NVIDIA’s Jacob Liberman on the Power of Agentic AI in the Enterprise - Ep. 250

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. Developers are excited about agentic AI, and they're not alone. More and more enterprises are deploying applications with agentic capabilities. But with the excitement comes new questions and challenges. How widespread will adoption of agentic AI become? What should AI teams be thinking about when designing and developing AI agent applications for the enterprise? And what should they be thinking about during adoption? With us live from GTC twenty twenty five to explore the growing role of AgenTic AI in the enterprise is Jacob Lieberman. Jacob is a director of product management at NVIDIA, where he leads a team building cloud native GenAI software solutions. Currently, he's focused on building accelerated storage platforms that connect AI agents to enterprise data. And prior to joining NVIDIA, Jacob held product management and engineering positions at Red Hat, AMD, and Dell. Jacob, welcome to the AI podcast, and thanks so much for taking the time to join us. Thank you for having me. I'm I'm very happy to be here. So should we start with the basics? I'm just gonna ask you, what is an AI agent? So no. I'd say an AI agent is the latest evolution in the way people are using GenAI. We're kind of in the third era of GenAI use already, which is crazy when you think about it because it's really been only eighteen months or two years since the technology became widespread. But Right. I would say that, it started out people would chat with LLMs, and they would use GenAI as copilots and assistants to do their work. Next, they used retrieval augmented generation Mhmm. To attach their LLMs to data and chat about their data. And now people are using large language models to reason and act and do things in the world. Right. So you could think about it this way. With an LLM, you could ask it to plan you a trip to Europe. With an AI agent, you could ask it to plan you a trip to Europe and to book it for you and to give it little cues like, hey. I like castles. Right. And it will do the research and create an itinerary for you, compare prices, and then actually book the trip. Right. So before we get a little deeper, is an agent a large language model with different capabilities? Is it a completely different piece of technology? Is there kind of a a concise way, and if there's not, that's fine, to sort of just set the level for the listeners of how how agents and LLMs and other models work together. Sure. So I would say that, large language model, when you train it, it's a bit of a generalist. Okay. And you can give it additional training data to make it more of a specialist. You can instruction tune it to actually follow directions and call tools and learn how to …