Multi-Agent AI System Orchestration: Building Complex AI Workflows
LangGraph, CrewAI, and human-in-the-loop patterns for production AI systems
Multi-Agent AI System Orchestration: Building Complex AI Workflows
LangGraph, CrewAI, and human-in-the-loop patterns for production AI systems
Design and implement multi-agent AI systems using LangGraph, AutoGen, and CrewAI for complex task decomposition, parallel execution, and human-in-the-loop workflows.
Multi-agent systems excel at complex tasks requiring diverse expertise or long-horizon planning. LangGraph models workflows as state machines with typed state, nodes as async functions, and edges defining flow. CrewAI uses higher-level abstractions with Agents (role, goal, tools), Tasks (description, expected output), and Crews. For human-in-the-loop: use LangGraph checkpointing with interrupt_before parameter to pause before irreversible actions, wait for approval, then resume. Best practices: design clear agent boundaries, use typed state throughout, implement timeouts and recursion limits, add comprehensive observability, test agents in isolation before system integration.