How our agents build on-brand pages with design.md
Vercel found that prose alone produced inconsistent agent-made pages, then paired design.md with fixed CSS primitives and repeatable evals to encode brand judgment.
Vercel built **design.md** as a public guidance file, paired it with a public stylesheet and an evaluation loop. It tests **seven fixed scenarios** across Claude Opus 4.8 and Codex with GPT-5.5.
For agent-built interfaces, encode reader intent and composition rules in context, move mechanical choices into reusable CSS, and rerun frozen prompts after every guidance change. Keep human review for hierarchy and deterministic checks for layout failures.
Vercel built **design.md** as a public guidance file, paired it with a public stylesheet and an evaluation loop. It tests **seven fixed scenarios** across Claude Opus 4.8 and Codex with GPT-5.5. For agent-built interfaces, encode reader intent and composition rules in context, move mechanical choices into reusable CSS, and rerun frozen prompts after every guidance change. Keep human review for hierarchy and deterministic checks for layout failures. The reported comparison used one generation per condition with no rerolls. It shows that guidance changed one page's structure, but does not establish general reliability across models, prompts, or brands.
This makes shared design guidance more concrete: semantic composition rules belong in agent-readable context, mechanical decisions in reusable CSS, and changes behind frozen prompts plus human and deterministic review. It supports harness engineering over ad hoc prompting, while the seven-scenario, single-sample evaluation sharply limits any claim that the method generalizes across brands, prompts, or models.