In this video, I break down Agentic RAG the way I actually design these systems in the real world: not “RAG vs search” as a debate, but a routing problem an AI agent should solve. I show how an autonomous agent can take mixed inputs like PDFs and web URLs, automatically chunk and store content into a VectorDB, and then answer questions by choosing the best retrieval path—vector search when you’ve already ingested reliable documents, and web search when the query needs fresh or missing context. The key idea is simple: let the agent decide the tool, not you. I walk through the design pattern, the decision logic (when to hit VectorDB vs when to browse), and a full demo where I upload research papers and ask questions that force the system to reason about source selection. If you’re building an “answer engine” or a document analysis assistant, this is the workflow that keeps your architecture clean: ingestion → chunking → indexing → tool selection → grounded answering. I also point you to the prerequisites on AI agents and agentic design patterns so you’re not copy-pasting code without understanding the system design.