AGENTS.md vs. skills: How to steer a coding agent

AGENTS.md and modular skills serve different roles in guiding coding agents: the former provides concise, always-on repository context such as build commands and coding conventions, while the latter delivers larger, task-specific procedures only when needed. The central argument is that choosing between formats matters less than verifying whether instructions actually change agent behavior, since compliance can decline as context grows, wording and placement affect outcomes, and results vary across models and projects. The recommended approach is a reproducible evaluation loop using fixed task fixtures, observable behavioral assertions, automated runners, and pass/fail checkers, with repeated runs to account for variability. Teams can then test configuration changes and model upgrades in CI, gate regressions in instruction adherence, and independently validate all agent-generated code through standard build, lint, and test pipelines. The article concludes that measurable feedback loops, potentially supported by agent-accessible CI results and pre-commit checks, turn agent steering from an assumption into a data-driven engineering practice.

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