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Parallel task execution coordinates multiple independent tasks concurrently while enforcing dependency-aware planning. It builds a dependency graph to ensure prerequisites are completed before dependent work starts, maximizing throughput safely.

A dependency-aware task graph maps prerequisites before dependent work starts, ensuring safe concurrency. You need it when multiple independent tasks run in parallel to maximize throughput without violating execution order constraints.

Wave-based task graphs launch multiple tasks concurrently across agent tiers to reduce total turnaround. You execute them by firing parallel execution waves while the system enforces dependency constraints between each wave.

Parallel task execution applies to scenarios with multiple independent tasks that can run concurrently to maximize throughput. It coordinates these tasks using wave-based execution and dependency-aware planning to ensure correct routing.

The best way to track progress is through lightweight per-task reporting that provides concise status updates per wave. This includes reporting touched files and highlighting blockers to keep routing decisions clear and auditable.

You should not use parallel execution waves when tasks are strictly sequential or lack independent concurrency. Without multiple independent tasks and dependency-aware planning, wave-based orchestration provides no throughput benefits.