From LLMs to Agentic AI: A Practical Guide to What They Mean and How to Choose

LLMs vs Agentic AI

As definitions, use cases, expectations, and investments around Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and agentic AI continue to expand, many individuals and organizations find themselves confused about how these concepts differ—and which approaches to adopt, when, and why. The rapid pace of AI innovation has only added to this uncertainty. To simplify this landscape and make the distinctions easier to understand, Ai_Vaidehi shared the following table on X.

The Compounding Value of AI

Artificial intelligence has become a deeply embedded layer in how businesses operate, compete, and create value. As AI grows more pervasive and sophisticated, each level—from foundational data and infrastructure to advanced machine learning, specialized capabilities like NLP and computer vision, and autonomous agents—builds on the one below, forming an increasingly intelligent and unified stack. This visual captures that evolution. Showing how AI is not just about isolated tools or models, but a layered architecture where every tier amplifies automation, decision‑making, and strategic insight. In that sense, the real opportunity lies not in adopting AI point solutions, but in orchestrating these layers into a coherent system that learns, adapts, and delivers compounding value over time.

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