Evidence-first authority page
How to Evaluate AI Agents: A Framework Beyond Marketing Scores
A method for evaluating AI agents on your own task, rather than trusting vendor-reported benchmark scores or third-party star ratings.
Direct answer
Evaluate an AI agent by running it against a representative sample of your actual task with a defined pass/fail rubric, checking failure modes as closely as success cases, and treating vendor-reported benchmark numbers as a starting hypothesis rather than a substitute for your own test.
Last reviewed 2026-08-29
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Why vendor benchmarks don't transfer
A benchmark score is only informative for tasks that resemble the benchmark's own test set, run under the same conditions the vendor used. A coding agent's strong score on a public benchmark doesn't predict its performance on your specific codebase, your specific conventions, or your specific class of bugs — those are exactly the variables the benchmark didn't test.
Build a rubric before you start testing
- Define what 'success' means concretely for your task — not 'it worked' but a checkable condition (tests pass, the PR is mergeable without further edits, the ticket is correctly triaged).
- Sample real cases, including edge cases and cases you know are hard, not just easy representative examples.
- Score failures by category, not just pass/fail — a wrong-but-safe failure and a wrong-and-destructive failure carry very different risk even at the same raw failure rate.
Weight reliability over peak capability
An agent that succeeds 95% of the time with a clear, safe failure mode on the remaining 5% is often more useful in production than one that succeeds 98% of the time but fails unpredictably and silently. For any task run without full human review, the shape of the failure distribution matters as much as the average success rate.