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Multi-Agent System

Team of specialized AI agents that collaborate on complex tasks

agentscomplex complexity
Formula
PrPrompts+LgLLM+FcFunction Call+AgAgent+MaMulti-agent+GrGuardrails

Overview

For complex problems that require diverse expertise, multi-agent systems deploy teams of specialized AI agents. Each agent has a role (researcher, coder, critic, etc.) and they collaborate through a shared protocol to achieve goals no single agent could handle.

How It Works

1. Define agent roles with specialized prompts and tools 2. Establish communication protocol between agents 3. Orchestrator assigns tasks to appropriate agents 4. Agents work, share findings, request reviews 5. Guardrails ensure agent interactions stay safe 6. Final output synthesizes all agent contributions

Use Cases

  • •Complex software development projects
  • •Research and analysis requiring multiple perspectives
  • •Content creation with writers, editors, fact-checkers
  • •Business simulations and decision support

Real-World Examples

CrewAI

Role-based multi-agent orchestration

AutoGen

Microsoft's multi-agent conversation framework

MetaGPT

Simulates a software company with specialized agents

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