From RAG to Agentic RAG: How AI Agents Decide Where to Look for Answers

Retrieval-Augmented Generation (RAG) made large language models far more useful by grounding their answers in real data. But standard RAG follows a fixed path: it searches one source once and passes whatever it finds to the model, with no way to check whether the results were right.

Agentic RAG changes that. By giving the model tools and a reasoning loop, retrieval becomes a decision the agent makes: whether it needs to search at all, which source to search, and when it has found enough. An agent can choose between a vector store, a web search, a SQL database or an API, review what it has retrieved, and try again if the answer falls short.

In this live session, we’ll walk through how that shift works in practice, see routing logic in action, and compare the trade-offs in latency, cost and reliability, so you know when to use Vanilla RAG, Agentic RAG or a mix of both in real projects.

What you’ll learn

1. Vanilla RAG and its limits: why a single source, a single retrieval pass and no self-correction can lead to incomplete or wrong answers.
2. What makes RAG agentic: how tools and a reasoning loop turn retrieval from a fixed step into a decision: whether to search, where to search and when to stop.
3. Routing logic in action: how an agent chooses between a vector store, web search, SQL database or API, using query classification, tool descriptions and self-reflection on what it has retrieved.
4. Trade-offs and when to use which: latency, cost and reliability compared, with a simple decision guide for choosing Vanilla RAG, Agentic RAG or a hybrid approach.