Design systems fed to agents are unverified by default. You can document every token, component rule, and spacing constraint you own, and still have no mechanism to confirm the agent is applying them correctly. That is the core problem this piece identifies.
The argument is not abstract. Real teams are shipping agent-assisted UI and discovering drift only after review, or not at all. Evals, the same evaluation frameworks used to test LLM outputs in other domains, are the proposed fix. The piece makes the case that design systems require the same rigor.
What makes this worth reading is not the conclusion but the framing: treating design system compliance as a measurable, testable property rather than a prompt engineering hope. If you work at the intersection of design tooling and AI, the methodology implied here is the thing to extract.
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