A thought provoking piece from Shahab Anbarjafari • Professor, Author, Public Speaker

I have been discussing with a few CTOs and CIOs, all sharp, no-nonsense leaders. The question they tossed at me sounded simple: “Shahab, what’s the difference between using Retrieval Augmented Generation (hashtag#RAG) and using hashtag#Agents?” But I could sense their real curiosity—like they were trying to understand a new dimension of AI that could define the future of their organizations.

In that moment, I felt like I was back at the dawn of deep learning, where we realized that neural networks were more than just pattern-matchers. They could transform entire industries, challenge our assumptions, and inspire daring research.

RAG is like giving your Large Language Model (hashtag#LLM) a map to a lost city. Instead of wandering aimlessly in the model’s own “imagination”, it can look up precise directions from your PDFs, databases, and other external sources. Suddenly, your LLM isn’t just guessing—it’s informed. It’s answering questions with grounded knowledge, not just well-structured fluff.

But then come the Agents—picture them as explorers who don’t just consult maps, but also carry a toolkit, a journal of past expeditions, and the ability to plan elaborate routes. Agents don’t just answer questions; they hashtag#act. They query other tools like search engines, calendars, APIs—whatever you hand them. They learn from ongoing dialogues and past experiences, piecing together complex solutions that transcend Q&A and approach real strategic thinking.

Standing there, explaining this to these tech leaders, I realized: this isn’t a trivial distinction. It’s the difference between having a brilliant “know-it-all” consultant at your side, and having an entire adaptive, resourceful team that can navigate complexity, make decisions, and get things done. It’s a shift from knowledge retrieval to orchestrated problem-solving.

I hope now you understand the leap from RAG’s information-rich accuracy to Agents’ dynamic, tool-empowered, long-horizon creativity. This, I believe, is where the next wave of breakthroughs in AI will come from—an evolution as profound as the shift from supervised learning to self-supervision, or from static feature engineering to deep representation learning.

In short:
RAG: Informed answers.
Agents: Strategic action.

And just like that, you’ve got a taste of tomorrow’s AI frontiers.