SAS Institute Inc. is set to play a significant role at theCUBE Research’s AI Agent Builder Summit on April 16, highlighting its comprehensive approach to deploying agentic AI technologies.

AI agents promise revolutionary changes to enterprise decision-making by offering autonomous capabilities that far exceed conventional automation. In a recent industry interview, the focus centered on clearly defining agentic AI, its value for businesses and, crucially, how to implement these advanced technologies effectively and safely within existing corporate structures. SAS, a longstanding leader in analytics and business intelligence, is positioning itself to provide robust frameworks for governance, trust, and the practical deployment of AI agents.

“Agents are really about autonomy, smarter decision-making, and the ability to make nested decisions, while we have the ability or we feel we need the ability to monitor those so we’re not led down the wrong path,” said Shadi Shahin (pictured, right), vice president of product strategy at SAS Institute Inc.

I recently spoke with Shahin prior to the AI Agent Builder Summit. We discussed the importance of clarity and trust in adopting AI agents. (* Disclosure below.)

Deploying agentic AI for strategic advantage

Businesses looking to deploy AI agents should start by clearly defining their objectives, assessing their risk tolerance and ensuring rigorous monitoring and governance systems are in place, according to Shahin. He recommends starting small, choosing manageable, measurable applications where incremental wins can build confidence and demonstrate clear ROI.

“Find a place to get started where you know you can monitor and measure the value of that agent, and then don’t try to solve it for a hundred agents — solve it for one, and then iterate and continue,” Shahin advised.

He emphasized the essential role that data governance and transparency play in ensuring successful deployment. As synthetic data becomes more widespread, enterprises must implement strict oversight to prevent data inaccuracies from compromising agent reliability.

“Synthetic data is here, and that’s good for businesses to be able to create better models, but it also introduces the risk of erroneous data or bad data because bad actors are out there,” Shahin added. “Meaning that the data we have is the data we need to trust, and you need to apply rigor there to make sure you understand what you have at your disposal.”

Here’s theCUBE’s complete video interview with Shadi Shahin:

Don’t miss theCUBE Research’s exclusive AI Agent Builder Summit on April 16.

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

Photo: SiliconANGLE