Agentic RAG

Agentic RAG extends classic RAG with AI agents that independently plan, control, and iteratively optimize the retrieval process.

What is Agentic RAG?

Classic RAG follows a rigid workflow: embed the question, search for similar documents, generate an answer. For simple questions, that’s sufficient — but for complex queries requiring multiple sources and relationships, this approach hits its limits. Agentic RAG overcomes this limitation by having an AI agent iteratively control the search process and adjust it as needed. For companies with extensive knowledge bases, this is a game changer.

Agentic RAG is the next evolutionary stage of Retrieval Augmented Generation (RAG). While classic RAG follows a fixed workflow — embed a question, search for similar documents, generate an answer — in Agentic RAG, an AI agent takes control of the entire process. It independently decides which sources to search, whether the information found is sufficient, and whether additional search steps are needed.

The agent can combine multiple strategies: first run a Hybrid Search, then re-rank the results with Cross-Encoder Reranking, search a Knowledge Graph for relationships as needed, and finally decide whether the collected information is sufficient for a well-founded answer — or whether it needs to search again. This iterative process delivers significantly better results than rigid RAG pipelines.

For companies with extensive knowledge databases, Agentic RAG is a game changer. Complex questions requiring multiple documents and relationships are answered more reliably. Agentic Engineering provides the methods to make such systems robust — especially for Agentic Chunking and dynamic source selection.