Handling Git Merge Conflicts in Multi-Agent Worktrees

Scaling multi-agent coding swarms using Git worktrees allows multiple autonomous agents to write code simultaneously without disk exhaustion. However, the throughput bottleneck rapidly shifts to the integration boundary. When dozens of agents modify repositories concurrently, development teams face textual merge conflicts, the hazard of automated models misinterpreting conflict markers, and silent semantic drift where code merges cleanly but breaks runtime contracts.

The Integration Wall in Multi-Agent Coding

Generating code in isolated worktrees is straightforward, but converging those changes back into a shared canonical branch creates friction. The primary challenges include textual merge conflicts when two agents touch the same configuration file or route index, and silent semantic drift where a merge succeeds textually while introducing runtime failures because Agent A updated a function signature while Agent B called the deprecated version.

The Myth of LLM Merge Conflict Resolution

Prompting an autonomous coding model to fix git conflicts is inherently dangerous. Models frequently hallucinate resolution logic, drop critical changes committed by peer agents, or accidentally preserve broken conflict markers in production builds. Instead of treating the language model as a conflict resolution tool, architectures must prevent conflicts before they reach the merge stage or enforce rigid programmatic validation.

Path Reservation Architecture

Instead of dealing with conflicts after the fact, the orchestrator implements pessimistic resource locks. When a task is dispatched to an agent, its target file paths and dependencies are claimed. If a subsequent task requires writing to overlapping files, the orchestrator enforces serialized dependency ordering rather than parallel execution. For a related implementation, see Agent Orchestrator Vs Model Router.

{
  "task_id": "task-8842",
  "assigned_agent": "agent-beta",
  "worktree_path": "/var/lib/worktrees/agent-beta",
  "reserved_paths": [
    "src/api/router.ts",
    "src/models/user.ts"
  ],
  "lock_status": "acquired"
}

The Serialized Merge Queue Blueprint

Coding runs in parallel across isolated worktrees, but merging into the canonical branch is strictly single-lane. When an agent finishes its work, the orchestrator rebases its branch onto the updated main branch, executes automated tests in the rebased context, and performs a fast-forward merge only if all checks pass. If an automated rebase encounters merge conflicts or test regressions, the system fails closed rather than permitting the agent to guess at conflict markers.

Conclusion

Integrating code from concurrent AI coding agents requires moving past the illusion that large language models make good merge conflict resolvers. By combining path reservation at the dispatch boundary with a serialized merge queue and fail-closed rebase testing, teams can maintain a stable canonical branch without sacrificing the throughput benefits of parallel worktree execution.