A Unified Data Plane For Agentic AI

Ask an AI agent a question it cannot answer, and it rarely admits it. Instead, it guesses, confidently, in complete sentences, and the answer often sounds right even when it is not. Enterprises call this “AI hallucination”. But the real failure usually happened earlier, when the agent went looking for an answer and could not find the right data, or found three versions of it that did not agree.

That gap gets more expensive as AI moves from a single chatbot to a team of agents working a task together. A chatbot answers one question and stops. An agent reads a ticket, retrieves a document, queries a database, calls a tool, maybe hands off to a second agent, and only then responds. Every one of those steps depends on data the agent can actually find, trust and act on. One weak link anywhere in that chain, and the error travels downstream with it. Now we are all hallucinating.

Agent Infrastructure Is Not Just Compute

Most conversations about scaling enterprise AI start with GPUs: more of them, faster ones, bigger models behind them. All useful, but none of it answers the harder question: how does an agent know which data is current, who is allowed to see it, what a given field actually means, and where an answer came from if someone needs to check it later? That is the data plane, and it is turning into its own category of infrastructure, as foundational to agentic AI as a network is to the internet.

One Meaning, Everywhere

The stakes rise after more than one agent touches the same information. A support agent, a finance agent and an inventory agent might all pull from the same customer record, but if one system calls it a “client” and another calls it an “account,” two confident agents can reach two different conclusions from the same underlying fact. Scale that across dozens of agents built on different tools by different teams, and disagreement becomes the default, not the exception.