Is Agentic RAG actually better than standard vector search for enterprise knowledge bases?

All answers to this question.

In my experience, Agentic RAG is vastly superior for "multi-hop" questions where the answer isn't in a single document. A standard RAG just does one vector search and hopes for the best. An Agentic RAG can look at the first result, realize it's missing a specific detail (like a date or a part number), and then trigger a second targeted search. For technical documentation where information is scattered across manuals and Jira tickets, the agent's ability to "think" and "iterate" on the search query is the only way to get a complete answer, despite the 5-10 second latency increase.

   Answered 2025-11-17 by Rebecca Foster

What’s the cost difference you’re seeing? If you’re running 3-4 LLM calls per user query just for the retrieval phase, that bill is going to stack up quickly.

   Answered 2025-11-18 by Patrick Murphy

• Patrick, the cost is definitely higher, but the "Human-in-the-loop" cost of dealing with wrong answers from a standard RAG is even higher. Cheryl, you can mitigate the latency by using a smaller, cheaper model like Llama 3-8B to do the query reformulation and only using the "big" model like GPT-4 to synthesize the final answer once all the data is collected.

     Commented 2025-11-19 by George Vance

I recommend looking into "Corrective RAG" (CRAG). It's a middle ground that uses a simple evaluator to decide if the retrieved context is relevant before involving a full agentic loop.

   Answered 2025-11-20 by Brenda Russell

• That’s a great suggestion, Brenda. CRAG provides a nice balance between performance and speed. It's often the best "first step" before going full autonomous agent.

     Commented 2025-11-21 by Rebecca Foster