ai-lab.md · build: passing ✓ · authored by kunlin × claude
Exploring Agentic AI in Product Design
Field notes from both sides of the loop — shipping an enterprise AI agent at work, and vibe-coding my own tools after hours. Different scales, same method: a living spec, an AI that executes, a designer who owns the judgment.
deliverable
craft
handoff
the edge
AI didn't shrink the design job. It moved it upstream — from drawing every screen to defining what "right" means.
// the loop — one method, two scales
⟲ the last step feeds the first — the spec never goes stale
01 / enterprise scale
NIA — designing an AI agent inside a $1.4B B2B platform
🚀 The method, anonymized: every feature starts as a markdown spec merged from vendor docs and raw user data — one file that stays alive from research to launch. In the design phase, AI drafts ~80% of the Figma first-pass through MCP; my job is the 20% that decides if it ships.
fig.01 — nia, persistent sidebar agent · shipped to production
// workflow — the spec is the product
// what made it work
The spec never went stale. Docs usually die after kickoff — this one ate every decision we made and briefed the AI better each week. The designer's real deliverable was the document, not the screens.
02 / personal scale
Vibe Coding — I don't write code. I write the rules AI follows.
🚀 Two working tools: a Claude skill that tailors my resume without breaking a pixel-locked layout, and a live job-search dashboard in Next.js. Same loop as the enterprise one — plus a harness: automated checks every build must pass before I even look.
Job Tracker ● Live
127
applications tracked · Next.js
fig.02 — one build cycle: red, patch the rule, green, ship
// workflow — two loops, and the harness only grows
// the unlock
Two loops. The harness catches everything measurable — and it only grows: each failure I catch by eye becomes a new automated check. The agent I work with next month is strictly better than this month's. Continuous learning, designed — not hoped for.