From RAG to Agentic AI: Adding LangGraph for Conditional Routing in a Local Support Assistant

Short summary

The author evolves a local RAG assistant into an agentic AI architecture using LangGraph, adding a classifier that routes queries to specialized agents (RAG, diagnostic, escalation) based on question type. Shared state is managed via a TypedDict that agents read and update, including a support-level field for automatic escalation. The article includes concrete Python code for state definition and the RAG agent implementation.

• •Classic RAG treats every query identically; agentic AI adds a reasoning layer that classifies and routes before acting
• •LangGraph connects autonomous agents into a directed graph with conditional routing and shared state
• •Code shows AgentState TypedDict, RAG agent implementation, and escalation logic via a support-level field

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