AI-Ready Design System
Making a design system machine-readable for AI-supported work
As part of my work at ELCA Informatics, I audited the design token foundation behind a complex design system: mature Android, iOS, and global token libraries already in active use. Android went through the audit first and set the pattern the other two libraries were then measured against. The libraries had grown in ways that made them hard to verify with confidence. Token scopes were broad or inconsistent, descriptions were incomplete or missing, naming had small inconsistencies, and changelog structures differed from library to library. For a human designer, most of that was manageable. For an AI-supported workflow, it was a blocker. An AI agent can only reason reliably about a design system when the system exposes its intent clearly, through names, scopes, modes, descriptions, and documented decisions.





