Is your data infrastructure built to feed autonomous AI, or will it be the reason your agentic projects get cancelled? Download the whitepaper to find out whether your infrastructure can keep up.
Agentic AI has stopped being a research category. By the end of 2026, Gartner expects 40% of enterprise applications to embed task-specific AI agents, up from less than 5% the year before. Roughly four in five enterprises have agents in production somewhere in the business, even if leadership cannot always say where. The boards have approved budgets. The pilots are running. The agents are out there, making decisions.
And yet Gartner also forecasts that more than 40% of agentic AI projects will be cancelled by the end of 2027. The framing in most boardroom conversations puts that failure at the door of the agent itself, or the model, or the use case. Twenty years of building data infrastructure for production AI workloads tells a different story. Agents do not fail because the model is weak. They fail because the data layer underneath them was built for a different consumer of data, and nobody noticed until the agent had been operating on stale, ungoverned, or incomplete context for long enough to do real damage.
