Multi-Agent Systems: Architecture, Patterns & Frameworks

What Are Multi-Agent Systems?

Multi-agent systems (MAS) use two or more AI agents that collaborate, compete, or coordinate to accomplish tasks that a single agent cannot handle efficiently. Each agent has specialized capabilities, and the system's intelligence emerges from their interaction.

Examples: •A research agent + writing agent + fact-checking agent collaborating on a report •A planning agent that delegates subtasks to specialist agents •Debate between agents to find the best solution

Architecture Patterns

Supervisor Pattern: One agent manages and delegates to others. Best for structured workflows.

Peer-to-Peer: Agents communicate directly. Best for collaborative problem-solving.

Hierarchical: Multi-level management with team leads and workers. Best for complex organizations.

Pipeline: Agents process sequentially, each adding value. Best for content creation or data processing.

Debate/Adversarial: Agents challenge each other's outputs. Best for accuracy-critical tasks.

Frameworks Comparison

LangGraph — Most flexible, graph-based orchestration, production-ready CrewAI — Simplest API, role-based teams, rapid prototyping AutoGen (Microsoft) — Conversational patterns, code execution, research-focused Semantic Kernel (Microsoft) — Enterprise-grade, .NET/Python, Azure-native Agency Swarm — OpenAI Assistants API-based, simple setup

Production Considerations

•Cost management — multi-agent = multi-LLM calls. Monitor and optimize. •Latency — sequential agents add latency. Parallelize where possible. •Error propagation — one agent's mistake can cascade. Add validation between steps. •Observability — trace which agent did what. Use structured logging. •Testing — test individual agents AND their interactions.

Frequently Asked Questions

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