Figma Launches Specs and Code Layers Beta to Guide AI Agents

Introduction: Teaching AI Agents the House Rules

Imagine hiring a talented new designer who has never seen your brand guidelines. The work might look good, but it probably won’t look like you. That is the problem Figma is tackling as AI agents start creating inside its platform. At its first Config event in India, the design company announced tools meant to steer agents toward more predictable, on-brand results.

Key Facts: What Figma Announced

Figma Specs

The headline feature is Figma Specs, a Markdown-based editor where users define their design language in a simple way. The document can serve as a reference for both teams and agents. Specs can live as a standalone file or sit within the canvas, and they can reference canvas elements and embed FigJam diagrams, code layers, and designs. Teams can also bring in design libraries, code libraries, and guidelines to manage context for the design system, and agents can consult this centralized source to follow the rules.

Code Layers in Open Beta

Code layers add coding to designs on the canvas, enabling more interactive prototypes. Announced in private beta in June, the feature is now in public beta. Users can ask the agent to turn a design into code or change part of the code to iterate on a prototype.

Wider Ecosystem

• Figma says it now supports over 20 new tools through its MCP (Model Context Protocol, a standard way for AI tools to connect to other software), including Open Code, Dia, Grok Bot, and Indian AI startup Sarvam.
• The Payload headless CMS (content management system), acquired last year, is being integrated with Figma Make for generating dynamic websites and managing content. Figma says Payload downloads grew sixfold in 12 months.

Technical Background: Why Guardrails Matter

AI models are non-deterministic: ask the same question twice and you may get different answers. Think of it like a jazz musician improvising; wonderful, but not always what the client ordered. A shared written spec works like sheet music, giving agents a consistent reference. Figma’s agent on canvas was made available to a wider user base in the past week, so these controls arrive just as usage grows.

Chief design officer Loredana Crisan framed the shift this way:

“There are 17-times more lines of code being produced today than in the past. But when you’re looking at releases, only 1.3-times more releases.”

Her point, per the article, is that much of that code is built to discover what the product is, not to ship.

Global Implications

Hosting the event in India is notable: Figma says the country is its biggest market by users. It opened its first Indian office late last year, covering sales, partnerships, go-to-market, and product support. Including Sarvam in MCP support also signals attention to local AI players. More broadly, if design systems become machine-readable instructions, brand consistency could become something teams enforce across both human and AI contributors.

Conclusion and Outlook

Figma is positioning itself as the place where software teams decide what to build, and Specs plus code layers aim to make AI contributions more dependable. Open questions remain, such as how well agents actually follow specs in practice; the source does not provide performance data. It is worth watching how the public beta feedback shapes the final product.

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🛒 Recommended Gear

• The Agentic AI Bible — Building Goal-Driven LLM Agents
• Build a Reasoning Model From Scratch (Sebastian Raschka)

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Sources (1 articles)

• [TechCrunch] Figma announces new features to define how agents work on its platform

※ This article synthesizes and analyzes the above sources. Generated: 2026-10-15 06:03