How Agentic RAG Works?

Standard RAG systems fail on ambiguous queries, multi-source answers, and false confidence because they lack any decision-making between retrieval and generation. Agentic RAG addresses this by introducing an LLM-powered control loop that can refine queries before retrieval, route to multiple knowledge sources, and self-evaluate retrieved results before generating a response. The three core capabilities — query refinement, routing, and self-correction — map directly to the three main failure modes of standard RAG. However, agentic systems come with real trade-offs: higher latency (potentially 10x), increased token costs (3-10x), reduced predictability, and the risk of overcorrection. The recommendation is to treat Agentic RAG as an engineering decision rather than a default, reserving it for complex, multi-source query patterns where the added overhead is justified.