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You orchestrate AI agent teams by initiating a structured pipeline that assigns distinct roles like Lead, Developer, Reviewer, and QA to manage the full development lifecycle from task execution to code commit.

Multi-agent code review and QA testing works by routing tasks through a structured pipeline where a Reviewer agent evaluates code quality and a QA agent runs tests before the final code commit is approved.

Yes, you need Git for version control and a task management system like 'bd' to successfully automate the end-to-end development lifecycle and ensure proper task execution and code commits.

The best way to manage AI teams for feature implementation is using an agent orchestration pipeline that decomposes tasks, assigns a Developer, and enforces code review and QA testing prior to committing code.

Yes, you can use this skill for task decomposition and role-based agent execution because it orchestrates distinct AI roles to guide a project from initial task breakdown through to final QA.

You should not use a multi-agent pipeline for code reviews if your project lacks integration with task management systems like 'bd' or version control like Git, as these dependencies are required for the structured workflow.