Multi-Agent Systems Explained

Multi-Agent Systems (MAS) are systems where multiple AI agents work together to solve complex problems. Instead of one all-powerful AI, you have specialized agents that communicate and collaborate.

Why Multiple Agents?

• Specialization: Each agent can be optimized for a specific task
• Scalability: Add more agents as needed
• Robustness: System continues even if one agent fails
• Cost efficiency: Use simple agents for simple tasks

Agent Types

1. Research Agent

Searches the web, reads documents, gathers information.

2. Coder Agent

Writes code, fixes bugs, implements features.

3. Review Agent

Reviews code, checks tests, ensures quality.

4. Deploy Agent

Deploys to production, manages infrastructure.

5. QA Agent

Runs tests, reports bugs, validates outputs.

Communication Patterns

Real-World Example

User: "Write a web app"
  |
  v
[Orchestrator]
  |
  +--> [Research Agent] --> "Best frameworks for web apps"
  |
  +--> [Coder Agent] --> "Writes the code"
  |
  +--> [Review Agent] --> "Reviews the code"
  |
  v
Final Web App