gsd-execute-phase

Multi-agent workflow execution is orchestrated by automatically spawning subagents to handle specific plan components, managing their lifecycles, and collecting structured results within a unified execution context. This ensures modular, scalable execution of coordinated planning, implementation, and verification steps.

Wave-based parallelization is a scheduling mechanism that groups project tasks into logical waves to optimize execution speed and resource management. It automatically dispatches agent types to execute independent tasks concurrently, ensuring dependencies are met before proceeding to the next wave.

Automated parallel execution of tasks like code refactoring and documentation updates is achieved by grouping them into waves. The workflow automatically dispatches subagents to handle each independent task concurrently, collecting all structured results efficiently within a unified execution context.

Yes, you can manage subagent lifecycles and parallel task execution without external dependencies. The workflow handles agent-type dispatching, wave-based scheduling, and structured result collection entirely within a unified execution context, requiring no additional setup modules.

The best way to coordinate independent coding tasks across multiple AI agents is using wave-based scheduling. This approach automatically groups tasks into logical sequences, spawns dedicated subagents for each component, and manages the entire lifecycle to ensure scalable, modular execution.

Use wave-based task parallelization when a complex project phase requires multiple independent tasks, such as code refactoring, documentation updates, and test generation. It is ideal for large-scale workflows needing coordinated execution of planning, implementation, and verification steps.