Put your design system into everything AI builds
AI is putting product-building in more hands — and design systems risk being left out of the workflows shaping what ships. Bring your components, tokens, patterns, and guidance into supported AI tools, so teams can create more consistent product experiences with AI.
Context for AI agents / Why it matters
AI can scale product work faster than teams can keep it consistent
Agents invent what they can’t find
When agents can’t find your components, they create their own, reach for generic patterns, or copy examples that no longer match your design system.
They hardcode instead of using tokens
Without the right tokens and usage guidance, agents make their own choices about values and patterns, creating inconsistencies in how interfaces look and behave.
The expected gains get lost in rework
Design, engineering, and QA teams fix avoidable mistakes across the workflow. That rework can absorb AI’s time savings, undermining expected productivity gains and ROI.
Context for AI agents / With zeroheight
Turn your design system into an AI advantage
zeroheight gives agents components, tokens, and code alongside the patterns, rules, and decisions that explain what to use and how. Teams can put that fuller picture to work across supported AI workflows, from prototyping and coding to reviewing what’s been built.
Context for AI agents / How it works
Bring your design system together for AI
Agents need more than access to your components
Design and code sources give agents the ingredients: components, properties, tokens, and code. Used alongside them, zeroheight adds the recipe — the patterns, rules, and decisions that explain what to use, when, how, and why — so agents can produce more consistent, on-system work with less to fix later.
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Comparison | |||
System context | Components, code, tokens, and guidance come together in one usable source. | Design system context is spread across sources, each providing only part of the picture. | |
Access and use | Teams can ask a connected agent or use the docs directly, while agents call on the same guidance for tasks — with one place for owners to maintain and govern it. | Access and maintenance are spread across separate sources, leaving people and agents to find and combine the guidance they need. | |
Guidance while building | Agents receive usage rules alongside components and tokens. | Assets or code may reach agents without the guidance around them. | |
Decisions | Agents can see what to use, when, how, and why. | Agents fill the gaps with assumptions about what to use and how. | |
Output | Agents produce more consistent, on-system work — with less to fix later. | Output can look finished while leaving off-system choices to fix in review. | |
FAQs
- What is design system context for AI agents?
Design system context is the components, tokens, patterns, code, and usage rules an AI agent needs to understand how a product should be built. It helps the agent use the real system instead of inventing components or filling gaps with assumptions.
- How can AI agents access design system context from zeroheight?
- Which AI tools can use design system context from zeroheight?
- What is the difference between MCP and the zeroheight API?
- Why connect an AI agent to zeroheight alongside Figma, Storybook, or a code repository?
- Does design system context make AI-generated work production-ready?
- How does design system context reduce drift and rework?