workflow-orchestration

Multi-agent workflow coordination uses a defined state machine to manage phase transitions across DISCUSS, PLAN, and EXECUTE stages. It enforces protocol-driven behavior for agent spawning, result collection, and handoffs to ensure reliable executions.

A workflow state machine for AI orchestration manages phase entry and exit checkpoints across DISCUSS, PLAN, EXECUTE, VERIFY, and COMPLETE states. It enforces safe transitions and predictable outcomes by coordinating multiple agents and handling blockers.

You start multi-agent phase coordination by invoking the orchestration skill and initiating the INIT phase with your initial data and goals. You then monitor transitions through DISCUSS, PLAN, and EXECUTE while the system tracks progress.

Multi-agent workflow orchestration supports blocker management and recovery by generating documented artifacts like FLOW.md and ITEM-XXX.md. These files support auditability and allow recovery when agents hit blockers during phase transitions.

Yes, you can use protocol-driven agent handoffs for complex AI workflows through Phase Handoff, Task Handoff, and Coordination Plans. These protocols minimize context loss and rework during multi-agent coordination and phase transitions.

State machine workflow orchestration for AI agents requires strict adherence to phase entry and exit checkpoints, which may introduce overhead for simple tasks. It is designed for complex multi-agent projects needing cross-phase coordination rather than lightweight executions.