Handling Git Merge Conflicts in Multi-Agent Worktrees

A developer outlines an orchestration architecture for running parallel AI coding agents in Git worktrees that avoids relying on language models to resolve merge conflicts. The approach uses pessimistic path reservation at task dispatch, a strictly single-lane serialized merge queue, and fail-closed rebase testing so that conflicting or regressing changes are rejected rather than guessed at. The stated goal is to preserve parallel throughput while keeping the canonical branch stable against textual conflicts and silent semantic drift.

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. 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. 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. 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 https://raylabs.app/articles/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" } 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. Integrating code from concurrent AI coding agents https://raylabs.app/articles/git-worktrees-parallel-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.