For complex, parallel tasks that might conflict, use `git worktrees`. This creates separate, tracked copies of the codebase, allowing multiple AI agents to work on different features simultaneously without creating merge conflicts in the main branch.

The developer's role is evolving from a linear workflow (code, submit PR, get review) to a parallel one. At Block, developers now manage multiple AI agents building numerous pull requests simultaneously, acting as an editor and context-switcher rather than the sole creator.

Tools like Git were designed for human-paced development. AI agents, which can make thousands of changes in parallel, require a new infrastructure layer—real-time repositories, coordination mechanisms, and shared memory—that traditional systems cannot support.

Each feature is built in distinct, user-testable phases, and each phase uses a new, isolated work tree. This serves as a "save point," preventing context from one phase from corrupting the next, reducing hallucinations, and allowing for easy rollbacks if something goes wrong.

To run autonomous agents effectively without them interfering with each other, foundational infrastructure is essential. This includes isolated work trees (sandboxes), reusable skills (functions), and clear state tracking to ensure consistency and avoid conflicts.

Structure your development workflow to leverage the AI agent as a parallel processor. While you focus on a hands-on coding task in the main editor window, delegate a separate, non-blocking task (like scaffolding a new route) to the agent in a side panel, allowing it to "cook in the background."

Manually managing git worktrees for parallel agents creates high cognitive load (naming, dependency installs). Kun Chen built 'Treehouse', a simple tool that automates worktree creation from a managed pool, pre-installing dependencies and eliminating manual overhead.

Instead of using separate worktrees which isolate agents, Git Butler's "parallel branches" allow multiple agents to operate in a single working directory. This enables them to see each other's changes in real-time, avoid merge conflicts, and even stack their work on top of each other's.

The agent development process can be significantly sped up by running multiple tasks concurrently. While one agent is engineering a prompt, other processes can be simultaneously scraping websites for a RAG database and conducting deep research on separate platforms. This parallel workflow is key to building complex systems quickly.