Multi-Agent Introduction

Introduction

A multi-agent system is several specialised agents that collaborate to achieve a goal. Each agent has a narrower scope, smaller prompt and clearer evals than one mega-agent.

Beginner analogy: a movie crew — director, cinematographer, editor, sound — beats one person trying to do everything.

Understanding the topic

Core concepts:

• Specialisation: each agent does one thing well.
• Coordination: supervisor or peer protocols.
• Composability: swap a specialist without breaking others.
• Higher cost & latency, higher quality on complex tasks.
• Use multi-agent only when single-agent breaks down.

Syntax reference

Visual workflow / architecture:

┌──────────────┐
│ Supervisor   │  routes tasks
└──┬───────┬───┘
   │       │
   ▼       ▼
┌──────┐ ┌──────┐
│ Plan │ │ Code │  specialists
└──┬───┘ └──┬───┘
   │        │
   ▼        ▼
┌──────────────┐
│   Critic     │  reviews output
└──────┬───────┘
       ▼
   Aggregator → Final Answer

Real-world use

Devin (planner+coder+critic), Cognosys, MetaGPT (entire software house), CrewAI showcase, OpenAI Swarm samples.

Best practices

• Add agents one by one with evals each time.
• Resist the 10-agent design — start with 2-3.

Common mistakes

• Mega-multi-agent architectures with vague responsibilities.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Two benefits of multi-agent over single-agent.
• Q2. Two downsides.
• Q3. Scenario: design a 3-agent team for blog content production.