Enterprise automation is entering a new era as agentic artificial intelligence systems evolve from rigid workflows into adaptive, intelligence-driven architectures.

Rather than relying on static rules and siloed tools, organizations are now leveraging AI agents that coordinate across platforms, respond in real time and continuously fine-tune operations based on live data. This shift marks a fundamental change in how enterprises approach automation — moving from task execution to dynamic, end-to-end orchestration that flexes with the needs of the business, according to Jesse Shiah (pictured), co-founder and chief executive officer of AgilePoint Inc.

AgilePoint’s Jesse Shiah talks to theCUBE about agentic AI and enterprise automation.

“It is very similar to what we talk about agentic today,” Shiah said. “In a way, in order to make sure that even you make the change to the system, you still have the trust, you still have the confidence. How can you do that without involving changing code?”

Shiah spoke with theCUBE’s Scott Hebner at the AI Agent Builder Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how agentic AI is reshaping enterprise automation by enabling adaptive, cross-platform orchestration through composable architectures and intelligent agents. (* Disclosure below.)

Agentic AI redefining enterprise automation architecture

The evolution of agentic AI is enabling a radical shift in how companies orchestrate and govern digital operations. Businesses are no longer limited to static workflows tied to a single vendor ecosystem. Instead, they are adopting abstracted, composable frameworks that can integrate agents from various platforms and execute decisions across multiple systems simultaneously, according to Shiah.

“It’s not specific to one applications or one functional area because … if you take the 80/20 rule loosely, about 20% of the applications are actually that cross-functional end-to-end and the rest of 80% maybe a lot of application for functional specific and tied to different platforms,” he explained. “But the 20% of the end-to-end actually drives probably 80% or more of the business outcomes.”

This shift requires a new kind of architecture, one that emphasizes holistic abstraction across both the AI agent layer and the business application layer. AgilePoint’s approach enables real-time system adaptation and orchestration without the need for code generation or modification. This empowers business users to make critical adjustments to workflows without needing to rely on IT for every change.

“That means that the application itself, your automation, your orchestration has to build in a way to enable agents to change them, change the logic, but without involving code changes,” Shiah explained. “That’s what we mean by holistic abstraction.”

Equally important is the role of governance and explainability. As enterprises look to integrate third-party agents, maintaining control and visibility becomes essential. The amplifies the significance of AgilePoint’s AI Control Tower — a framework that allows organizations to audit agent decisions, retrain models with real data and contain risk, Shiah emphasized.

”What that means is you can operationalize AI agents without physically changing the business process,” he said. “That provides a huge benefit. Businesses don’t have to understand AI for you to operationalize or adopt AI. The AI developers … they do not have to understand operations. The AI control tower brings them together. That’s where you operationalize, you govern, but you also democratize AI.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the AI Agent Builder Summit:

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

Photo: SiliconANGLE