parallel-agents

To run multiple AI agents in parallel, you can start a parallel session with multiple models to execute the same task concurrently. This provides isolated workspaces and side-by-side comparison to streamline complex tasks.

To prevent conflicts when running concurrent AI agents, the system orchestrates isolated git worktrees for each agent. This prevents cross-talk and conflicts by giving every agent a separate working directory.

Yes, you can compare outputs from different AI models before merging. The system provides side-by-side comparison of the parallel results and uses a winner-based merge flow to consolidate the best outcome.

You can delegate background tasks to cloud AI agents for asynchronous task handling. This background delegation allows you to offload work and speed up decisions without waiting for sequential completion.

You need git worktrees to manage parallel feature work because they provide isolated environments for each agent. This isolation is necessary to prevent cross-talk and ensure clean result comparison before merging.

The best way to merge results from a multi-model experiment is using a winner-based merge flow. After running the models concurrently in isolated workspaces, you compare the results side-by-side and consolidate the winning output.