Agentic RAG & Semantic Caching: Building Smarter Enterprise Knowledge Systems

Author(s): Utkarsh Mittal Originally published on Towards AI. Section 1: The Rise (and Limitations) of RAG Enterprise data is messy. It lives in Slack threads, Google Drive folders, SharePoint libraries, spreadsheets buried three levels deep in someone’s OneDrive, and meeting transcripts that no one ever reads again. Structured data has always been manageable — you query a database, you get an answer. But unstructured data? That’s the vast majority of what organizations produce, and before 2023, the best tool we had to navigate it was Ctrl+F. Figure 1: RAG Architecture DiagramThis article discusses the evolution of Retrieval Augmented Generation (RAG) systems, highlighting their initial limitations and the transition to more sophisticated architectures including Agentic RAG and Semantic Caching. It emphasizes the importance of structured data organization, the roles of various components in these newer systems, and depicts how advancements address pain points inherent in earlier models. The article concludes by showcasing practical implementations and detailing the construction of an agentic RAG system that integrates real-time data querying with intelligent routing and retrieval strategies, paving the way for smarter enterprise knowledge techniques. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI