Teaching agents product design at Vercel
Vercel just solved the hardest part of coding agents: teaching them *why* decisions exist, not just what shipped.
Why it matters
As AI agents move into product development workflows, the bottleneck shifts from speed to judgment. Vercel's product-design skill system shows how to embed product context, design reasoning, and standards into repositories so agents can make decisions that require human judgment—not just copy patterns from code.
The key facts
8 to knowVercel treats agents as native to their workflow, with product decisions stored in repository alongside code
Three-part system: skill routing, traceable findings, and evidence-intake workflow
Separates deterministic checks (linters) from judgment-based agent guidance
Tests agent behavior on unseen interfaces using evals with shipped examples and holdouts
Weekly evidence-intake workflow collects design feedback from Slack, Figma, GitHub for skill updates
Lint rules encode product guidelines as deterministic checks; agent guidance handles decisions requiring context
Coverage-gap list makes missing guidance explicit for continuous improvement
Stable rule IDs link to examples and sources; human approval required before changing guidance
Go to the source
Vercel Blogvercel.com
Publisher excerpt: Coding agents can produce working UI fast, but what's harder is a different shape. They can copy your product's style, match its patterns, and try to follow its conventions. What they cannot do is understand why those patterns exist. Code shows agents what shipped, not why one component, phrase, or…