distributed-task-orchestrator

Parallel execution of multi-step workflows is achieved by decomposing complex requests into independent atomic tasks and orchestrating sub-agents to run concurrently via the Claude CLI. The aggregated results are then persisted.

Task decomposition for batch processing breaks down complex user requests into independent atomic tasks with clear inputs and outputs. Multiple sub-agents are then assigned to execute these tasks concurrently.

Yes, you can use parallel sub-agents for code analysis and documentation processing. The orchestrator assigns decomposed tasks to sub-agents, running them concurrently to improve throughput where latency matters.

The best way to orchestrate parallel agents for batch data tasks is to decompose the pipeline into atomic tasks and execute them concurrently. This simulated or real Claude CLI execution minimizes latency and maximizes throughput.

No, manual assignment is not needed. The orchestration automatically enforces task decomposition, assigns the independent atomic tasks to sub-agents, simulates or executes Claude CLI commands, and aggregates the final results.

You should avoid parallel task orchestration for multi-step operations when tasks are strictly sequential or dependent on each other. The orchestrator is designed for independent tasks with clear inputs and outputs that benefit from concurrency.