Multi-agent systems explained: How AI agents collaborate

Multi-agent systems coordinate multiple autonomous, LLM-powered AI agents to divide and complete complex workflows that would be difficult for a single agent, using communication protocols such as A2A for agent-to-agent exchange and MCP for access to external tools. Agents may be simple reflex, model-based, goal-based, utility-based, learning, hierarchical, or collaborative, and systems can use centralized, decentralized, hierarchical, or sequential architectures depending on needs for control, resilience, governance, scalability, or strict task dependencies. Their main advantages include specialized task handling, parallel processing, fault isolation, and easier scaling, but implementations can face coordination overhead, error propagation, high inference costs, and difficult debugging. Examples include Toyota subsidiary Woven’s code-compliance workflow, PGA TOUR’s content-production pipeline, CUADRA’s customer-support system, and Synergy Logistics’ warehouse-management tools. In voice AI, multi-agent designs can triage calls, retrieve information, and transfer customers between specialized agents while retaining context, and platforms such as ElevenAgents are presented as tools for creating and monitoring these voice and chat systems.

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