Multi-Agent Systems

Multi-agent systems involve multiple AI agents working together, each with specialized roles and capabilities. They can collaborate, delegate, and solve complex problems that single agents struggle with.

Key Insight: Specialized agents working together often outperform a single generalist agent.

Agent Roles

Researcher

Gathers information from web, databases, documents. Expert at finding relevant data.

Analyst

Processes data, identifies patterns, draws insights from information.

Writer

Creates content, reports, summaries. Optimized for clear communication.

Critic

Reviews outputs, provides feedback, ensures quality and accuracy.

Collaboration Patterns

Sequential

Agents work in order: A → B → C. Each agent's output feeds the next.

Example: Research → Analyze → Write → Review

Hierarchical

Manager agent delegates tasks to worker agents.

Example: CEO → Team Leads → Specialists

Collaborative

Agents discuss and debate to reach consensus.

Example: Multiple experts voting on best solution

Competitive

Agents compete, best output is selected.

Example: Multiple agents generate code, best one wins

Communication

Agents need to communicate effectively. Common approaches:

Shared Memory: All agents access common context/database
Message Passing: Agents send structured messages to each other
Blackboard: Central knowledge base agents read/write to
Direct Handoff: One agent explicitly passes control to another

Frameworks

CrewAI

Role-based multi-agent orchestration. Define crews with specific roles.

crew = Crew(agents=[researcher, writer])

AutoGen

Microsoft's framework. Agents can be human or AI, with flexible conversation patterns.

chat = GroupChat(agents=[agent1, agent2])

Use Cases

Content Creation: Research + Write + Edit + Fact-check
Software Development: Architect + Coder + Tester + Reviewer
Customer Support: Classifier + Specialist + Escalation
Data Analysis: Collector + Cleaner + Analyst + Visualizer
Research: Multiple researchers covering different sources

Challenges

⚠️ Coordination Overhead: More agents = more complexity

⚠️ Cost: Multiple LLM calls can get expensive

⚠️ Latency: Sequential agents take longer

⚠️ Debugging: Harder to trace issues across agents

Best Practice: Start with a single agent. Add more only when specialization provides clear benefits.