How to evaluate coding agents on your codebase

Evaluating coding agents should focus on verifiable changes rather than completed sessions, persuasive explanations, or large diffs, using real backlog tasks that reflect a team’s languages, dependencies, review standards, and potential edge cases. Teams should define independent acceptance checks before runs, provide reproducible environments with consistent commits, dependencies, permissions, and settings, and avoid leaking solutions or giving candidates unequal assistance. Evaluations should distinguish agent-facing development tests from reviewer-controlled validation, assess scope and security implications alongside functional correctness, and document setup conditions that may affect outcomes. Each run should record configurations, changed files, checks, interventions, elapsed and human time, failures, retries, and abandoned work so reported costs and reliability are not distorted by selective successes. Benchmark findings such as Factory’s Legacy-Bench and review studies can inform methodology, but their rankings and results should not be generalized to other repositories, models, or conditions. Pilot results should guide limited adoption in task categories supported by evidence, while ongoing rollout metrics can supplement—but not replace—independent correctness review and repeatable evaluations after meaningful product, model, or workflow changes.

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