Explore the world of Figma Weave workflows in the new Community area. Figma Weave (formerly Weavy) combines multiple AI models with professional editing tools in a node-based canvas, covering image, video, animation, and VFX.

Figma opens the canvas to agents. The use_figma MCP tool lets Claude Code and Codex generate and modify designs grounded in your actual design system. The key distinction from earlier code-to-design experiments: agents work with what your team has already built, making design system quality a direct input to AI output quality. 

Figma Skills are Markdown instruction files that teach AI agents how to reliably handle specific Figma tasks — generating a design system from a codebase, building screens from existing components, and more. Community contributors are already adding new ones.

Figma and Anthropic demonstrate the Claude Code roundtrip in both directions. Write to Canvas lets Claude Code create and modify designs directly in Figma; Code to Canvas brings live browser UI back in as editable frames. Worth watching alongside the Agents on Canvas blog post and Figma Skills page for the full picture of what this workflow ecosystem looks like.

Ed Bayes leads design on Codex at OpenAI; Gui Seiz leads AI product design at Figma. Together with Aakash Gupta, they give a rare behind-the-scenes look at what the Codex-to-Figma MCP integration actually changed inside their teams. The workflow is genuinely bidirectional, code and canvas reading from each other directly. The most concrete signal that something shifted: designers are working directly in staging.

A thorough, practical guide from Christine Vallaure on what design systems need to work well with agents. Five things most systems are missing: semantic tokens, full state coverage, slot-based composition, exact Figma-to-code property matching, and token-based auto layout. Pairs well with the Brad Frost video — both cover the same design system moment from different angles.

Brad Frost and Chromatic cofounder Dominic Nguyen walk through how Storybook’s new MCP exposes design systems for agents to use reliably. The key insight: AI doesn’t invent states your components don’t have — it amplifies what’s documented. Coverage, completeness, and constraints are now load-bearing.

Christine Vallaure built a five-person AI editorial team from one text file and seven lines. Five named agents: Rachel brings reader feedback, Joan brings numbers, Caitlin flags voice problems, Miranda raises buyer concerns, and Elke runs the room as editor in chief. Technically one model shifting between personas inside a single Claude conversation, but behaviorally a team critique. The most concrete personal account I’ve read of what agentic AI actually feels like to operate as a solo creator.

Steve Schoger from Tailwind Labs recorded a step-by-step walkthrough on taking an AI-designed dashboard and turning it into something that looks like it was designed by a pro.

Pair the Emil Kowalski skill with the Jakub Krehel skill above, and you’ve got a solid baseline of design engineering taste baked into your AI coding workflow. Emil’s focus is specifically on animations — spring physics, performance, and interruptibility.

Admins can now purchase additional AI credits in Figma, either as a subscription with volume-based pricing or pay-as-you-go for variable usage. Available now for Organization and Enterprise plans, coming to Professional in May.

Yann-Edern Gillet, design engineer at Linear, revisits his ​“Rosetta Stone” metaphor for design engineering translation through the lens of AI. The central argument: when translation is cheap and instantaneous, the bottleneck shifts from execution to meaning — and the new craft is preserving intent while everything accelerates.

Ridd interviews Kris Puckett, design manager at Stripe and formerly design lead at Mercury and Dropbox, on becoming an AI-native designer by using tools like Claude Code, OpenClaw, and personal AI to ship his own apps, supercharge his design practice, and build highly personal systems for life and work. I’m deeply curious about the last part and can’t wait to dig into his course Neuma on building a personal AI system.

The new Workflow Lab format, showing an end-to-end process, is a smart way to frame the new AI image tools in context. The three new tools (erase object, isolate object, expand image) are genuinely useful for anyone who’s had to leave Figma to do basic cleanup in Photoshop, and Vectorize finally removes a step that’s been a quiet annoyance for years.

Patrick Morgan makes a clean distinction that vibe-coding discourse keeps blurring: prototype code is for exploration, production code is for endurance. He is building a protected prototyping environment using Claude Code, a place where his team can move fast and then deliberately port the right assets across the boundary into production.

There is a clear parallel with how the design team at Notion works. In the recent episode of How I AI, Brian Lovin showed their collaborative ​“prototype playground,” where the entire team can create, share, and iterate on functional prototypes.

That also reminded me of how my team worked a decade ago, back when front-end development was a tad simpler. We had a separate ​“mockups” directory inside the Rails monorepo, where designers prepared static HTML mockups with production-ready CSS and JS. By the time designs were handed off to engineers in a feature branch, all polish and design details were already baked in. The design team must be fairly technical, but there is no going back to handing off Figma files after working this way.

Martin Rariga, Founding Product Designer at Equals, breaks down a practical AI-powered design engineering stack: Claude Code for big refactors and complex builds, Cursor for precise edits, and Figma (via MCP) to reference the new design direction while restyling.

FigJam is now available in Notion as a pre-configured MCP integration.

“Join Figma designer advocate, Ana Boyer and OpenAI product designer, Ed Bayes as they talk through roundtripping between code and canvas.”

From design system documentation and PRDs to user research and feedback, Make can now pull in context from across your product ecosystem. Figma added new featured connectors for Amplitude, Box, Dovetail, Granola, Marvin, and zeroheight. You can also connect Make to any remote MCP server by setting up a custom connector.

Once you’ve installed and authorized a Make connector, just hit @ in your Make file and start typing the connector name to pull external context directly into your prototype.

Alex Barashkov is disappointed by this release, and I have to agree with some of his points. I spend more time in Cursor than Figma lately, and returning to a workspace without AI agents is always hard. In the most recent and relevant example, after importing a few screens from code to Figma, I had to manually replace fonts (no ​“Selection fonts” for bulk edits, so first had to test a few plugins) and colors (a bit easier but still cumbersome), then abstract repetitive elements into components. While doing this, I kept asking myself why I have to waste time on this when bots can do it in minutes.