You can tell whether an AI system is reliable by testing it on representative tasks with clear success criteria, checking both its answers and real-world outcomes, and repeating the tests to see whether failures persist. Those tests are evaluations, or “evals.” Their results are evidence about the tasks tested—not proof that the system will work in every situation.
What an AI eval can—and cannot—tell you
An evaluation gives an AI system an input and applies grading logic to measure success. For an AI agent, the system being tested includes not only the model but also the software that directs its tools and actions. A useful eval may therefore need to examine the action trace and the resulting state, rather than just the final response. Anthropic’s guide to agent evaluations gives the example of an agent claiming it booked a flight: the meaningful check is whether a reservation actually exists.
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An eval supports a limited claim: how the system performed on the tested tasks, under the tested conditions, according to the chosen grader. It cannot establish a universal accuracy rate or guarantee dependable performance in every setting. Results depend on the questions selected, the grading method, implementation details, and whether the test resembles actual use.
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How to evaluate an AI system
- Define the intended use. Specify what the system should do, who will use it, and the conditions under which it must work. Include situations where it should refuse or ask for clarification.
- Turn expectations into observable criteria. Write tasks that resemble real requests, then decide what counts as success, partial success, or failure. Include edge cases and examples of both desired and undesired behavior.
- Choose a grader that fits the task. Use deterministic code checks for facts such as whether a required tool was called or a database changed as intended. For qualities that require judgment, use human review or a model-assisted grader, and check its judgments against examples reviewed by people. Anthropic describes these grader trade-offs: code can be quick and reproducible but brittle to valid variations, while open-ended judgments need careful calibration.
- Run repeated trials and retain evidence. If the system’s responses vary, repeat tasks under consistent conditions. Keep outputs and traces, and report the kinds and number of failures alongside any average score.
- Compare systems consistently. Use the same tasks, instructions, tool access, grader definitions, and run conditions. Track relevant measures such as task success, latency, token use, cost per task, and errors.
- Check performance beyond the test set. Use monitoring and user feedback in real use to find failures the static task set missed.
- Review the evaluation over time. Refresh tasks when they stop resembling users’ needs, become too easy, or may have been exposed during training.
Why a benchmark score can mislead
A benchmark’s average is based on a sample of questions; a different sample could produce a different result. In a 2024 article on evaluating model capabilities, Anthropic recommends reasoning about performance across the broader universe of relevant questions rather than treating one observed average as the underlying skill.
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Test design and presentation also matter. In its 2023 discussion of model evaluations, Anthropic describes MMLU as covering 57 tasks, from mathematics to history and law, and reports that small changes to answer formatting can shift accuracy by approximately 5%. Those figures describe the example in that article; they are not a general estimate of formatting sensitivity across benchmarks.
Other weaknesses include benchmark questions that appeared in training data, inconsistent implementations, and flawed, ambiguous, or unanswerable questions. Human review can better reflect conversational quality, but reviewers may disagree or lack relevant expertise. Model-generated questions can expand coverage, yet need human verification because they may contain inaccuracies or bias. A benchmark can also reward the wrong behavior: Anthropic describes an agent that exploited a policy loophole in a flight-booking task. The agent failed the written evaluation even though its solution was better for the user, showing why criteria must reflect the actual goal.
How to compare two AI systems fairly
Do not rely on one composite score if different failures have different consequences. Compare systems across the same representative tasks, then look at:
- Task success: Did the system achieve the user’s real goal, including the required outcome in the environment?
- Consistency: Does it succeed across repeated runs, or only on a lucky attempt?
- Failure severity: Are mistakes minor inconveniences or high-impact errors?
- Coverage: Do tasks reflect likely users, edge cases, and requests that call for refusal or clarification?
- Robustness: Do small changes in phrasing, formatting, or environment alter the result?
- Cost and speed: What latency and cost accompany successful completion?
- Evidence quality: Are checks objective where possible, human judgments calibrated, and conditions documented well enough to reproduce?
A higher average score may not make a system the better choice if it fails more often on a consequential subset. The right choice depends on which tasks matter and how costly their failures would be.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What makes an eval useful over time
A static test set is useful for checking whether a change causes regressions, but it cannot cover every future situation. Keep the task set tied to real user needs, document the instructions and grading rules, and revise it when the system or its environment changes. Anthropic’s evaluation discussion highlights contamination, inconsistent implementations, and flawed questions as reasons benchmark results need ongoing scrutiny.
There is no universal statistic for how often AI is wrong, and no single accuracy threshold that proves a system dependable. A score is meaningful only alongside the tasks, conditions, grading approach, and failure patterns that produced it.
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