Companies are eager to embrace artificial intelligence, but many are slowed down by outdated data systems and rising infrastructure costs, exposing gaps in enterprise AI preparedness. As AI agents flood systems with telemetry and demand structured data, they are forcing organizations to modernize — and fast.

But in larger enterprises, there’s a major disconnect between leadership and the teams responsible for actually buying technology. Chief executive officers say they’re “all in on AI,” while legal and compliance departments are still blocking AI products, though that caution is already starting to give way to acknowledging the potential of the agentic era, according to Clint Sharp (pictured), co-founder and chief executive officer of Cribl Inc.

“What [CEOs] are going to find once they get there, and I’ve been talking to a lot of [chief information security officers] … and I’m like, ‘Hey, look, agents are going to be the future,'” Sharp told theCUBE. “Eventually, we’ll be able to eliminate a lot of your tier one analysts, and we’ll be able to 10X investigate, turn them into 10X investigators — make them way, way, way more productive.”

Sharp spoke with theCUBE’s John Furrier at AWS re:Invent, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed enterprise AI preparedness and the massive data-infrastructure overhaul required to support the coming agentic era.

Enterprise AI preparedness is critical

While AI agents promise major productivity gains, they could also force enterprises to have to expand their already expensive logging infrastructure. With data volumes rising about 30% annually and infrastructure running at near-capacity, organizations simply can’t afford to multiply their logging costs and still expect an immediate return on investment as agents generate far more queries and telemetry, according to Sharp.

“If I then go generate five times, 10 times, 20 times more queries, I’m going to need that much more infrastructure in order to accommodate that workload. That’s where the ROI is going to mismatch,” Sharp noted. “The promise is, ‘Hey, it’s going to do the work of humans so much faster than what humans are going to do.’ But the humans are generating queries today. They’re logging into search bars and typing in terms and trying to find bad actors and find problems … Agents are going to do the same thing. They’re just going to do it [at] a much more rapid clip.”

In other words, if enterprises don’t change how they collect and store telemetry, agents simply multiply query volume and infrastructure costs instead of improving return on investment. Companies such as Cribl are positioning its platforms as a way to tame that curve by filtering, routing and restructuring machine data so that customers can handle far more queries without linearly scaling their logging stack. But to handle the shift as a whole, organizations need to push for semantically consistent data and modern lakehouse architectures so agents can actually understand and act on the information rather than wade through unstructured text, according to Sharp.

“The data needs to be structured rather than unstructured. It needs to have a well understood semantic model so that the agents will be able to understand the data,” he explained. “If you start working now, by the time your lawyers and compliance people allow you to go procure an agent, then you’ll be ready to go get the benefits of our agentic future.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent:

Photo: SiliconANGLE