Developer Upgrades Local RAG Assistant to Agentic AI Using LangGraph

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A developer has documented how they evolved a fully local RAG assistant — built with Ollama, ChromaDB, and LangChain — into an agentic AI system using LangGraph. The original setup treated every support query identically, routing all questions through the same retrieval pipeline regardless of their nature. The upgraded architecture adds a classifier layer that first categorises each query as procedural, a known error, or a complex incident before routing it to the appropriate agent. Specialised agents — a RAG agent, a diagnostic agent, and an escalation agent — share a common state object and update it as they handle each case. The key innovation is a planning step before action, allowing the system to mimic how a real support technician assesses a situation before deciding how to respond.

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