Design

Researching design system inputs with AI agents

AI agents gather and cross-reference market reports, competitor analysis, and technical papers to inform design system decisions with cited sources and confidence levels.

Why the human is still essential here

Jordan defines the problem, reviews the evidence, and makes the final creative and strategic decisions about the design system.

How people use this

Competitor UI pattern scan

AI searches live websites and source-backed summaries to compare how competing products use navigation, cards, forms, and layout conventions before the team defines its own system.

Perplexity / ChatGPT

Design trend synthesis

AI compiles market reports, platform guidelines, and technical articles into a concise brief on current typography, color, and interaction trends relevant to the product category.

Perplexity / Claude

Accessibility standards digest

AI reviews WCAG guidance and implementation references to produce a practical checklist for contrast, hierarchy, spacing, and component behavior in the design system.

ChatGPT / Claude

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I built a full design system with AI agents as my team.

I built a full design system with AI agents as my team. At VibeSell we needed a design system. Brand colors, typography, surface hierarchy, component patterns. As a two person team with no designer, I was hitting a wall trying to do the research, make the creative decisions, and implement it all by myself. So I brought AI agents into every phase. Not just the code. The research, the brainstorming, the architecture, and the implementation. I made the calls. The agents did the heavy lifting. But it wasn’t added as chaos, it was added as a structured process. Here’s the setup. I use the BMAD method, an open source framework where specialized AI agents facilitate every phase of product development. The research phase pulls live data from the web. Market reports, competitor analysis, technical papers. Every claim is cross referenced across multiple sources, with confidence levels and URLs cited. This isn’t an LLM guessing from its training cutoff. It’s structured research with real sources. Then comes what BMAD calls Party Mode. Multiple AI agents, each with a distinct role, enter a group discussion. A product manager, architect, UX designer, and analyst debating design system choices from completely different angles. The system selects which 2 to 3 agents are most relevant to each topic, and they naturally build on each other’s points. The framework deliberately shifts creative domains every 10 ideas so you don’t get stuck in a local optimum. Every workflow has explicit choice points. I define the topic and goals. I confirm scope before detailed work begins. I choose which direction to explore next. The agents handle the tactical execution. But the strategic decisions stay with me. The process flows through structured phases. Discovery, planning, architecture, implementation. Each phase has defined inputs, outputs, and gates before moving forward. By the time we reach implementation, the agent has full context from every prior phase. The research findings, the design decisions, the architectural constraints. In the video below, you can see one of our agents using a browser to test and select colors for our background animation in real time. This is the implementation phase. The agent is making visual decisions informed by the entire design system spec that was built through the earlier phases. Two people. No designer. A full design system built through human judgment and AI execution working together. BMAD is open source. Link in the comments.

JD
Jordan DaubinetCTO & Co-founder @ vibesell.ai - Making Sales Easy
Mar 25, 2026