Multi-agent system
Also known as: multi-agent, multi-agent architecture, agent swarm, agent orchestration, agent network
Single agents work well for bounded tasks. But as tasks become more complex, longer-running, or require different types of expertise, splitting the work across multiple specialized agents often produces better results. One agent might act as an orchestrator that breaks down a goal and delegates subtasks. Specialist agents handle specific domains: research, coding, data analysis, quality checking. Each agent only needs to be good at its own job.
This architecture mirrors how teams of people work. The orchestrator is like a manager who assigns work and reviews it. The specialist agents are like individual contributors with deep expertise in their lane. The main challenge is coordination: agents need to communicate reliably, share context, and handle failures gracefully when one agent in the chain produces bad output.
Multi-agent adoption has accelerated fast. Gartner reported a 1,445% increase in inquiries about multi-agent systems from Q1 2024 to Q2 2025. Most major agent frameworks now have first-class support for multi-agent patterns. MCP, the Model Context Protocol, is becoming the standard layer for how agents connect to tools and to each other.