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Parallel task execution is achieved by decomposing complex problems into sub-tasks and assigning them to specialized sub-agents that work concurrently. This coordinated multi-agent teamwork optimizes workflows for both cost and performance.

Task decomposition is the process of breaking down large, complex problems into smaller, manageable sub-tasks. This allows a multi-agent team to assign specific roles like backend or frontend development to optimize overall performance.

Yes, you can assign specialized roles to different AI agents. The system supports built-in and custom project agents, allowing you to leverage specific agent types like backend-dev, frontend-dev, and tester for optimal task performance.

You orchestrate an AI agent team by creating and assigning sub-tasks for coordinated workflows. For feature development, a backend-dev agent can implement APIs while a frontend-dev agent builds the UI simultaneously, with a tester ensuring quality.

The best way to manage parallel AI agents is through coordinated workflows that facilitate task creation, assignment, and inter-agent communication. This multi-agent orchestration ensures complex work is executed efficiently in parallel.

Yes, multi-agent task decomposition works for both built-in and custom project agents. You can define specialized roles within your team to handle specific sub-tasks, ensuring optimal task performance and coordinated execution.