AIエージェントの指示ファイルは、なぜ端末ごとにズレていくのかThis article explains the structural reasons why AI agent instruction files…

Teams that adopt AI coding agents such as Claude Code, Cursor, and ChatGPT frequently hit the same wall once more than one person is involved: the instruction files meant to keep every agent behaving the same way slowly diverge from one machine to another. This is a structural outcome rather than a case of careless configuration, and the common remedies only partly solve it. For teams standardizing on agentic coding tools, the issue matters because inconsistent instructions quietly produce inconsistent output.

Instruction files are the persistent context an agent reads before acting. Claude Code looks for a CLAUDE.md file, Cursor uses a .cursorrules file or a .cursor/rules directory, and a growing number of tools recognize the AGENTS.md convention. These files typically encode coding standards, architectural boundaries, preferred libraries, naming rules, and review expectations. When they match, agents on different machines tend to produce comparable results. When they drift, the same prompt can yield materially different code depending on whose laptop ran it.

The first structural cause is scope. Most agents support layered configuration: project-level files that live inside the repository and personal or global files stored in a developer's home directory. Only the project-level layer is naturally shared through version control. Global settings, often where individuals tune verbosity, language preferences, or shortcuts, never travel with the codebase, so two developers can run the "same" project with quietly different effective instructions.

A second cause is format fragmentation. Because each agent reads its own file in its own location, a team using several tools must maintain parallel copies of what is conceptually one set of rules. Keeping a CLAUDE.md file, Cursor's rules, and an AGENTS.md file in agreement is a manual synchronization task, and manual synchronization tends to lag. One file gets updated during a refactor while the others do not, and the gap widens with every commit.

A third cause is local, uncommitted change. Developers frequently tweak instructions to fix an immediate annoyance, then forget to commit the edit or deliberately keep it personal. Tool versions add further variance: an updated agent may interpret the same file differently, or introduce new precedence rules that reorder how layered instructions combine. The result is that identical text on disk does not guarantee identical behavior.

It is also worth examining the workarounds teams reach for, and where each falls short. Committing instruction files to the repository addresses the project layer but does nothing for global and machine-local configuration. Symbolic links or shared dotfiles can propagate a single source, yet they depend on every developer setting up and maintaining the same linking scheme, which is itself a source of drift. Copy-and-paste duplication across formats is the most common approach and