What Does Multi-Agent Mean in AI?

Multi-Agent是這篇文章討論的核心
In artificial intelligence, an **agent** is an autonomous software entity that can **perceive its environment, make decisions, and take actions** to achieve a specific goal. In an enterprise context, AI agents often handle tasks such as fetching data, calling tools, booking meetings, or answering customer questions.

**“Multi-agent”** refers to a system where **multiple AI agents work together**, often with different specialties. Instead of one large model trying to do everything, a multi-agent system divides work among coordinated agents — for example, one agent for research, one for planning, and one for executing code — that communicate, delegate, and refine outputs.

### Key features of multi-agent systems
– **Specialization**: Each agent is responsible for a narrow task or domain.
– **Coordination**: Agents pass information, request help, and align on a shared workflow.
– **Autonomy**: Agents can act and react without every step being hard-coded.
– **Scalability**: Teams of agents can handle complex, end-to-end business processes.

### Why it matters now
Gartner has reported a sharp rise in enterprise interest, predicting that **40% of enterprise applications will embed task-specific AI agents by 2026**, up from less than 5% in 2025. This shift reflects a move from single-purpose AI assistants to coordinated “virtual workforces” of agents that collaborate on real business workflows.

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