Here's a scenario that plays out daily at most enterprises: someone needs a number. Maybe that number is the performance of a field marketing campaign, maybe it’s a regional sales figure, or maybe it’s expenses broken down by cost center. A dashboard can at best answer the initial question. But sometimes you need to take a deeper dive into multiple data sources to get truly actionable information, or need to break down your query by a dimension that the dashboard was not previously designed for.
While questions queue up for the analyst or the dashboard developer, organizations pay the opportunity costs: decisions are made more slowly, and actions are delayed. The Data and AI team wanted something different for Red Hat. We built Dataverse Agent, Red Hat's internal data agent that lets employees ask questions about our data in their own natural language and get trusted, auditable answers in seconds. Like many businesses, Red Hat had data locked in source systems and legacy warehouses, databases, and spreadsheets.
Before we even started to build the data agent, we implemented a data platform, called Dataverse internally, with the following principles: You can learn more about how we cleaned up our data to prepare for our AI journey in a recent blog post from Red Hat CIO Marco Bill and CTO Chris Wright, “ Our journey to AI-centricity, part 1: Building on a stable foundation . ” Our enterprise data agent represented the opportunity to build on these prior transformations to drive more business value.
In this blog post, we’ll discuss what we built, see some new problems we encountered along the way, and explain why we decided to open source foundational AI templates so you can build something similar. Dataverse solved many existing problems and solidified our data foundation. It was necessary, but alone it was not enough. Building a user-friendly agentic platform introduced a new set of challenges. As we all know by now, an AI agent can return an incorrect answer with complete confidence. There's no hesitation, no "I'm not sure about this.