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Operational intelligence provides enterprises with a promising advantage: enhanced data quality that enables faster decision-making. But as artificial intelligence-powered agents become more integrated into the workforce, that same autonomy introduces new challenges around governance, accountability and change control. Early AI adoption has brought significant challenges. Many agents impress in isolation, yet turning them into dependable, scalable operations remains elusive. Addressing this value gap is essential for the enterprises looking to successfully blaze the trail, according to Shailesh Manjrekar (pictured), chief AI and marketing officer of Fabrix.ai Inc. “We are really at an inflection point with agentic, right? Every enterprise, small or large, wants to be an autonomous enterprise,” Manjrekar said. “However, what we are seeing is there is what I call an AI value gap. There are a lot of these AI projects happening, but really how much they contribute to … an outcome is [what] enterprises are struggling with.” Manjrekar spoke with Bob Laliberte, principal analyst at theCUBE Research, at the “Agentic AI Unleashed: The Future of Digital & IT Operations” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed ways to close the AI value gap, what an agentic operational intelligence platform looks like in practice and the enterprise guardrails needed to move from experiments to outcomes. (* Disclosure below.) How operational intelligence serves as the connective tissue Enterprises don’t just want more dashboards — they want actionability that doesn’t compromise on governance. That means unifying data, models and runbooks so agents can respond to alerts, telemetry and tickets, then capture what they did, why they did it and what happened next. In short, the platform should operate as a fabric, not a feature, Manjrekar explained. “Fabrix.ai is, essentially … the inventor of the data fabric to begin with, and now the agentic operational intelligence platform,” he said. “What I mean by that is: think of us as a connective fabric.” Of course, discipline is essential for any such connective fabric. When it comes to agentic AI, guardrails and observability make their actions auditable, especially at the foundational level. The focus shifts from product quantity to workforce capacity by adding operations agents, then measuring improvements in cost, risk and speed across information technology operations, Manjrekar noted. To make that shift real, the platform itself has to be built for the enterprise — large language models are just one component in a larger, governed stack. “An LLM is a means to an end. There have to be several building blocks around LLMs, which actually makes it enterprise-ready,” Manjrekar said. “We’ll be dwelling about it throughout our presentation today, and hopefully towards the end of the segment, the audience will walk away with the enterprise capabilities, what we have in terms of guardrails — in terms of a dynamic [Model Context Protocol] server.” Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the “Agentic AI Unleashed: The Future of Digital & IT Operations” event: (* Disclosure: TheCUBE is a paid media partner for the “Agentic AI Unleashed: The Future of Digital & IT Operations” event. Neither Fabrix.ai, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.) Photo: SiliconANGLE
