Machine-Readable Design System

Value hypothesis

Structuring a design system, and all it's parts, in such a way that AI agents can read, query, and generate from it reliably. It allows downstream workflows to operate using a product's actual components, rather than training data, included grounded generation, automated documentation, and a both preprod and production auditing.

Quality · Innovation

The design system team translates existing human-readable documentation into a machine-readable format, and updates the design token architecture so it is semantically rich enough to assure consuming LLMs use it correctly. The work has three layers: 1) structured component metadata that encodes usage patterns, anti-patterns, selection criteria, and composition rules for each component; 2) a codebase index that maps the full relationship graph - what components exist, what uses what, how deep the dependency chains run; and 3) query protocols that teach agents how to traverse the architecture and reason over cached data rather than re-exploring on every interaction. Together these layers form a retrieval pipeline for design systems.