Agentic RAG: When Retrieval Gets Smart
Naive RAG always retrieves, even when the model already knows the answer — wasting tokens, latency, and money. Agentic RAG turns retrieval into a tool the model decides to call, evaluates, and retries. This is exactly how Claude's tool use, Perplexity's multi-source research, and ChatGPT's web-browse-when-needed work in 2026. Self-RAG, Corrective RAG, query routing, MCP-served retrievers — retrieval becomes a verb, not a pipeline stage.
Learning Objectives
After this lesson, you will be able to:
- Understand how query routing looks at a user's question and automatically picks the best way to find the answer -- vector search, web search, database query, or no search at all
- Describe Self-RAG: the AI first decides whether it even needs to search, and after searching, checks if what it found is actually useful before answering
- Compare fallback strategies (Corrective RAG) for what happens when the first search comes up empty or irrelevant
- Design a multi-step retrieval pipeline for complex questions that have several parts requiring different sources
Prerequisites