AI agents may be new, but the data problems they encounter are not. Before an agent can answer a question or take action, it needs to know what data exists, what it means, and how trustworthy it is. A single errant join, stale table, ambiguous definition, or missing access rule can quietly turn an otherwise capable agent into a confident source of bad decisions. And when agents string multiple steps together, even strong accuracy rates can deteriorate quickly.
The good news? Much of the infrastructure needed to solve this problem already exists, if organizations rethink how data is collected, understood, and governed. Instead of repeatedly moving data through pipelines and warehouses, one creative solution uses an immutable, replayable fact log, continuously profiling data as it arrives and capturing schema changes, lineage, transformations, distributions, and other signals along the way.
Tune into this DM Radio episode as host Eric Kavanagh talks with Mike Kowalchik of Matterbeam about why infrastructure originally designed to make data easier for humans may prove even more valuable for AI agents, and how evidence-backed inference, agent-specific governance, and reusable data context can help turn promising prototypes into trustworthy production systems.
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