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An AI agent’s average success rate is not enough to tell you what it can do or whether it will do it again. A useful evaluation report pairs task-level and category results with an explicitly weighted overall score, repeated-run consistency, uncertainty, and a clear account of the benchmark and its success criteria. It should distinguish “succeeds at least once” from “succeeds reliably on every attempt”: those measure different things.
Why an average success rate can mislead
An aggregate combines outcomes across tasks, but it can hide two important differences: an agent may perform well on some kinds of work and poorly on others, and its result on the same task may change between runs. A single percentage cannot show either pattern. Anthropic notes that task-specific success rates can differ and that a task passed on one evaluation run may fail on another (Anthropic’s guide to evaluating AI agents).
That matters when deciding what the result means. A system that succeeds on one attempt may be useful when a user can retry or select the best result. A system expected to complete a workflow consistently needs evidence about repeatability, not merely evidence that success is possible.
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What an agent evaluation report should include
Task outcomes and success criteria
Define what counts as success before running the evaluation. Report how many tasks met that threshold and the denominator, such as 18 of 25 tasks, rather than presenting a percentage alone. If tasks allow partial credit, show the average rubric score separately: pass rate answers how often the threshold was met, while rubric reward can preserve information about results that fall short.
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Breakdowns by meaningful task type
Show results for meaningful categories, workflows, or response formats alongside the overall figure. OpenAI’s LifeSciBench announcement says model performance varies substantially by task type, workflow, and response format; its findings concern that benchmark and should not be generalized to all agents (OpenAI’s LifeSciBench announcement). Include each category’s sample size, and avoid confident comparisons between categories with very few tasks.
Repeated-run consistency
When repeatability matters, say how many independent attempts were made and report how often tasks or task groups succeeded across those attempts. A lone successful run does not establish that the agent will reproduce the result. Select a measure that matches the use case rather than treating every success metric as interchangeable.
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Uncertainty and sample size
Include an uncertainty interval or other estimate when the evaluation design supports one, and name the method, sample size, and limitations. The ChatGPT Agent system card describes 95% confidence intervals for pass@1 using bootstrap resampling. It also cautions that this method can understate uncertainty on very small datasets: resampling captures sampling variation, but not all variation between problems (OpenAI’s ChatGPT Agent system card, expert deep dives). An interval should not be presented as covering uncertainty it was not designed to measure.
An overall score with its weighting
An overall result can make a report easier to scan, but state how it was calculated. For example, say whether tasks were weighted equally, categories were weighted equally, or another rule was used. Different weighting choices can produce different aggregates when categories contain unequal numbers of tasks. There is no universal weighting rule established by the cited guidance; justify the choice in relation to the evaluation’s intended use.
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Scope, configuration, and version
Identify the benchmark and task set, environment, agent configuration, grader or rubric, and evaluation date or version. A benchmark result is evidence about performance under those conditions, not a blanket forecast of production results. HAL Reliability cautions that a single-benchmark score can mislead, while Zapier’s AutomationBench describes using a public task set alongside a separate held-out private set; agreement between the sets is directional, not guaranteed (HAL Reliability’s key findings; Zapier’s AutomationBench repository).
Pass@k is not the same as reliability
Pass@k asks whether at least one of k attempts succeeds. It is useful when one good result among several attempts is valuable—for example, if a workflow can generate alternatives and a person can choose one. Passk asks whether all k attempts succeed, emphasizing consistent success. Anthropic distinguishes these measures in its agent-evaluation guidance (Anthropic’s guide to evaluating AI agents).
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Neither metric is universally best. Choose based on what the user or downstream system needs: at least one usable result, or success across repeated attempts. Report the number of attempts and the evaluation setup so readers can interpret the metric. For illustration, Anthropic calculates that a 75% per-trial success rate across three trials yields about a 42% probability that all three succeed. This is an illustrative calculation, not a measured industry rate.
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- Use the same task set and conditions. Keep the benchmark, environment, agent configuration, grader, and evaluation period consistent for each system.
- Show task and category outcomes. Include denominators and partial-credit results where applicable, not only an aggregate.
- Measure repeatability when it matters. Report the number of runs and a metric aligned with the use case, such as pass@k or passk.
- Explain uncertainty. Name the interval or method, sample size, and what sources of variation it does and does not capture.
- Disclose aggregation and scope. State the weighting rule and describe the benchmark’s boundaries; do not imply that benchmark performance guarantees production performance.
What the reported numbers do—and do not—show
Benchmark-specific figures can illustrate why context matters. In its 2026 LifeSciBench announcement, OpenAI reported an overall exact pass rate of 25.7% for GPT-5.5 and 36.1% for GPT-Rosalind. Those numbers describe results on LifeSciBench, not a general ranking of agent capability across unrelated tasks.
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A strong report therefore lets readers see what was tested, where the agent succeeded, how results varied across attempts, and how the summary number was formed. The average can remain—but it should be one part of the evidence, not the whole report.
Quick Recap
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