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Multi-Agent Systems

Learn to orchestrate multiple agents working together on complex tasks

Multi-Agent Systems

When one agent is not enough

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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.

Examples
  • •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.

OrchestrationSupervisorWorker Agent
  1. Receive - Task comes in (user request, scheduled trigger, etc.)
  2. Route - Supervisor/router decides which agent(s) to involve
  3. Delegate - Task is sent to specialist agent(s)
  4. Execute - Specialist agents do their work
  5. Aggregate - Results are collected and combined
  6. Respond - Final output returned to user