df:execute-objective

Wave-based parallel execution coordinates subagents by discovering plans, analyzing dependencies, and grouping tasks into execution waves that run concurrently. This dependency-aware scheduling ensures multi-agent coordination while maintaining global progress and state management across the objective.

You orchestrate multiple jobs by running them in waves and collecting results in a unified workflow. This approach reduces manual coordination, drift, and context rot by applying dependency-aware scheduling and spawning subagents to execute tasks concurrently.

Yes, dependency-aware scheduling supports multi-agent coordination for large projects by analyzing task dependencies, grouping work into execution waves, and spawning subagents concurrently. It maintains global progress and state management while respecting checkpoints and routing requirements.

The best way to execute planned objectives with clear gating and verification is using an end-to-end workflow that implements plan discovery, dependency analysis, wave grouping, and subagent execution. This objective-driven automation ensures robust fault tolerance through context-aware orchestration.

Context-aware orchestration handles fault tolerance by loading full execute-job context per subagent and respecting checkpoints and routing requirements. This ensures robust execution even when issues arise, maintaining state updates and verification throughout the workflow.

Dependency analysis matters because it determines which tasks can run concurrently in the same wave and which must wait for dependencies to complete. This grouping enables parallel execution while preventing conflicts and ensuring proper task ordering across the objective workflow.