Claude Code Agent Teams

Claude Code's new "Agent Teams" feature enables the coordination of multiple Claude Code instances, essentially allowing AI agents to work collaboratively on development tasks. A designated lead agent orchestrates the team by assigning tasks and synthesizing results, while individual teammates operate independently within their own context windows and communicate directly with each other. This approach offers a more sophisticated parallelization strategy than simple subagents, particularly for complex and collaborative coding efforts.

• Purpose & Use Cases: Agent Teams are best suited for tasks benefiting from parallel exploration, such as comprehensive research, detailed code reviews (e.g., security, performance, test coverage), developing new modules, debugging with competing hypotheses, and coordinating changes across different layers (frontend, backend, tests).
• Mechanism & Control: The user prompts the lead agent to create a team, defining roles and tasks. Teammates then work on a shared, dynamic task list, capable of both self-coordination and explicit assignment from the lead. Users can interact directly with individual teammates, specify models for each, require plan approval before implementation, and enable a "delegate mode" for the lead to focus purely on orchestration.
• Communication & Context: Each teammate maintains its own independent context window and can communicate directly with others via messages or broadcasts. The lead's conversation history does not transfer to teammates upon creation.
• Display Modes: Teams can operate in "in-process" mode within a single terminal or "split panes" mode (requiring tmux or iTerm2) for a visual overview of each agent's activity.
• Resource Implications: Agent Teams are noted to be significantly more token-intensive than single sessions, as each teammate consumes tokens independently.
• Best Practices & Limitations: Recommendations include providing ample context to teammates, appropriately sizing tasks, monitoring progress, and avoiding file conflicts. The feature is experimental, with limitations such as no session resumption for in-process teammates, potential task status lags, one team per session, and no nested teams.

In essence, Agent Teams represent an advanced step towards autonomous, collaborative AI in software development, offering a powerful tool for complex projects despite their current experimental nature and increased resource consumption.