Adaptive Search Agents Deliver 79% Accuracy Gain in RAG Systems

Traditional RAG pipelines use fixed search strategies that fail on multi-hop questions and cannot adjust retrieval scope dynamically. A-RAG replaces this rigid architecture with autonomous AI agents that intelligently choose between keyword search, semantic search, and deep reading based on question type—improving multi-hop QA accuracy from 50.2% to 89.7% while cutting token usage in half. This research suggests a fundamental shift toward question-aware, adaptive retrieval as the new standard for RAG systems.