RAG vs GraphRAG: When Agents Hallucinate Answers

Traditional RAG makes AI agents hallucinate statistics and aggregations. This demo builds a travel booking agent with Strands Agents and compares RAG (FAISS) vs GraphRAG (Neo4j) to measure which approach reduces hallucinations when answering queries about 300 hotel FAQ documents When AI Agents Don't Just Answer Wrong—They Act Wrong In the previous blog post , we explored at a high level why AI agents hallucinate and introduced 4 essential techniques to stop them: Graph RAG, semantic tool selection, neurosymbolic guardrails, and multi agents validation. Now we're going to dive deeper into each one. This is Part 1: we'll build a travel booking agent, load 300 hotel FAQ documents, and measure exactly where traditional RAG breaks down and how GraphRAG with Neo4j eliminates those failures. When AI Agents Don't Just Answer Wrong They Act Wrong AI agents differ from chatbots. A chatbot giving incorrect information is annoying. An agent hallucinating during execution is catastrophic—it might f