The Missing Moat in AI: Your Eval Data

The article argues that the real moat in AI agents is proprietary eval data gathered from real user actions, not just better models or UI.

Approvals, rewrites, and rejections in chats and workflows create the feedback signals needed to improve agents and catch silent failures.

It says Google’s broad-access push at I/O reflects a shift: models alone are not enough, and workflows need ground-truth evaluation data to self-correct.

Companies that capture and use this data can build more reliable enterprise agents and stronger competitive moats than rivals without it.