Design

Communicating and implementing complex interactions with AI agents using visual logic

Use an AI agent to help implement nuanced UI interactions by supplying structured inputs (reference URL, screen recording, target element code, hypothesized trigger logic) and having the agent compare implementation approaches (e.g., Intersection Observer vs scroll listeners), then iterating toward high-fidelity behavior.

Why the human is still essential here

The designer must define and evaluate interaction quality, provide visual/behavioral hypotheses, and approve fidelity; the agent helps translate that intent into working implementations and plans, but can drift without human correction.

How people use this

Scroll-triggered animation implementation

AI translates a reference interaction into code by proposing trigger logic and generating an animation implementation that matches timing and easing targets.

Claude Code / Framer Motion

Intersection Observer vs scroll listener comparison

Given the target element and behavior constraints, the agent evaluates approaches, flags performance pitfalls, and produces a recommended implementation with fallbacks.

ChatGPT / GitHub Copilot

Interaction spec from recording

AI extracts a step-by-step behavior spec (states, thresholds, durations) from a screen recording and turns it into acceptance criteria for implementation review.

ChatGPT (Vision) / Claude

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Expert UI designer specializing in visual design systems, component libraries, and pixel-perfect interface creation. Creates beautiful, consistent, accessible user interfaces that enhance UX and reflect brand identity

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You are UI Designer, an expert user interface designer who creates beautiful, consistent, and accessible user interfaces. You specialize in visual design systems,

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You are UX Researcher, an expert user experience researcher who specializes in understanding user behavior, validating design decisions, and providing actionable

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Community stories (1)

Medium

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

What two months of working inside Claude Code taught me about design systems, technical negotiation, and the future of IC work

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?

Kermit YenProduct Designer

Mar 5, 2026