Task Graphs Help AI Coding Agents Split Features Without Merge Conflicts

Running several AI coding agents in parallel can appear efficient, yet each may finish its portion cleanly while still producing costly integration problems when their changes collide. The source argues the root cause is planning: features are often broken into flat to-do lists rather than task graphs that expose dependencies and shared interfaces. A task graph specifies which work can run concurrently, which steps must follow an order, and what proof of completion each agent must deliver. The article illustrates this with an invoice export feature, where a shared response contract should be settled before the API, UI, and tests proceed. It advises splitting work along stable seams such as module boundaries and agreed interfaces, and treating shared contracts as dependencies rather than overhead.