Agentic RAG for Internal Tools: Designing an LLM Agent That Knows When Not to Query Your Vector Store
Your agent just burned through 50,000 tokens retrieving documents to answer "What is 2+2?" That happened to someone building an internal tool with AutoGen. The agent had access to a vector store containing company documentation. Every query, regardless of complexity or type, triggered a retrieval call. Simple arithmetic, date formatting, basic string manipulation—the agent dutifully searched through thousands of embeddings before responding. The vector store bill arrived. Management asked questions. The problem with agentic RAG systems today is not teaching agents when to retrieve. That part is easy. The hard part, the part that separates a functional internal tool from an expensive disaster, is teaching the agent when retrieval is unnecessary, irrelevant, or actively harmful. Most tutorials show you how to wire up LangChain or AutoGen with a vector store, wave their hands at "the agent will figure it out," then move on to the next shiny feature. Reality deliver...
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