Last updated on February 23, 2026 by Editorial Team
Author(s): Utkarsh Mittal
Originally published on Towards AI.
Section 1: The rise (and limitations) of RAGs.
Enterprise data is messed up. It resembles Slack threads, Google Drive folders, SharePoint libraries, spreadsheets buried three levels deep in someone’s OneDrive, and transcripts that no one ever read. Structured data has always been manageable – you ask the database, you get the answer. But unstructured data? This is the vast majority of items produced by organizations, and before 2023, the best tool we had for navigating it was Ctrl+F.
This article discusses the development of Retrieval Augmented Generation (RAG) systems, highlighting their early limitations and the transition to more sophisticated architectures, including agentive RAG and semantic caching. It emphasizes the importance of structured data organization, the roles of different components in these new systems, and shows how advances address pain points inherent in earlier models. The article concludes by demonstrating the practical implementation and detailing the construction of an agentic RAG system that integrates real-time data query with intelligent routing and retrieval strategies, paving the way for smart enterprise knowledge technologies.
Read the entire blog for free on Medium.
Published via Towards AI
We build enterprise-grade AI. We will also teach you how to master it.
15 Engineers. 100,000+ students. The AI ​​Academy side teaches what actually avoids production.
Get started for free – no commitments:
→ 6-Day Agent AI Engineering Email Guide – One Practical Lesson Per Day
→ Agents Architecture Cheatsheet – 3 Years of Architecture Decisions in 6 Pages
Our courses:
→ AI Engineering Certification – 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course.
→ Agent Engineering Course – Hands-on with production agent architectures, memory, routing, and eval frameworks – built from real enterprise engagements.
→ AI for Work – Understand, evaluate, and apply AI to complex work tasks.
Comment: The content of the article represents the views of the contributing authors and not those of AI.
