dmux-workflows

You can run parallel AI agents in tmux by using a pane manager to orchestrate multiple sessions concurrently. This reduces idle time by splitting research, implementation, and testing into coordinated tracks across different harnesses like Claude Code and Codex.

Yes, you can use Claude Code and Codex together in a multi-agent workflow. Cross-harness coordination lets you assign different tools to different panes, optimizing throughput by matching specific AI harnesses to specialized tasks like research or implementation.

The best way to parallelize a test and fix loop is to run tests in one tmux pane and implement fixes in another. This shortens feedback cycles by allowing concurrent execution, letting one agent validate while another immediately applies the required code corrections.

You coordinate multiple AI agents for code review by parallelizing different review perspectives across panes. Agents analyze security, performance, and tests simultaneously, merging their individual results into a single comprehensive report for faster pipeline execution.

Multi-agent orchestration requires a tmux environment to manage panes. An external helper handles git worktrees and tmux orchestration to plan and execute multi-pane sessions, ensuring parallel AI agents operate without overlapping workspace conflicts.

You should use parallel agent sessions instead of a single assistant when tasks can be split into independent tracks, such as multi-file features or research and implementation. Parallel workflows reduce idle time and accelerate delivery compared to sequential processing.