Amazon Web Services Inc. is accelerating the shift from artificial intelligence pilots to production with the launch of AWS Bedrock AgentCore, a new runtime built to deploy AI agents at scale with memory, orchestration and enterprise-grade security.

As enterprises move past proof-of-concept phases, the need for stronger agent infrastructure is becoming urgent. AgentCore is designed to meet that demand, enabling developers to move faster while maintaining control over data, access and trust boundaries. By embedding memory and tool-use capabilities into a managed runtime, AgentCore gives teams the framework they need to operationalize AI agents across complex workflows — without sacrificing governance or performance, according to Ben Schreiner (pictured), head of AI and modern data strategy at AWS.

AWS’ Ben Schreiner talks to theCUBE’s John Furrier about Bedrock AgentCore.

“Big news with AgentCore coming out and really demonstrating our innovation ahead of the problems you could foresee that agents could create if they weren’t governed, if they weren’t observed, if they didn’t have a secure runtime,” Schreiner said. “You can really see the innovation coming out of our engineering teams to help enterprises not only develop these agents, but also make sure that they’re deploying them in a secure, reliable way.”

Schreiner spoke with theCUBE’s John Furrier at AWS Summit NYC, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AWS Bedrock AgentCore is enabling enterprises to move AI agents from pilot projects to scalable, secure production environments while also reshaping development practices and data strategies to support real-world deployment.

How AgentCore is reshaping the enterprise AI stack

AgentCore arrives as companies reimagine how AI agents fit into their business logic and workflows. Rather than being a one-size-fits-all solution, AWS emphasizes flexibility and alignment with actual customer needs, driven by its “working backwards” development ethos, according to Schreiner.

“That’s at the root of how we approach all customers; they’re all unique and there may be some themes and trends that transcend customer segments or industries,” he said. “We want to take each customer’s challenge and what they’re trying to do and work backwards from that.”

That mindset extends to the agent development lifecycle. Early enthusiasm for “vibe coding” — where non-technical users sketch out intent — is now meeting enterprise rigor. AWS’ Kiro platform helps bridge this gap, enabling better collaboration between business users and technical teams to get agents from prototype to production, Schreiner explained.

“Kiro brings those two things together for the first time, where you’re seeing the requirements and the documentation and all the things that need to go into creating a production-ready solution,” he said. “So many customers got stuck in POC land and it’s unfortunate, but if you don’t get into production, then you didn’t really solve the problem that you had identified … production and scale is the goal, and we drive toward that with our customers.”

Another critical factor in deploying agents at scale is data readiness. Organizations are rediscovering the importance of strong, well-governed datasets as they realize that AI effectiveness is tightly coupled to data quality. That’s prompting many to rethink legacy architectures in favor of more modern, flexible designs.

“We need executives to understand that the machines and the agents that you create are only as good as the data they have access to,” Schreiner added. “If you want good answers from your agentic workflows or anything you do with AI, then you’ve got to make sure the data it has access to is strong. That gets to governance, it gets to security, it gets to all the things that we’ve been professing for decades now.”

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

Photo: SiliconANGLE