Design

Multi-agent brainstorming for design system decisions

Role-based AI agents such as a product manager, architect, UX designer, and analyst debate design system options from different perspectives to generate and refine ideas.

Why the human is still essential here

Jordan chooses the direction to explore, confirms scope, and decides which ideas are worth pursuing instead of letting AI decide autonomously.

How people use this

Role-based design critique

AI personas simulate product, UX, engineering, and analytics stakeholders to challenge a proposed component pattern and surface tradeoffs before the team commits.

ChatGPT / Claude

Brainstorming board generation

AI turns a design-system prompt into structured workshop boards, sticky notes, and clustered themes so early ideas can be explored faster in a visual format.

FigJam AI / Miro AI

Component naming workshop

AI proposes naming schemes, taxonomy options, and organizational logic for tokens and components while different agent roles argue for clarity, scalability, and developer handoff.

FigJam AI / ChatGPT

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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