Artificial intelligence is well into its prove-it phase, and the agentic AI stack is the new system for task-driven agents that ship, scale and pay back.

The enterprises looking beyond the headlines are instead standardizing on measurable outcomes. Bigger models alone won’t close the gap between demos and dividends. Instead, real progress comes when reliable, well-governed data underpins the entire build-and-deploy cycle, according to Rajeev Nayar (pictured, left), vice president of data and AI at Tiger Analytics.

Tiger’s Rajeev Nayar and Google Cloud’s Iliana Quinonez talk with theCUBE about the importance of data in the era of the agentic AI stack.

“One of the things that people are beginning to realize is good AI requires good data,” he said. “Building the foundation of the data — making sure that you invest in that so that you can actually both scale your AI and get value out of it — is becoming a critical issue.”

Nayer and Iliana Quinonez (right), director of customer engineering for North America startups at Google Cloud, spoke with theCUBE’s Rebecca Knight for the Google Cloud Partner AI Series event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how the agentic AI stack – much like any AI deployment – fundamentally depends on a solid data foundation. (* Disclosure below.)

Inside the agentic AI stack: Data is the currency, ROI is the compass

For strategic partners like Google Cloud and Tiger, data quality and governance are a non-negotiable base layer for the agentic AI stack. Firms are reframing data strategy as a balance sheet item, not a chore, because return on investment only compounds when the foundation is sound, according to Quinonez. 

“Think about your data and think about your data as a currency,” she said. “That’s your new currency in business. The best you can do with your data is acquire, leverage, leave and secure it — and then put it to good use. It’s the new currency, and that’s an area that’s not going to change. There’s only going to be more data — more things to build up from.”

With that mindset, early movers are translating principles into shipped products rather than proofs of concept. They’re targeting multilingual search and near-term marketing lift as first wins, then codifying those gains into reusable agent frameworks that scale across markets, according to Nayar.

“[A client] was trying to create an e-commerce platform where they were rethinking their digital search capability,” he said. “It’s a unique sort of problem because they’re in so many different markets, and there are so many languages and regions and all these considerations. Google and Tiger partnered to first envision this solution and we started building out these solutions using Google Cloud capabilities and Vertex AI to basically solve the search problem … We actually helped to create an agentic platform for them where businesses could come in and define their own agents.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Google Cloud Partner AI Series event: 

(* Disclosure: Google LLC sponsored this segment of theCUBE. Neither Google nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE