Why AI Data Agents Are Untrusted: The Need for Provenance and Visible Calculations
hugobowne · x · 2026-09-02
Article critiques current AI data agent products that deliver results (e.g., "$4.21M net revenue") without exposing metric definitions, source queries, assumptions, or unverified content, forcing users to redo the analysis.
Key Points:
- This is a product design problem, not just a model capability issue.
- Design for Verification: AI products should expose provenance, visible calculations, trusted starting points, diffs, contradictions, and smaller inspectable units.
- Connection to Eval: "Hard to eval" is often a product smell; designing for verification generates stronger eval data.
The post announces a live discussion with Hamel Husain to explore how transparency can solve trust issues in AI outputs.
Related event: Unverifiable AI Data Agents Are a Product Design Flaw, Expert Says(4 posts)→
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