Orchestrating Parallel AI Coding Agents Without Collisions
The author faced issues when trying to split a feature across multiple AI coding agents working in parallel on the same repository. To address this, they built Ruah Orch, an open-source orchestration engine that coordinates AI agents and prevents conflicts.
Why it matters
Ruah Orch addresses a critical problem in the AI coding tools landscape, enabling parallel execution of AI agents without collisions and conflicts.
Key Points
- 1Ruah Orch provides isolated workspaces for each AI agent to prevent file conflicts
- 2It checks for file locks before agents start to avoid discovering conflicts later
- 3It supports smart parallel execution with dependency ordering to ensure tasks run in the right order
- 4It enforces modification contracts before merging to catch violations early
Details
The author describes their experience of trying to split a feature across five AI coding agents, which resulted in unresolvable merge conflicts due to the lack of workspace isolation, file ownership, merge strategy, and dependency ordering in existing AI coding tools. To address this, they built Ruah Orch, an open-source orchestration engine that coordinates multiple AI agents working on the same repository. Ruah Orch provides each task with an isolated workspace (Git worktree), checks for file locks before agents start, and supports smart parallel execution with dependency ordering. It also enforces modification contracts (owned, shared-append, read-only) to catch violations before the merge. Ruah Orch is not tied to a specific AI tool and supports executors for Claude Code, Aider, OpenAI Codex, and any shell command.
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