How to Manage AGENTS.md Across Repositories

At first, teams add AGENTS.md to one or two key repositories. As AI coding agents spread, AGENTS.md files are copied into more projects, sometimes with quick edits for local needs. After a few months, nobody can say with confidence which version is "correct" or how many variants exist.

This pattern creates hidden risk. Small wording changes or missing sections can shift how agents behave, but those shifts are rarely tracked centrally. When leaders want to roll out a new standard, they first have to rediscover where AGENTS.md lives.

• Grepping across repositories for AGENTS.md and editing files in place.
• Maintaining a shared template in a wiki and asking teams to update their copies.
• Sending announcements with example snippets but no enforcement mechanism.

These approaches can work for a handful of repositories, but they do not scale. Each update depends on individual maintainers remembering to apply changes, and there is no simple way to see which repos are now out of date.

Automation starts with reliable inventory. A scanner or CI job lists every AGENTS.md across your GitHub organizations and surfaces the contents in a central place. From there, teams can compare each file to an org-level template and decide what needs to change.

The next step is to use scripted or tool-assisted pull requests to apply updates. Instead of pushing commits directly, automation opens PRs that show exactly how AGENTS.md will change so maintainers can review and approve them.

Governance adds clear ownership and decision-making on top of automation. Platform or AI enablement teams define the standard, security reviews weigh in on sensitive sections, and repositories receive rollout PRs with a defined acceptance window.

Exceptions are documented: if a repo must diverge from the template, that divergence is tracked rather than silently edited into AGENTS.md. This makes it easier to answer questions about where and why behavior differs.