What Is Agentic RAG?

Agentic RAG upgrades traditional retrieval-augmented generation by adding an intelligent routing layer that decides which knowledge base to consult before the LLM generates an answer. In a standard RAG setup, a user query is embedded, sent to a single vector database to fetch relevant context, and then the LLM uses that context (plus a prompt) to produce the final response. The LLM’s job is largely limited to summarizing or generating based on retrieved text, because the system assumes one fixed source of truth.

The transcript contrasts that fixed flow with an agentic workflow built for messy, real-world knowledge. Instead of one vector database, the system can maintain multiple specialized stores—for example, one holding Udemy course content and another holding live-course materials. When a user asks for “recent Udemy courses on agentic AI,” the key problem becomes deciding whether the query should hit the Udemy database or the live-courses database. In traditional RAG, that decision would typically be hard-coded or handled outside the retrieval step. In agentic RAG, the LLM is wrapped into an agent that can dynamically route the query to the most relevant retriever tool (each tool corresponds to a vector database).

That routing step is the central difference. The agent is integrated with multiple retriever tools, so it can choose DB2 for Udemy-related questions and DB3 for live-course questions. Once the agent selects the right database, retrieval returns more accurate context, and the LLM then summarizes that context into the final answer. The transcript emphasizes that this improves accuracy because the system avoids forcing every query through the same retrieval pipeline when different queries map better to different knowledge sources.

The explanation also frames agentic RAG as a framework that enhances traditional RAG by incorporating intelligent agents to handle complex tasks and make decisions dynamically. It highlights a practical implementation pattern using LangGraph-style routing workflows: a request enters the workflow, the agent checks relevance against the connected vector store, and the system can branch to different retrieval paths. If the needed information is missing from one vector database, the workflow can continue to other conditions or fall back to the LLM’s own capabilities, with additional routing rules controlling what happens next.

Finally, the transcript positions agentic RAG as a scalable approach. As more specialized vector databases are added—such as company policy documents or other domain corpora—each becomes a retriever tool the agent can select from. The result is a more modular system where the agent chooses the best context source per query, rather than relying on a single retrieval store for every question.