The article provides a comprehensive guide on building a production-ready Retrieval-Augmented Generation (RAG) system using Google ADK and Vertex AI RAG Engine. It addresses the challenge of modern knowledge management by explaining how RAG agents can access proprietary knowledge bases to reduce inaccuracies and hallucinations in AI-generated responses. The system processes documents from various sources, converts them into vector representations, and utilizes a hybrid search combining semantic and keyword matching for accurate retrieval. It also supports multi-modal content and real-time web data integration with Bright Data for keeping the knowledge base current. The guide details the setup of a development environment, document ingestion, vector embedding, and the creation of an intelligent RAG agent that manages conversation context and generates responses with proper grounding and citations. Furthermore, it explores the integration of Bright Data to enhance RAG capabilities with real-time web data, offering patterns for dataset integration, real-time scraping, and AI scraper insights to expand the system's scope. The article concludes by emphasizing the benefits of combining proprietary and external data to maintain accuracy, scalability, and comprehensive knowledge retrieval in AI applications.