AI agents may be new, but the data problems they face are not. Before an agent can answer a question or take action, it needs to understand what data exists, what it means, and whether it can be trusted.

A single errant join, stale table, ambiguous definition, or missing access rule can turn a capable agent into a confident source of bad decisions. And as agents chain multiple steps together, small errors can quickly compound.

The good news? Much of the infrastructure needed to solve these challenges already exists - if organizations rethink how data is collected, understood, and governed.

Join this episode of DM Radio as Host Eric Kavanagh speaks with Mike Kowalchik of Matterbeam about an approach built around an immutable, replayable fact log that continuously profiles incoming data while capturing schema changes, lineage, transformations, distributions, and other signals.