Uber Automates Design Documentation with Agentic Systems

Uber has unveiled uSpec, an agentic system that automates the creation of component design specifications by transforming visual designs from Figma into detailed technical documentation. The system, described in the Uber Engineering Blog by Ian Guisard, reduces documentation time from weeks to minutes while maintaining strict security controls over proprietary design data.

From Visual Design to Technical Contract

The core innovation behind uSpec is its ability to function as a "Visual-to-Technical Spec" compiler. An AI agent operating within the Cursor IDE connects to a local Figma Desktop session through a WebSocket bridge, enabling the agent to "crawl" the component tree and extract critical information including design tokens and variant axes.

This automation addresses a particularly time-consuming aspect of Uber's design process: ensuring designs include complete accessibility feature descriptions. With seven platform stacks and three accessibility frameworks spanning hundreds of pages, every design change previously created a massive combinatorial workload. What once required manual translation of Figma documents into detailed technical specifications for each platform is now handled automatically.

Agent Skills: Encoding Domain Expertise

The intelligence driving uSpec resides in what Uber calls "Agent Skills"—structured Markdown files that encode the company's internal domain expertise. These skills handle complex mappings such as:

• Platform-Specific Accessibility: Translating a single visual button into its corresponding VoiceOver (iOS), TalkBack (Android), and ARIA (Web) semantic properties
• Density Logic: Calculating how padding and typography scale across Uber's implementation stacks, including SwiftUI, React, and Android Compose

This approach allows Uber to maintain consistency across its diverse technology stack while automating the tedious aspects of documentation.

Local Processing for Security

Governance of proprietary design information was a critical consideration for Uber. uSpec addresses this by keeping the entire pipeline local. As Guisard noted, "No cloud API, no proprietary design data leaving your network. At Uber, this is what makes AI-assisted documentation possible in the first place: nothing leaves your machine."

The system leverages the Figma Console Model Context Protocol (MCP), an open-source tool that enables the agent to read and write directly to the local Figma Desktop app rather than calling cloud-based design APIs. This architectural choice ensures that sensitive design data never leaves Uber's network perimeter.

Integration with Michelangelo

uSpec is integrated into Michelangelo, Uber's centralized AI platform. To maintain security, all agentic requests pass through the GenAI Gateway, a Go-based proxy that mirrors the OpenAI API. The gateway provides critical functions such as PII Redaction, scrubbing internal identifiers before requests reach external models like Claude 3.5 or GPT-4o.

The Broader Context

Uber's choice to maintain Figma as the primary artifact represents one side of an architectural divide emerging in 2026. While enterprise-scale organizations require a visual "source of truth" for cross-functional alignment, individual developers are increasingly using agentic workflows to jump directly from requirements to production-ready code.

Discussions on platforms like Reddit's r/UXDesign highlight this tension. User u/Bandos-AI commented on the shift: "I am a hybrid between developer and UX designer. I leverage AI a ton when coding and lately I rarely use Figma anymore. It's super easy to prompt your way into reusable UI components. React and similar front-end frameworks are especially good with AI as they are based on collections of isolated components."

This divergence reflects different organizational needs: enterprises require rigorous documentation and governance, while individual developers prioritize speed and direct implementation. Uber's uSpec system demonstrates how large organizations can harness AI automation while maintaining the controls necessary for enterprise-scale operations.

The system represents a significant step forward in how organizations can leverage AI for design-to-development workflows, particularly in environments where security, accessibility, and cross-platform consistency are paramount.