Why Multiple Agents?

A single agent can be powerful, but some problems are too complex, too large, or too time-sensitive for one agent to handle alone. Multi-agent systems distribute work across specialized agents that collaborate, communicate, and coordinate to achieve shared goals.

Think of it like a company: you don't have one person doing everything. You have specialists (marketing, engineering, sales) who work together. Multi-agent systems apply this same principle to AI.

Interactive: Single vs Multi-Agent Performance

Compare how different agent configurations handle the same task (creating a research report):

🤖

Single Agent

One agent handles everything sequentially

Tasks Handled:

•Research

•Analyze

•Write

•Review

•Format

Performance:

Completion Time:

60 minutes

Considerations:

•Sequential execution

•No specialization

•Single point of failure

⚠️ Limited by sequential execution and lack of specialization

⚡

Parallelization

Multiple agents work simultaneously

🎯

Specialization

Each agent focuses on specific tasks

🔄

Fault Tolerance

System continues if one agent fails

🎯 When to Use Multi-Agent

✓Complex workflows with distinct subtasks

✓Time-critical tasks requiring parallelization

✓Specialized expertise needed in different domains

✓Fault tolerance is critical for reliability

Introduction to Multi-Agent Systems

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