A groundbreaking architecture integrates Retrieval-Augmented Generation (RAG) directly into AI agents, fundamentally transforming how they access knowledge, reason, and execute complex tasks. This shift moves beyond standalone LLMs by creating persistent, updatable knowledge backbones for agents—enabling more accurate, context-aware problem-solving across domains like coding and data analysis.