LangChain and LangGraph: Building Reliable Agentic AI Workflows Modern AI applications are no longer simple chatbot wrappers around an LLM. Real enterprise AI systems need to: understand user intent retrieve relevant context call tools and APIs maintain state follow business rules validate outputs retry failed steps escalate risky decisions produce auditable results This is where LangChain and LangGraph are useful. LangChain provides building blocks for connecting LLMs with tools, prompts, retrievers, vector databases, APIs, and external systems. LangGraph provides a graph-based orchestration layer for building stateful, multi-step, controllable AI workflows. In simple terms: LangChain connects the AI to capabilities. LangGraph controls how those capabilities are used. 1. What Is LangChain? LangChain is a framework for building applications powered by large language models.…