Agentic Retrieval-Augmented Generation (RAG) is an advanced information retrieval system that integrates AI agents to enhance productivity and reduce costs in AI workflows. By combining AI agents with RAG, systems can autonomously retrieve and process live web data, making them more dynamic and capable of handling real-time queries. This guide demonstrates building an Agentic RAG system using Bright Data for web scraping, Pinecone as a vector database, and OpenAI for text generation, coordinated by an agent controller. The system requires setting up API keys for Bright Data, OpenAI, and Pinecone, and involves steps such as embedding generation, data retrieval, and embedding updates. A feedback loop is suggested for continuous optimization. The effectiveness of the system heavily depends on the quality of input data, for which Bright Data provides reliable and structured web data. The guide encourages exploring enhancements like integrating with other databases and automating data retrieval to maintain high-quality input data.