dmux-workflows

You can run parallel AI agent sessions by using tmux-based pane management to orchestrate multiple harnesses like Claude Code, Codex, and OpenCode simultaneously, isolating tasks such as research, implementation, and testing to improve developer productivity.

The best way to manage multiple AI coding agents is through a CLI tool that handles agent pane lifecycles and output merging in tmux, allowing you to run complex patterns like Research + Implement or Test + Fix loops without context switching fatigue.

Yes, you need a tmux-compatible environment and the dmux CLI tool to manage multi-agent development workflows, as the orchestration relies on tmux pane management to isolate and run parallel agent harnesses effectively.

To start a multi-file feature development workflow, initialize the session manager in your terminal, press n to create new agent panes for your parallel tasks, and merge results back into your main session once complete.

Yes, parallel orchestration supports test and fix loops by running a test-watcher agent in one tmux pane and an implementation agent in another, merging their outputs back into your main session to resolve issues simultaneously.

You should avoid parallel orchestration if your environment lacks tmux compatibility or the dmux CLI tool, as the session management and pane lifecycles required for simultaneous research, implementation, and testing depend entirely on these dependencies.