What is Agentic RAG?
Agentic RAG is an advanced retrieval-augmented generation pattern where an AI agent autonomously decides when, what, and how to retrieve information during the generation process. Unlike traditional RAG systems that follow fixed retrieval patterns, agentic RAG gives the agent control over the retrieval decision-making process, allowing it to adaptively query knowledge sources based on its understanding of the task and context.
This approach treats retrieval as a dynamic tool that the agent can strategically employ rather than a static preprocessing step. The agent can decide whether retrieval is necessary, formulate multiple queries, evaluate the quality of retrieved information, and even retry or refine queries based on the results. This flexibility enables more sophisticated information gathering strategies and better handling of complex questions that require multiple rounds of retrieval.
Agentic RAG is particularly powerful for tasks requiring multi-step reasoning, where the agent must gather different pieces of information at various stages of problem-solving. It combines the strengths of autonomous agents with retrieval-augmented generation, resulting in systems that can navigate large knowledge bases more intelligently and produce more accurate, well-grounded responses.