Multi-Agent AI Systems
Build AI systems with multiple specialized agents that collaborate, debate, and solve complex tasks together.
TL;DR
Multi-agent systems use multiple AI agents with different roles (researcher, writer, critic) that collaborate to solve complex tasks requiring diverse skills.
Agent architectures
Sequential: Agents work in pipeline (researcher → writer → editor)
Hierarchical: Manager agent coordinates worker agents
Collaborative: Agents discuss and refine together
Competitive: Agents propose solutions, best one wins
Communication patterns
- Shared memory/context
- Message passing
- Debate and refinement
- Voting/consensus
Use cases
- Complex research tasks
- Content creation with review
- Code generation + testing + debugging
- Multi-perspective analysis
Implementation frameworks
- AutoGen (Microsoft)
- CrewAI
- LangGraph
- Custom orchestration
Challenges
- Coordination overhead
- Conflicting outputs
- Cost (multiple LLM calls)
- Debugging complexity
Best practices
- Clear agent roles
- Explicit communication protocols
- Termination conditions
- Monitor costs
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Key Terms Used in This Guide
Agent
An AI system that can use tools, make decisions, and take actions to complete tasks autonomously rather than just answering questions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligence—like understanding language, recognizing patterns, or making decisions.
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