Agentic RAG: A Next Generation RAG Architecture

Retrieval-augmented generation (RAG) has become a cornerstone technique for grounding large language models (LLMs) on external knowledge. However, traditional RAG pipelines are essentially static: an LLM retrieves documents from a single source and generates an answer, without the ability to reason about the retrieval process or adapt when one pass isn’t enough.

For example, a medical query like, What are the latest treatment options for Type 2 diabetes and how do they compare?, may require first retrieving up-to-date clinical trial results and then fetching guideline documents for comparison. A single retrieval pass would likely miss one of these layers, leaving the answer incomplete.

Agentic RAG is an emerging paradigm that embeds autonomous AI agents into the RAG pipeline. An agent is simply an AI system that can make decisions and take actions toward a goal, such as rephrasing a search query, calling an external tool (e.g., a calculator or database), or combining results from multiple sources before answering. This idea of agency, i.e., the ability to plan, act, and adapt, makes agentic RAG more powerful than standard RAG. By introducing reasoning loops, tool use, and even multiple cooperating agents, agentic RAG systems push beyond the limitations of standard RAG to handle more complex queries with greater adaptability.

In this newsletter, we’ll explore agentic RAG’s architecture and theoretical foundations, compare it with standard RAG, and guide on implementing an agentic RAG system, complete with example code and diagrams.

From RAG to agentic RAG

A traditional RAG system consists of two main components: