoning satisfy a written rubric.
- Reproducible artifact: files, code, data, calculations, or a report that another person can inspect and rerun.
- Final state: a document edited, form submitted, file created, or other state that an evaluator can inspect directly.
Define partial credit and failure before running the agent. For example, a research brief might receive separate points for factual accuracy, primary-source quality, coverage of required questions, citation correctness, and disclosure of uncertainty. A task should not pass merely because the prose sounds plausible.
OSWorld illustrates this separation for computer-use tasks: it specifies task setup, agent interaction, post-processing, and an evaluation function, and distinguishes official results evaluated by its team under unified settings from self-reported results. The same principle applies to research agents: score the resulting state or evidence, not the agent’s account of what happened.
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Choose tasks that match the capability you claim
“Good at research” is too broad to test. Break the claim into task families and sample each family that matters to your users.
| Benchmark or task family | Primary capability | How correctness is checked | What it cannot establish |
|---|---|---|---|
| BrowseComp | Finding difficult, obscure answers on the web | Questions are designed to be hard to solve but comparatively easy to verify | It does not measure long-running computer workflows, experimental replication, or every form of synthesis |
| OSWorld | Using real web and desktop applications, files, and multi-application workflows | An evaluator checks the resulting computer state after interaction and post-processing | It is not a comprehensive measure of source quality or research reasoning |
| PaperBench | Replicating published machine-learning research | Hierarchical, individually gradable subtasks are assessed against detailed rubrics | Its score is specific to the selected papers, subtasks, tools, and study setup |
| Literature synthesis | Combining evidence into a useful, balanced answer | Human-authored criteria for accuracy, completeness, source quality, and uncertainty | Open-ended grading is less deterministic and requires evaluator training |
| Research with execution | Running experiments or analyses and reporting valid results | Independent reruns, file inspection, and consistency checks | A polished report alone cannot prove that an experiment ran |
Never combine scores from these rows into a single league table. They exercise different abilities, operate in different environments, and often use different graders. A high browsing score does not imply reliable file manipulation; successful computer interaction does not establish that a literature review is complete.
Use held-out, representative tasks
Build a test set from the requests your users actually make: web research, document and file work, synthesis, and—when relevant—executing analyses. Keep evaluation tasks and answers out of development prompts and feedback wherever possible. BrowseComp explicitly cautions against revealing task examples in ways that enable benchmark leakage. Use fresh or private tasks for final measurement, and record how they were sampled so that “representative” is a claim others can examine.
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Set the boundary of each task
Write the starting state, allowed resources, time limit, tool permissions, required deliverables, and stopping condition. State whether the agent may ask questions, whether browsing is unrestricted, and what counts as a valid citation. Decide in advance how to score a correct answer with weak sources, a well-sourced answer that omits a required fact, and a reproducible artifact with a minor formatting defect.
A repeatable evaluation protocol
1. Specify the claim and pass criteria
Turn a product claim into an observable statement such as: “On held-out policy questions, the agent will answer at least 85% correctly, cite a primary source for each material claim, and identify uncertainty.” List every required output and assign points before the run. Include explicit failure conditions—for example, an invented citation, an unexecuted experiment presented as completed, or a final file saved to the wrong location.
2. Create an independent reference or rubric
For exact-answer tasks, use deterministic checks whenever possible: normalized string comparison, database lookup, unit tests, file hashes, schema validation, or a state predicate. For open-ended work, have subject-matter experts write a rubric with observable criteria and examples of full, partial, and zero credit. Keep the reference answer and hidden checks outside the agent’s context.
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An LLM judge can help with scale, but its output is another measurement that needs validation. Compare its decisions with human labels on a separately labeled judge set, measure agreement, inspect disagreements, and revise ambiguous criteria. PaperBench reports hierarchical rubrics and a separate assessment of its judging system; a judge score should never be accepted solely because the judge generated it.
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Record the exact task text, initial state, model and version, system and developer instructions, scaffold or orchestration code, tool definitions, data and network access, temperature or sampling settings, timeout, and run identifier. Save the complete interaction trace, retrieved URLs or documents, intermediate files, final files, tool errors, and evaluator output. For computer tasks, score the post-task state independently rather than trusting a narrative such as “I clicked submit.” The agent evaluation documentation describes a practical pattern of datasets, graders, and repeatable evaluation runs.
4. Verify sources and artifacts
Check that every cited source exists, supports the nearby claim, and was available under the test’s access rules. Detect citations that point to a search-result snippet rather than the underlying document. For generated files, inspect metadata, formulas, code, and dependencies; rerun critical calculations in a clean environment. If the task requires browsing, preserve the retrieved page or a timestamped snapshot when permitted so later page changes do not silently alter the grade.
5. Repeat and analyze variation
Run each task more than once when sampling, tool timing, retrieval, or external state can change the outcome. Report per-task results, aggregate accuracy, variation or confidence intervals, total and per-task cost, and failure categories. A single successful run can hide a low completion probability. If you have n independent binary runs with observed success rate p, publish an uncertainty interval rather than presenting p as exact; choose and state the interval method in advance.
6. Test calibration and robustness
Ask the agent to express confidence or identify claims it could not verify. Calibration is useful only if confidence tracks correctness: a system that says “90%” should be right substantially more often than one that says “60%,” across comparable bins. Also perturb task wording, document order, filenames, interface layout, or irrelevant formatting. A large performance drop after a harmless change indicates brittleness rather than robust capability. The HAL GAIA reliability dashboard emphasizes consistency across repeated runs, confidence calibration, and robustness to task-format perturbations.
7. Publish a reproducible scorecard
Make the evaluation auditable. Include the task source and sampling rule, benchmark version or date, sample size, inclusion and exclusion rules, model and scaffold, tools and access, grader definitions, aggregation method, run count, confidence intervals, cost, and limitations. Publish representative successes and failures, with enough artifacts for an independent party to check the result. If only some runs were reproduced externally, label those results clearly.
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What the major benchmark examples tell you
OSWorld: outcome verification for computer workflows
OSWorld describes 369 tasks involving real web and desktop applications, operating-system file I/O, and workflows across multiple applications. Its evaluation path includes initialization, agent interaction, post-processing, and an evaluation function. The project separates official team-evaluated results from self-reported progress and results. Use it to test whether an agent can reach a required computer state, not as a complete measure of research quality.
PaperBench: decomposing research replication
PaperBench asks agents to replicate 20 ICML 2024 Spotlight and Oral papers through 8,316 individually gradable subtasks. The study reports a 21.0% average replication score for the best-performing setup tested there. That figure belongs to that benchmark, paper set, rubric, tools, and study date; it is not a universal ranking of research agents. Its main lesson is methodological: break a large research objective into checkable subtasks and make the grading criteria explicit.
BrowseComp: difficult to find, easier to verify
BrowseComp focuses on obscure questions for which an answer can be checked reliably. Its authors describe this as an “asymmetry of verification”: “Questions that are hard to solve but easy to verify … are good for benchmarks, as they are both challenging and also reliable to grade.” This design is valuable when building your own tests, but leakage can inflate apparent ability, so protect task examples and answers.
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GAIA: read scores with verification and cost context
The HAL GAIA leaderboard marks results reproduced by the HAL team as verified, shows accuracy confidence intervals when multiple runs are available, and reports total API cost. Those fields answer questions a bare percentage cannot: Was the result independently reproduced? How variable was it? What did it cost?
MLR-Bench: fabricated experiments are a measurable failure mode
The MLR-Bench preprint covers 201 machine-learning research tasks and uses rubric-guided LLM review. Its authors report that, in the cases they tested, coding agents frequently produced fabricated or invalid experimental results. Treat that as a finding from that benchmark study, not as a rate that generalizes to every agent. In your own evaluation, require executable artifacts, logs, and independent reruns before accepting reported numbers.
How to interpret a benchmark score without overclaiming
A score supports a narrow statement: performance on a specified task distribution, with a specified model, scaffold, environment, tools, grader, and run procedure. It does not by itself support “the agent can do research” or “the agent is reliable in production.” Before comparing two numbers, verify that they share:
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- the same benchmark and split, version, and task count;
- the same environment, permissions, time limits, and tool access;
- the same model version and agent scaffold;
- the same grader and aggregation rule;
- the same repeat-run policy and uncertainty reporting; and
- comparable cost accounting.
Vendor-published results and product descriptions should be attributed to their publisher or benchmark team. Independent reproduction is stronger when the protocol, data, traces, and evaluators are available. A benchmark can be internally rigorous while still failing to predict performance on your organization’s sources, languages, security constraints, or workload.
A compact scorecard template
| Field | What to record |
|---|---|
| Claim | The narrowly defined capability and intended user decision |
| Tasks | Sampling frame, held-out set, benchmark version/date, and task count |
| Environment | Initial state, network/data access, tools, permissions, and time limit |
| Agent | Model/version, prompts, scaffold, sampling settings, and retry policy |
| Grader | Deterministic checks or rubric, judge validation, and disagreement handling |
| Results | Per-task outcomes, aggregate score, confidence interval, calibration, and robustness |
| Operations | Run count, latency, token/API cost, failures, and recovery behavior |
| Evidence | Traces, sources, files, logs, rerun instructions, and independently reproduced cases |
| Limitations | Known leakage risks, untested task families, grader weaknesses, and generalization limits |
Common evaluation mistakes and fixes
Counting completion messages
Failure: accepting “research complete” as proof. Fix: require an answer key, source checks, artifact inspection, or final-state predicate that the agent cannot edit.
Using one easy demonstration
Failure: selecting a task after seeing the agent solve it. Fix: freeze a representative, held-out set and disclose the selection rule.
Letting the agent grade itself
Failure: using the same model to produce and approve the result without validation. Fix: use deterministic checks or independently trained human/LLM graders, then audit judge agreement.
Reporting only an average
Failure: hiding that a few tasks account for most successes or that repeated runs disagree. Fix: publish per-task outcomes, confidence intervals, run count, and failure categories.
Ignoring cost and recovery
Failure: treating an expensive, retry-heavy success as equivalent to a fast first-pass success. Fix: record total API cost, elapsed time, retries, tool failures, and whether a human had to intervene.
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Confusing source quantity with source quality
Failure: rewarding many links even when they are duplicated, secondary, or irrelevant. Fix: score authority, direct support, recency where relevant, and coverage of required claims.
Accepting plausible experimental numbers
Failure: trusting tables or charts without executable provenance. Fix: require code, inputs, environment details, logs, and an independent rerun; mark unverifiable results as failures or clearly labeled claims.
Evaluation software and workflow choices
Evaluation platforms can organize datasets, graders, traces, and comparisons, but a platform does not make a weak protocol valid. LangSmith documentation describes offline evaluations on curated datasets, reference outputs, version comparisons, and heuristic checks of expected tool calls. Braintrust documentation describes defining agent tasks and test cases and running evaluation experiments. These pages establish vendor-described workflow capabilities, not independent evidence that one platform produces superior scores. Whichever system you use, retain raw artifacts and document the grader logic so the result remains inspectable outside the dashboard.
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Set the decision rule before looking at results. For a high-stakes deployment, require a minimum score on every critical task family, a maximum rate for severe failures such as fabricated citations, acceptable confidence calibration, and a cost or latency ceiling. If the agent misses the threshold, diagnose the failure category—retrieval, reasoning, tool use, execution, citation, or grading—then change one part of the system and rerun the same held-out protocol. Do not quietly replace failed tasks or broaden the claim after seeing the score.
The most trustworthy conclusion is therefore conditional: this agent achieved this result on these tasks, in this environment, with this grader, across this many runs, at this cost, with these observed failure modes. That level of specificity is what turns an agent’s assertion into evidence.
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