AI adoption starts with truth

Jon Eide and Aadil Hussaini on why Replit’s internal data agent got used, which they put down to trust rather than capability:

AI adoption is limited by trust. A user who gets burned by a confidently wrong answer will double-check the next one.

Their fix is an operational-truth layer: a version-controlled repository of business definitions, canonical metrics, source relationships, validation rules and documented past failures, which grounds every internal agent.

The mechanism I find persuasive is that corrections arrive as pull requests. When someone spots a wrong answer, the fix is diffable, revertible and attributable, it goes through human review, and it then propagates to every downstream agent rather than living in one person’s head. They are explicit that the review gate deliberately slows things down. Agents also validate against the layer before making a claim. Their data agent now handles over 1,000 warehouse-backed questions a week, grown by word of mouth.

The framing worth keeping: “a semantic layer is not the product; it is the shared contract that lets a company safely add a system of specialized capabilities.” The point is that one reviewed corpus makes improvements compound across agents instead of errors compounding.