A simple evaluation loop for AI coding agents
A builder-focused tutorial for testing coding agents with task sets, repository constraints, review gates, and regression checks.
Coding agents are easier to trust when their performance is measured against real repository tasks. The goal is not a perfect benchmark. The goal is a repeatable loop that catches regressions before a tool becomes part of daily work.
Build a task set
Pick ten tasks from your own repository: one small bug, one refactor, one test addition, one docs update, one dependency change, and a few ambiguous issues. Keep the expected outcome short and concrete.
Make repository rules explicit
Write down what the agent may change, what it must not touch, how it should run tests, and which commands are destructive. Better agents respect constraints, but they still need clear boundaries.
Review the diff, not the story
Judge the final patch and command evidence. A confident explanation can hide a weak change. Keep the review focused on behavior, tests, security, maintainability, and whether unrelated files were changed.
Track tool fit over time
Some agents are strongest at issue triage. Others are better at localized edits, test generation, or multi-file refactors. Record the pattern so you choose the right agent for the job instead of asking one tool to do everything.