Multi-Agent Systems

Introduction

Multi-agent systems use multiple specialised agents that collaborate — researcher, planner, coder, critic. They tackle problems too complex for a single agent.

Beginner analogy: Like assembling a startup team where each member has a clear role and they message each other.

Understanding the topic

Core concepts to understand:

• Each agent has a role + system prompt + allowed tools.
• Agents communicate via messages (often through a shared state).
• Coordinator (manager agent) assigns work.
• Frameworks: CrewAI, AutoGen, LangGraph.

Syntax reference

Visual workflow / architecture:

        ┌─── Manager ───┐
        │       │       │
        ▼       ▼       ▼
   Researcher  Coder   Critic
        │       │       │
        └───►Shared State◄────┘
                │
                ▼
            Final output

Real-world use

Microsoft AutoGen, CrewAI case studies show research + coding + writing teams of agents shipping production work.

Best practices

• Keep roles narrow — generalist agents underperform.
• Cap total iterations across all agents.
• Log inter-agent messages for debugging.

Common mistakes

• Agents bickering forever — add tie-breakers.
• Cost explosion across many parallel agents.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is a multi-agent system?
• Q2. Pros and cons vs a single agent?
• Q3. How do you prevent infinite agent loops?