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You can run parallel AI agents in isolated git worktrees by using a coordinator workflow to spawn agents, evaluate their outputs, and merge the winner. This requires a git repository with worktree support.

Multi-agent parallel execution is a technique where multiple AI agents compete on a single task within isolated worktrees. You need it for code optimization, creative variation, or research exploration when parallel experimentation accelerates discovery.

You evaluate and merge the best agent output by using a central coordinator that applies metric or LLM-based evaluation to rank the parallel results. After identifying the winner, the coordinator executes a final merge into the repository.

Yes, you need a git repository with worktree support to run parallel agents. The worktrees provide the isolated environments required for each agent to experiment independently before the coordinator evaluates and merges the winning result.

The best way to automate research exploration with multiple AI agents is to use a coordinator workflow that spawns agents in parallel isolated worktrees, ranks their outputs through metric or LLM-based evaluation, and merges the top result.

Yes, you can use LLM-based evaluation to rank parallel agent outputs. The central coordinator supports both metric and LLM-based evaluation methods to identify the best result before executing the final merge.