Multi-Agent Systems

Design systems where multiple AI agents collaborate with specialized roles and orchestrator patterns

Multi-Agent Systems

Why Multi-Agent?

**Specialization:** Each agent focuses on one thing

**Parallelism:** Independent work happens simultaneously

**Quality control:** One agent reviews another's work

**Modularity:** Add/remove agents without rewriting

Orchestrator Pattern

Orchestrator (Task Parser)
  |        |        |
Researcher Analyst  Writer
  |        |        |
Orchestrator (Combine Results)

Agent Roles

Each agent needs a clear system prompt:

python

researcher_prompt = '''
You are a research specialist.
Find relevant information on the given topic.
Return: key findings, source URLs.
Do NOT analyze — just gather.
'''

analyst_prompt = '''
You are a data analyst.
Extract patterns and insights from research findings.
Return: 3-5 key insights with evidence.
'''

When NOT to Use Multi-Agent

Task is simple (<5 steps)

Single agent achieves acceptable quality

Latency is critical

You're just starting out (start with ReAct)

Your Turn!

Design a multi-agent system for code development with roles: PM, Architect, Developer, Tester, Reviewer.

✏️ Code Editor

Loading Python...

📤 Output

Write your solution and click "Run Code" to test it!

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