Multi-Agent Systems
Learn to orchestrate multiple agents working together on complex tasks
Multi-Agent Systems
When one agent is not enough
Imagine a hospital. You would not want one doctor doing surgery, prescribing medication, handling billing, and scheduling appointments. Each task requires different expertise, and splitting the work lets specialists focus on what they do best.
Multi-agent systems work the same way. Instead of one "general purpose" agent trying to do everything, you have specialist agents that excel at specific tasks, coordinated by a manager agent.
- •A customer service system: Router → Billing Agent / Technical Agent / Returns Agent
- •A content pipeline: Research Agent → Writer Agent → Editor Agent
- •A code assistant: Planner Agent → Coder Agent → Reviewer Agent → Tester Agent
A multi-agent system is a group of AI agents that work together to accomplish tasks that would be difficult or impossible for a single agent.
Multi-Agent = Specialisation + Coordination + Communication
The key is that agents have different roles and tools, and there is a clear structure for how they work together.
- Receive - Task comes in (user request, scheduled trigger, etc.)
- Route - Supervisor/router decides which agent(s) to involve
- Delegate - Task is sent to specialist agent(s)
- Execute - Specialist agents do their work
- Aggregate - Results are collected and combined
- Respond - Final output returned to user
There are several ways to implement multi-agent systems:
- LangGraph - Python library for stateful agent workflows
- AutoGen - Microsoft's multi-agent conversation framework
- CrewAI - Role-based agent orchestration
- Custom - Build your own with API calls and message passing
The principles are the same regardless of framework - define agents, their roles, and how they communicate.