Multi-Agent System (MAS) Architectures and Collaboration Methods: 4 Types and Practical Use Cases

Multi-Agent Systems (MAS) are structures where specialized AI agents collaborate to overcome the limitations of a single LLM. MAS accounts for 66.4% of the Agentic AI market, with a core approach where a centralized coordinator divides tasks and assigns them to specialized agents. Inter-agent communication uses the A2A protocol, and external tool integration uses the Model Context Protocol (MCP).

Architecture Types and Characteristics

System structures are categorized into four types based on their purpose.

• Centralized: A single orchestrator controls knowledge and distributes tasks. Security management is easy, but a failure in the orchestrator can paralyze the entire system.
• Decentralized: Agents act autonomously on equal footing. Fault tolerance is high, but communication complexity increases.
• Hierarchical: Features a layered command structure with high-level strategy formulation and low-level execution. Audit trails are clear, but latency may occur.
• Sequential: Agents pass results sequentially to the next step. Debugging is easy, but parallel processing is impossible, potentially slowing down speed.

Key Benefits and Challenges

MAS provides Specialization, Parallelism, Fault isolation, and Scalability. In particular, it increases processing speed by performing independent tasks simultaneously and prevents total system shutdown when a specific agent fails. On the other hand, challenges to be resolved include difficulties in context transfer in environments with hundreds of agents, compounding errors from LLMs, accumulating API call costs, and opacity in debugging.

Practical Use Cases

• Woven by Toyota: Applied MAS to automate MISRA standard compliance for autonomous driving software. Reviewer, Coder, and Evaluator agents collaborated to correct 81.5% of compliance errors.
• PGA TOUR: Constructed a pipeline with data, editor, writer, and publishing agents to produce over 1,200 pieces of content per week, reducing costs by 95%.
• CUADRA: Improved customer response satisfaction from a human baseline of 57% to 95.5%.
• Logistics Solution Provider: In warehouse operations, an orchestrator delegates tasks to inventory risk, task priority, and stockout prediction agents to provide recommendations based on WMS data.