Executing Work in Parallel

Concurrent task execution groups independent work and launches them simultaneously using a single function_calls block with multiple Task invocations, each with its own agent and context, eliminating sequential delays and accelerating overall completion time.

Map task dependencies systematically: identify shared prerequisites that must complete first, then group remaining tasks with no interdependencies as parallel candidates. Phase-based orchestration handles shared dependencies in Phase 1, then executes independent work concurrently in Phase 2.

Yes, parallel execution works for layer-based workflows: establish shared dependencies like database schema first, then delegate backend API, frontend components, and other independent layers to specialist agents simultaneously, reducing overall feature delivery time.

Define explicit dependencies in your task analysis phase: identify which tasks require outputs from others, execute those prerequisite tasks first, then batch independent work into parallel blocks. Reassess after each phase to detect newly unblocked tasks.

Use agent delegation for 2+ independent tasks, research investigations, multi-file refactoring, and workflows spanning different technical domains where specialist agents reduce context saturation and improve execution efficiency compared to sequential single-agent processing.

After parallel execution completes, reassess outputs for integration points, dependencies on downstream work, and prepare batch repeats if additional parallel phases remain. This cycle continues until all work phases finish.