Multi-Agent System
Definition
A multi-agent system (MAS) is an architecture where multiple specialized AI agents collaborate, communicate, and coordinate to solve complex problems that exceed the capabilities of a single agent.
Why It Matters
Some problems are too complex for a single AI agent. Multi-agent systems enable specialization (one agent for research, another for writing, another for review) and parallel execution (multiple agents working simultaneously). This mirrors how human teams operate, with different experts collaborating on different aspects of a project.
How It Works
In a multi-agent system, each agent has a defined role, set of tools, and objectives. A supervisor agent or orchestration layer coordinates communication between agents, routes tasks appropriately, and aggregates results. Agents can communicate through shared memory, message passing, or direct handoffs. Frameworks like CrewAI and AutoGen simplify building these systems.
When to Use It
Use multi-agent systems when: (1) the task requires diverse expertise that would be hard to encode in one prompt, (2) you need parallel processing for efficiency, (3) you want to implement checks and balances (e.g., one agent writes, another reviews), or (4) the workflow naturally decomposes into distinct roles. For simpler tasks, a single well-prompted agent is usually sufficient.
Source
Multi-agent collaboration can outperform single-agent approaches on complex tasks through specialization and parallel execution.
https://arxiv.org/abs/2402.01680