agentic-arch

Build a multi-agent system with LangGraph by constructing stateful graphs that decompose broad queries, delegate sub-tasks to specialized agents, and synthesize findings into comprehensive reports using robust state management and conditional routing patterns.

Coordinate multiple AI agents using CrewAI by defining role-based teams where specialized agents handle distinct tasks like web search or data analysis, enabling reliable execution and planning for complex autonomous applications.

State management in autonomous agent architectures works by maintaining context across agent loops and conditional routing steps, ensuring reliable execution and data continuity when agents delegate sub-tasks or synthesize findings within frameworks like LangGraph.

You can integrate external tools into an AI agent loop by applying detailed tool integration patterns and design examples for graph construction, allowing agents to reliably execute web searches, calculations, and data analysis within a production-grade framework.

Choose LangGraph over CrewAI when your multi-agent coordination requires stateful graph construction with conditional routing, and choose CrewAI when you need role-based teams with specialized agent delegation for autonomous research and report generation.

You need familiarity with AI architecture concepts to implement production-grade agent systems, as this guidance focuses on advanced design patterns, state management, and reliable execution for complex autonomous applications using LangGraph and CrewAI.