multi-agent-orchestration

To build a multi-agent system, you define specialized roles, goals, and expertise for individual agents, then implement sequential, parallel, hierarchical, or consensus-based orchestration patterns to coordinate their task delegation and collaboration.

Multi-agent orchestration coordinates specialized AI agents to collaborate on complex problems. You need it when a single task requires diverse expertise, modular agent design, flexible workflows, and robust inter-agent communication protocols to aggregate results effectively.

Yes, this Skill provides framework integration templates for CrewAI, AutoGen, LangGraph, and Swarm. It manages direct, tool-mediated, and manager-based communication between agents to execute your defined workflow orchestration patterns.

The best way to structure a sequential workflow is by defining specific agent roles like a researcher to gather data and an analyst to interpret it. You then chain these agents so the output of one feeds directly into the next for result aggregation.

Yes, multi-agent orchestration supports parallel, hierarchical, and consensus-based workflows. This allows multiple specialized agents to process tasks simultaneously or within a manager-based structure to aggregate final results.

Consensus-based workflows allow multiple agents to collaboratively evaluate and synthesize diverse perspectives before producing a final result, whereas sequential patterns simply pass tasks linearly from one specialized agent to the next without collaborative feedback loops.