Multi-agent patterns represent powerful architectural approaches for building complex AI systems where multiple specialized agents collaborate to solve problems beyond the capabilities of single-agent systems. These patterns have emerged as fundamental building blocks in modern AI frameworks including Google ADK, LangChain/LangGraph, Crew AI, and Microsoft AutoGen.

Each pattern addresses specific collaboration challenges, from centralized coordination and sequential processing to parallel execution and human augmentation. Understanding these patterns provides developers with a conceptual toolkit for designing AI systems that can tackle increasingly complex real-world problems through structured agent collaboration.

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Note: these patterns are not mutually exclusive - sophisticated AI applications often combine and layer multiple patterns together, such as using a coordinator pattern to manage several parallel pipelines, each with their own hierarchical decomposition and human oversight components, creating hybrid architectures tailored to specific complex problem domains.

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Coordinator/Dispatcher Pattern: A central agent analyzes tasks and routes them to specialized agents for specific domains.

Sequential Pipeline Pattern: Agents work in sequence, with each agent's output becoming the next agent's input.