The next evolution beyond a single agent like Autoresearch is a platform for agent swarms to collaborate on a single codebase. AgentHub is conceptualized as a "GitHub for agents," designed for a sprawling, multi-directional development process.

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 creative process with AI involves exploring many options, most of which are imperfect. This makes the collaboration a version control problem. Users need tools to easily branch, suggest, review, and merge ideas, much like developers use Git, to manage the AI's prolific but often flawed output.

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.

The IDE Zed was built for synchronous, Figma-like human collaboration to overcome asynchronous Git workflows. This foundation of real-time, in-code presence serendipitously created the perfect environment for integrating AI agents, which function as just another collaborator in the same shared space.

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.

Instead of siloing agents, create a central memory file that all specialized agents can read from and write to. This ensures a coding agent is aware of marketing initiatives or a sales agent understands product updates, creating a cohesive, multi-agent system.

Agents in Buzz don't alter local files. They create separate Git work trees to build and test features in parallel, allowing for safe, simultaneous software development. Agents can even push to their own hosted repositories, creating a self-contained ecosystem.

Complex orchestration middleware isn't necessary for multi-agent workflows. A simple file system can act as a reliable handoff mechanism. One agent writes its output to a file, and the next agent reads it. This approach is simple, avoids API issues, and is highly robust.