Design

Communicating and implementing complex interactions with AI agents using visual logic

Use reference URLs, screen recordings, inspected element code, and annotated Figma diagrams (plus planning steps like ‘Plan Mode’) to help an AI agent implement high-fidelity interaction effects while minimizing translation loss from design intent to shipped behavior.

Why the human is still essential here

The designer observes, hypothesizes mechanisms, sets quality thresholds, and reviews plans/iterations; the agent handles research and code experimentation, but intent and acceptance remain human-led.

How people use this

Community stories (1)

Medium
9 min read

Building a Scalable Product System as a Solo Designer — With AI Agents

As we entered 2026, my weekly feed has been saturated with forecasts from top-tier practitioners regarding the future of work. These predictions trigger a unique blend of anxiety and excitement — anxiety from the fear of falling behind, but excitement because the barriers to building products that once required an entire team are being systematically lowered.

Based on the insights I’ve gathered, I believe the ‘New IC’ (Individual Contributor) is on the rise. These are not passive freelancers forced into independence by downsizing; they are professionals who actively choose to oversee the entire lifecycle of planning, research, design, and delivery. Their scarcity stems from their T-shaped knowledge structure:


| A deep, specialized core, a broad, cross-disciplinary vision, and crucially, the ability to translate their expertise into a language that AI Agents can execute.


This article is a documentation of my journey over the past two months — a transition from ‘using AI tools to speed up tasks’ to ‘co-architecting systems with AI Agents.’ It all boils down to one fundamental question:


| Can a designer, empowered by AI Agents, independently build a maintainable and scalable product system?


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KY
Kermit YenDigital Product Designer
Mar 6, 2026