Section 156 · Chapter 21, Predictions for the Tokenized Product Future
Quality as a Horizontal Layer
The endgame is not every model team testing itself. The endgame is an independent quality layer that works across models, platforms, apps, and agents.
What to do
- Define runnable checks that exercise integration and quality horizontal layer.
- Set acceptable outcomes and blocker failures for integration and quality horizontal layer before running the evaluation.
- Run representative cases for integration and quality horizontal layer and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for integration, quality horizontal layer needed to reproduce work on Quality as a Horizontal Layer.
- Report results for integration, quality horizontal layer by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, horizontal AI quality should define platform-independent eval contracts, cross-vendor trace schemas, model-agnostic rubrics, independent judge calibration, portable regression suites, and governance rules that separate generation from validation. The evaluator must be able to compare systems across vendors and workflows, not merely certify one model in isolation.
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Cite this page
Jason Arbon. "Quality as a Horizontal Layer." Testing AI Knowledge Edition, section 156.
https://jarbon.ai/testing-ai/knowledge/ch156-quality-horizontal-layer.html