orchestrator

To coordinate multiple AI sub-agents for parallel task execution, you provide a complex objective to the orchestrator, which decomposes it into discrete work items, delegates them to specialized sub-agents, and executes independent items simultaneously before aggregating the results into a unified outcome.

A multi-phase workflow for AI task automation is a structured process that breaks down complex objectives into four stages: planning, delegation, parallel execution monitoring, and result aggregation across modular sub-agents to produce a cohesive final output.

You aggregate results from parallel AI agents by using an orchestration layer that collects, reconciles, and summarizes the outputs from all delegated sub-agents after their parallel execution completes, forming a cohesive final report.

Yes, you can use this orchestrator skill to run a data-processing pipeline. It supports scenarios where the orchestrator collects data from several sources, processes steps in parallel via sub-agents, and merges the results into a final report.

You should use a multi-agent orchestrator instead of a single AI assistant when your objective requires planning, delegation, and parallel execution across modular agents, especially for complex tasks that exceed the capacity of a single workflow step.

No, you do not need any external dependencies to run the multi-agent orchestrator workflow. The skill operates independently without requiring additional components or external libraries to execute its planning, delegation, and aggregation phases.