Letting AI Control RAG Search Improved Accuracy by 79%

Letting AI Control RAG Search Improved Accuracy by 79% Most RAG (Retrieval-Augmented Generation) search pipelines are built like this: Query → vector search → Top-K retrieval → dump everything into LLM This fixed pipeline is the root cause limiting RAG accuracy. A February 2026 ArXiv paper (arXiv:2602.03442) proposed A-RAG (Agentic RAG), replacing the fixed search pipeline with an AI agent. Result: multi-hop QA accuracy improved by 79% (50.2% → 89.7%). And retrieved tokens dropped by half. Higher accuracy with less retrieval. Here's how this counter-intuitive result works. Three Limits of Fixed-Pipeline RAG Limit 1: Weak on Multi-Hop Questions Question: "Where did the person who invented X attend university?" Required searches: Round 1: "Who invented X" → identify the person Round 2: "That person's university" → get the answer Fixed pipeline: One vector search for "inventor of X + university" → No chunk directly contains the answer → Retrieves many low-relevance chunks → LLM guesses →