Preventing File Conflicts When Running Multiple AI Coding Agents
Preventing File Conflicts When Running Multiple AI Coding Agents
Running five AI coding agents on the same repository sounds productive until they all edit the same file at the same time. One agent adds a function, another reformats the imports, a third changes the same function signature. The result is merge conflicts, lost changes, and wasted tokens.
This is not a hypothetical problem. Anyone running parallel Claude Code sessions on a single codebase has hit it.
The Core Problem
AI agents do not coordinate with each other by default. Each one reads a file, plans changes, and writes the result. If another agent modifies the same file between the read and write, one agent's changes get silently overwritten. There is no lock, no merge, no warning.
Git Worktrees - The Best Current Solution
Git worktrees give each agent its own working directory with its own checkout of the repository. Agent A works in repo-worktree-a/, Agent B works in repo-worktree-b/. They share the same git history but their file systems are completely isolated.
Each agent commits to its own branch. Merging happens after the work is done, when you can review conflicts properly instead of having them silently resolved by whichever agent writes last.
Task Partitioning
The simplest conflict prevention is giving agents non-overlapping tasks. Agent A works on the backend API. Agent B works on the frontend components. Agent C writes tests. If their file sets do not overlap, conflicts do not happen.
This requires upfront planning but it dramatically reduces wasted work. A few minutes of task assignment saves hours of conflict resolution.
File-Level Coordination
When worktrees are not practical, you can add lightweight coordination. A shared lock file or a simple convention - "Agent 3 owns src/auth/" - reduces collisions. Some teams use a coordinator agent whose only job is assigning file ownership to worker agents.
The key insight is that multi-agent productivity is not about running more agents. It is about running agents that do not interfere with each other. Five agents with good isolation outperform ten agents stepping on each other's changes.
Fazm is an open source macOS AI agent. Open source on GitHub.