parallel

To orchestrate parallel subagents, you parse a plan, infer missing task dependencies, and launch subagents in sequential waves based on the dependency graph to ensure correct execution order while logging run progress.

Managing multi-agent execution workflows involves inferring dependencies directly from the plan content when they are not explicitly modeled, reducing manual setup before launching subagents in parallel waves.

Dependency inference for parallel subagents works by analyzing plan content to automatically determine task relationships, grouping independent tasks into waves so they can execute concurrently while respecting sequential constraints.

Yes, you can automate task coordination by parsing user requests, launching subagents in parallel where possible, and aggregating results with run logs to provide complete auditability and traceability for the workflow.

Parallel subagent orchestration supports logging run progress by collecting and aggregating execution logs throughout the wave-based task execution, ensuring complete traceability and auditability of the multi-agent workflow.

You need wave-based orchestration when a plan contains tasks that can run concurrently, allowing grouped subagents to execute in parallel waves to optimize throughput while maintaining required dependency order.