dmux-workflows

You can run parallel AI agent sessions by using dmux to orchestrate multiple tmux panes, allowing you to coordinate tasks across different harnesses like Claude Code and Codex simultaneously for faster iteration.

Yes, cross-harness orchestration allows you to coordinate tasks across Claude Code, Codex, and OpenCode simultaneously, enabling faster iteration by distributing work across multiple AI systems in parallel panes.

Yes, you need a tmux-enabled terminal and the dmux installation to manage parallel AI coding workflows, as dmux relies on tmux-based pane management to ensure deterministic behavior across agent sessions.

The best way to structure multi-file feature development is by applying pattern-driven workflows like Research + Implement or Multi-File Feature, creating independent panes with clear task boundaries before merging results back into the main session.

Parallel agent panes handle code review and testing through pattern-driven workflows like Test + Fix and Code Review pipelines, managing multiple agent sessions in parallel with deterministic pane behavior to accelerate the review process.

You should avoid using parallel AI sessions when you lack clear task boundaries, as dmux requires independent panes and planned merges to function correctly, making it unsuitable for tightly coupled or interdependent coding tasks.