UPDATED 16:15 EDT / JUNE 25 2025

Rob Strechay and Paul Nashawaty, principal analysts at theCUBE Research talk about agentic workflows at the AI Agent Builder Summit 2025. AI

Trust and transformation: TheCUBE Research’s take on agentic workflows in enterprise AI

Enterprise AI is entering a new phase, where intelligent systems are built to collaborate, make decisions and achieve goals — not just generate content — with agentic workflows driving this transformation behind the scenes.

These advanced workflows represent more than a technical upgrade. They signal a shift in how businesses structure automation, combining small language models, real-time data and human-in-the-loop design to create adaptive systems that deliver tangible outcomes. With trust, accountability and domain expertise now front and center, organizations are moving beyond experimentation toward scaled, production-ready AI strategies, according to Rob Strechay (pictured, left), managing director and principal analysts at theCUBE Research.

Rob Strechay, principal analyst at theCUBE Research, talks about agentic workflows at the AI Agent Builder Summit 2025.

TheCUBE Research’s Rob Strechay talks about agentic workflows.

“It’s not just about gen AI; it’s how do you bring together chain of AI and chain of thought and things of that nature or mixtures of experts and all of the different technologies that go into that,” Strechay said. “As I look at it even down the stack from that toward the data platforms, there’s a lot of pieces that have to go into the data wrangling.”

Strechay and Paul Nashawaty (right), principal analyst at theCUBE Research, spoke with theCUBE’s Scott Hebner as a part of the analyst kickoff at the AI Agent Builder Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how agentic workflows are reshaping enterprise AI by enabling goal-oriented, collaborative systems that require trust, domain-specific models and cultural alignment to move from experimentation to real-world impact. (* Disclosure below.)

Why agentic workflows require trust, maturity and ecosystem thinking

Building effective agentic workflows requires more than just advanced models — it calls for alignment across application development, data infrastructure and organizational culture. Many companies are moving beyond the proof-of-concept stage, but unlocking value depends on connecting discrete systems into cohesive chains of intelligence, according to Strechay.

“I think people can sit back today, watch this and watch all of the people we have on, talk to how do you succeed with this? How do you get out of death by POC? And I think that’s a big key to today,” he said.

One recurring theme is the growing role of domain-specific models. These enable AI agents to act within nuanced contexts, drawing on fine-tuned knowledge to generate meaningful outcomes. While generative AI is a gateway, real business transformation happens when agents are purpose-built for specific industries and integrated into complex workflows, according to Hebner.

“Our research shows more than half of businesses plan to actually acquire pre-built agents, customize them and then integrate them with their own proprietary agents that they build,” Hebner said. “You’re going to need a platform to do that.”

Still, technical readiness alone isn’t enough. Maturity varies across enterprises — some are just beginning, while others are actively building and deploying agents, the analysts emphasized. Organizational leaders must foster trust in these systems, particularly as they move into decision-critical functions, Hebner explained.

“I think it’s around half actually trust the outcomes that they’re getting from their AI when they’re making decisions,” he said. “With agents now promising to make business critical decisions and even act autonomously, the issue of trust would become even more pronounced. If you think about it, our business is really going to just blindly trust AI agents? I seriously doubt it, and that’s why I’ve hardened my view that trust becomes the currency of innovation as we go forward here.”

The cultural shift required to support agentic workflows is profound. Developers, business users and leadership alike must embrace the shared responsibility of designing systems that are accountable, explainable and reliable. As AI’s role in decision-making expands, so too must the understanding of governance and ethical deployment, Nashawaty said.

“Compliance, regulations and governance are also key because one of the things that we’re also seeing is, with the proliferation of applications, the citizen developer is taking on more of a role in development,” he added. “AI is being used more and more at the lines of businesses, therefore the citizen developer needs to have those governance and controls in place as well.”

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

(* Disclosure: TheCUBE is a paid media partner for the AI Agent Builder Summit. The sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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