What is Multi-Agent System?

A multi-agent system (MAS) is an AI architecture in which multiple specialized AI agents collaborate, communicate, and coordinate to solve complex tasks that would be difficult or impossible for a single agent to handle alone. Each agent in the system is assigned a specific role or capability — such as planning, coding, testing, reviewing, or communicating — and the agents work together through structured interactions to produce results that exceed what any individual agent could achieve.

Multi-agent systems draw inspiration from human organizational structures. Just as a software development team has specialized roles (architect, developer, QA engineer, DevOps engineer), a multi-agent system assigns complementary roles to different AI agents. Communication between agents typically follows defined protocols: agents may share a common workspace, pass messages through an orchestrator, or follow a predefined workflow graph. Popular frameworks for building multi-agent systems include CrewAI (role-based agent teams), LangGraph (graph-based agent orchestration), AutoGen (conversational agent teams), and custom architectures built on LLM APIs.

In practice, multi-agent systems are used for increasingly sophisticated applications. In software development, a planning agent decomposes requirements, a coding agent implements features, a review agent checks code quality, and a testing agent verifies functionality. In research, a question-formulation agent designs queries, a search agent gathers information, an analysis agent synthesizes findings, and a writing agent produces the final report. ShipSquad's AI squad model exemplifies this approach, pairing a human expert with 8 specialized AI agents covering decomposition, architecture, frontend, backend, QA, DevOps, code review, and client communication.

Key design considerations for multi-agent systems include defining clear agent roles and boundaries, designing effective inter-agent communication protocols, managing shared state and context, handling conflicts between agents, implementing error recovery when one agent fails, and determining the right balance between agent autonomy and centralized orchestration. The emerging Agent-to-Agent (A2A) protocol and similar standards aim to enable interoperability between agents built with different frameworks.