When you run multiple AI agents in parallel, they will try to edit the same files. Agent A adds a function to utils.ts. Agent B refactors the imports in utils.ts. The result? A merge conflict at best. Silent corruption at worst.
This is the fundamental problem of parallel AI execution. Every tool that claims "parallel agents" either ignores it (hoping for the best) or serializes writes (defeating the purpose).
We decided to solve it properly.
In orchex, every stream declares which files it owns (can modify) and which files it reads (can reference but not modify). When the orchestrator applies artifacts back to your codebase, it enforces these boundaries:
This means parallel agents literally cannot corrupt each other's work. The safety guarantee is structural, not behavioral.
When you define streams, ownership is explicit:
(See the orchex documentation for YAML stream definition examples.)
Stream auth-api can import from src/types/auth.ts (it's in reads), but if the LLM tries to modify that file, orchex rejects the artifact. Only auth-types can write to src/types/auth.ts.
Ownership enforcement is what makes parallel execution trustworthy. Without it, you're just running multiple agents and hoping they don't collide. With it, you have a deterministic guarantee: same input, same execution boundaries, every time.
This is the difference between "fast and broken" and "fast and correct."