Personal Agents Light the Fuse In the Age of Data Intelligence

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Breaking Analysis with Dave Vellante 55 min 2 speakers 8 chapters transcribed 1 month ago

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What is the AI wave and how does it compare to the PC era?

George Gilbert 0:00

From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante.

Dave Vellante 0:11

The AI wave is starting to look a little bit like the PC era with some obvious differences. But we're talking about personal productivity. Bottoms up. And decentralized funding and individuals taking control of their own work with agents, open tools, and repeatable skills, much like power users once did with spreadsheets and PCs. But unlike the PC era, these agents don't just create documents and dashboards, they act. So if every person and every department And every vendor builds its own island of intelligence, hmm while this might be necessary in in in the journey to the end state of AI. Might be a necessary step, but we risk Recreating the enterprise silo problem that has plagued software for decades.

Dave Vellante 1:00

You know, only it's got this time it's gonna happen faster with greater liabilities. So the premise of this breaking analysis is that. While the impetus for AI initially came from CEOs, top-down CEOs and boards of directors, the first phase of enterprise enterprise AI. Is largely taking shape and being driven by individuals and personal agents that are being deployed. We think that the sustainable value is going to accrue to the platforms that organize enterprise knowledge into a true system of intelligence, what we call the SOI. Now ahead of Snowflake Summit and Databricks data plus AI summit. We're going to focus in on these two companies in context of the industry. As we wrote about a year ago in a previous episode, almost exactly a year ago, both of those companies, Snowflake and Databricks, they've crossed the Rubicon.

Dave Vellante 1:55

They're no longer just data platforms serving only analytics. They're moving into the layer. Where enterprise data Trust Context, actions, and even Business logic. become human readable. Agent readable. And eventually executable by agents. That same control point is being pursued by others, namely application vendors, hyperscalers, and even frontier model companies, because whoever helps organizations best model the business is going to shape how agents reason, how they decide, and how they act. And this transformation is we think it's going to happen in two motions simultaneously. Bottom up individuals that are going to build personal agents and skills that immediately improve productivity, and then top down.

Dave Vellante 2:48

Leadership has to guide that energy toward an AI native architecture so that those skills become governed assets that are connected to a share on shared ontology, not just another generation of disconnected tools. We don't want that. So the caution, by the way, is. Model makers, the big LLM vendors, are going to try to bundle the model, the agent, the interface, and their own version of enterprise memory. We're seeing that early stages of that already. Frontier models, they're very, very important. They're critical, in fact. But owning the model is not the same as owning the enterprise operating context. So the system of intelligence is the world that the agent lives in. The model Think of that as the engine that reasons inside that world.

Dave Vellante 3:34

And in this episode of Breaking Analysis, we're going to dig deeper into the emerging AI software stack. Prescribe the steps as to how enterprises can evolve. This model and what to watch out for and how they can be most effective. Welcome, George Gilbert, the architect. of this enterprise model and and deep thinker on this topic. George, thanks for coming on and good

George Gilbert 3:57

to be with you, Dave, as

Dave Vellante 3:58

always. All right, awesome. Let's bring up the first slide here and start by reviewing yet again the emerging AI software stack. Now the concepts behind this model are borrowed from Jeffrey Moore, who's been working with George to really envision the future state of the enterprise and how to get from where we are today to where we're going. And George, we talked about this last week in breaking analysis, but but set up the puzzle pieces of this AI stack that we're showing here.

George Gilbert 4:24

Okay, so