Agentic engineering is redefining intelligent systems by replacing static automation with autonomous, goal-driven artificial intelligence agents capable of reasoning, acting and collaborating dynamically.

Kubiya Inc. harnesses agentic engineering to optimize on-demand capacity and artificial intelligence-driven project planning, enabling efficient resource management. By integrating with Kubernetes, OpenShift and other orchestrators, the platform dynamically scales infrastructure to meet agent or business demands, according to Amit Eyal Govrin (pictured), co-founder and chief executive officer of Kubiya.

Kubiya’s Amit Eyal Govrin talks with theCUBE about the pivotal role of agentic engineering in the AI era.

“Let’s assume that the same VP of engineering now had Kubiya as an example, and could go and prompt the task in simple English, ‘Scan my entire AWS bill for the last 30 days and find underutilized resources for the last 30 days that are candidates for retirement and terminate those,’” Govrin said. “Immediately, there’s going to be an agentic project planner. We’re going to use a powerful model, of course, a thinking model. It’s going to go and plan out the entire [product requirements document]. ‘Here are the agentic teammates or the agentic engineers that are required.’”

Govrin spoke with theCUBE’s Rob Strechay at the KubeCon + CloudNativeCon NA event, during an exclusive on-the-ground broadcast from theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the significance of agentic engineering and how Kubiya is pioneering its adoption. (* Disclosure below.)

From key performance indicators to execution: Kubiya’s agentic engineering platform

Kubiya, an on-demand agentic engineering organization, leverages AI-powered agents to transform business key performance indicators into actionable engineering outcomes. By treating engineering as a service, the platform enables organizations to dynamically scale capabilities, execute tasks and manage resources efficiently — without increasing headcount, according to Govrin.

“You may want to have compliance involved, you may want to have the platform engineer and a FinOps team involved, and all of those are costed out,” he said. “Now, you know exactly how long it’s going to take this agentic engineering org to perform this end to end. It’s going to know how much it’s going to cost you in terms of token consumption. It’s a Kanban board that starts getting populated with tasks, and these tasks get solved with agents with the human in the loop.”

Platform engineering is the backbone of AI deployment, providing the infrastructure, tools and workflows that enable models to move seamlessly from research to production. It ensures AI systems operate reliably, scale efficiently, maintain security and deliver actionable insights in real-world environments, according to Govrin 

“We really originated from platform engineering,” he said. “We understood that platform engineering should not be treated as its own unique segment. It should be treated as a necessary part of the process of deploying AI in production, and I think platform engineers often like to over-engineer and over-complex because everybody wants their own flavor, just like they did with the backstage approach. Everybody wants their own [identity providers].”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the KubeCon + CloudNativeCon NA event:

(* Disclosure: TheCUBE is a paid media partner for the KubeCon + CloudNativeCon NA event. Neither Red Hat Inc., the primary sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE