The AI arms race is accelerating at breakneck speeds. With autonomous AI agents as the current innovation frontier, the progress bar has shifted to equipping them to understand the “why” of a problem, rather than simply the “what.”

Next-gen systems will exercise decision making with insight, precision and purpose.

“In terms of agentic AI, I think it’s been a buzzword now that’s been thrown around quite a bit over the past 12 to 18 months,” said Michael Garas (pictured, right), AI partnerships leader at IBM Corp. “From an enterprise customer perspective, we’ve seen individuals believe that agentic AI can do everything under the sun, all the way to the complete skeptics that say agentic AI just isn’t where it needs to be today. I think the top-of-mind concern for enterprises is how to make sure AI agents have the context that they need.”

Garas and Stuart Frost (left), founder and chief executive officer of Geminos Software, 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 the deep mechanics of next-generation AI agents, outlining how true AI decision intelligence is reshaping enterprise strategy, underpinned by causal AI and real-world scalability. (* Disclosure below.)

Understanding causality will unlock smarter AI agents

The concept of causal AI is the missing link for truly intelligent decision-making. Unlike LLMs, which recognize patterns, causal AI can understand relationships between actions and consequences. This allows systems not just to describe what is happening, but to suggest what to do next and why, according to Frost.

“What we’re finding is that we need to underpin all of this with causal AI, which really gives you, for the first time, the ability to make truly data-driven decisions and put them in the context of the overall enterprise challenge,” he said. “What we’re doing is underpinning the agents and the LLMs with that causal information so that they can help us make some of the easier decisions. Not all decisions are complex, but we do need that foundation.”

At the core of this capability are causal knowledge graphs, an advancement over traditional knowledge graphs. While conventional graphs map static entities and relationships (e.g., organizational hierarchies, systems and roles), causal knowledge graphs incorporate dynamics — how things change over time and what causes those changes, Frost explained.

Geminos has been working closely with IBM to refine the interpretation of causality in AI agents and, subsequently, their decision-making capabilities. Leveraging the watsonx framework has also brought governance and scalability, Frost added.

“Closing that loop is incredibly powerful, and it doesn’t require the old chore of building ontologies for every industry,” he said. “We can actually do it much quicker and more effectively using agentic AI to bring in subject matter experts and do curation and so on. That’s an area that we’re working very closely with IBM, using their orchestration tools, agentic orchestration tools, and also their LLMs like Granite. We’re very open on that. We do use other models, but we’re finding that the IBM ones are very effective.”

IBM’s models serve as the “fast brain” for quick insights, while Geminos’ Causeway platform provides the “slow brain” — a deeper layer of reasoning rooted in causal AI. By marrying these layers, enterprises can create a feedback loop where LLMs extract knowledge from documents and logs, which is then fed into causal models, according to Garas.

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