dmux-workflows

Parallel AI agent orchestration uses tmux pane management to run separate sessions across different development harnesses simultaneously. It provides workflow patterns and git worktree isolation to execute independent tasks like multi-file feature builds without context conflicts.

Coordinating parallel workflows between Claude Code and Codex is best handled through cross-harness task assignment. You can assign independent sub-tasks to separate dmux panes, allowing multiple agents to work simultaneously and merging completed results back into your main session.

To prevent context conflicts when running multiple AI agents, you should use git worktree isolation for each session. This separates the working directories of parallel agents, ensuring they do not overwrite each other's changes during multi-file feature development.

Yes, you can use tmux for automated test-fix iteration loops with AI agents. Pre-built workflow patterns are available to orchestrate these loops, allowing agents to iteratively run tests and apply fixes across isolated panes without manual intervention.

Git worktrees are highly recommended when splitting development tasks across multiple AI sessions to maintain isolation. They prevent file conflicts and poor resource allocation by giving each parallel agent a separate working directory before merging results.

Running multiple AI agents in parallel causes token overuse due to duplicated work and poor resource allocation across active sessions. You can manage this by applying resource management best practices and pre-built workflow patterns to optimize divide-and-conquer parallelism.