Multi-Agent Systems

Multi-agent systems (MAS) are AI architectures where multiple specialized AI agents work together to accomplish complex objectives. Each agent has specific capabilities, and they collaborate through communication, task delegation, and coordination — similar to how a team of human specialists works together.

Types of multi-agent architectures: hierarchical (a manager agent delegates to specialist agents), peer-to-peer (agents communicate directly as equals), pipeline (each agent handles one step in a sequential process), and swarm (many agents work in parallel with emergent coordination).

Key benefits of multi-agent systems: specialization (each agent excels at specific tasks), scalability (add more agents for more capability), reliability (if one agent fails, others can compensate), and complexity handling (break impossible tasks into manageable subtasks).

A practical example: a customer service multi-agent system might include a triage agent (classifies the inquiry), a technical support agent (handles technical issues), a billing agent (handles payment questions), a sales agent (handles upgrade requests), and a supervisor agent (monitors quality and handles escalations).

Multi-agent systems introduce challenges: coordination overhead (agents need to communicate efficiently), context sharing (passing relevant information between agents), conflict resolution (when agents disagree), and debugging complexity (understanding what went wrong across multiple agents).

Frameworks like CrewAI, AutoGen, and Chipp's dispatch system enable building multi-agent workflows where specialized agents collaborate on complex tasks.