If you last compared Figma, Diagram Genius, and Galileo AI as three separate design tools, that comparison is now out of date. Figma acquired Diagram in 2023, and Genius is no longer presented as a standalone product. Galileo AI became the predecessor to Google Stitch after its acquisition by Google. The useful comparison in 2026 is Figma AI and Figma Make versus Google Stitch.

Use Figma Design AI for work inside an existing Figma system, Figma Make for code-backed prototypes, and Google Stitch for broad exploration from prompts, code, or design files. Each can shorten early exploration. None removes the need to test the flow and review accessibility, security, and implementation quality.

The short answer: what happened to Genius and Galileo?

Diagram Genius became part of Figma's AI story

Genius came from Diagram, an AI design company that Figma acquired in June 2023. Figma's historical description of Genius said it could parse Figma files and make suggestions using components from a design system. That source does not substantiate later claims such as automatic conversion testing or guaranteed high-performing UX copy.

There is no current standalone Genius product in Figma's product or help documentation. Designers looking for an actively maintained tool should evaluate Figma's native AI tools, the Figma design agent, and Figma Make.

Galileo AI was succeeded by Google Stitch

Galileo AI co-founder Arnaud Benard announced that Google had acquired the company and described Stitch as the next generation of its product. This is the co-founder's statement rather than a Google newsroom announcement. It is more accurate to call Stitch a successor than a simple rename: the current service is a Google Labs experiment with its own canvas, agent, integrations, and release cycle.

Google's May 2026 update says Stitch can begin with a text or voice prompt, existing code, or design files. Its agent streams work to the canvas and accepts direction while it is generating. A result can be shared through Google AI Studio, exported to Google Antigravity for backend work, or published with Netlify. Backend logic remains a separate step in the documented workflow.

What Figma AI means in 2026

"Figma AI" is an umbrella label, not one assistant. It covers small editing tools inside Figma products, a design agent in beta, and Figma Make for code-backed prototypes. The distinction matters because each produces a different kind of output.

AI tools inside Figma Design

Figma Design includes focused actions for common canvas work. According to the current Figma Design AI documentation, these include finding assets with text or an image, replacing placeholder content, adding prototype interactions, renaming layers, rewriting or translating text, and editing images. First Draft can create editable early designs, and its functionality is moving into the Figma design agent.

These tools are useful when the file is already the source of truth. A designer can clean up layer names, explore copy lengths, find an existing component, or create an early direction without leaving the canvas. The output remains editable Figma content, which makes comparison and critique straightforward.

Access has real constraints. Figma says Design AI requires a paid plan and a Full seat. An administrator can disable AI features, usage draws from a shared credit system, and the design agent is still a gradual beta rollout. Figma also warns that AI output may be wrong or misleading. A team should confirm availability before building a process around it.

Figma Make is for functional prototypes and web apps

Figma Make turns prompts and attached designs into code-backed prototypes, web apps, and interactive UI. You can point at an element and request a change, work in the code editor, attach a framed design, and provide style context. This makes it a better fit than a static mockup when a team needs to test behavior or demonstrate a flow.

Make is not simply Figma Design with a chat box. The normal Design tools are not directly integrated into the Make editor. In the standard Copy design workflow, paid users can copy the current preview into Figma Design as editable layers, but that snapshot is one-way: changes made in Design do not sync back to the original Make file, and the pasted layers are not automatically connected to the team's design system. Figma separately offers a limited Mac desktop beta for Make on a local codebase; that is not the standard Make workflow or general availability.

The first generated version may use generic components, placeholder copy, or incomplete states unless the team provides frames, libraries, tokens, and explicit requirements. Figma still labels Make publishing as beta. Public publishing creates a URL that anyone with the URL can access, while Organization and Enterprise users can instead restrict a published app to an internal audience; administrators can also disable web publishing. Figma says the author remains responsible for the content, visitor data, and applicable legal obligations. Treat a Make result as a prototype that needs engineering, privacy, security, accessibility, and rights review.

Figma MCP connects agents to design context

The remote Figma MCP server supports a different workflow. It can read design context and retrieve screenshots or downloadable assets. In supported clients, its beta write-to-canvas tool can create and modify native Figma objects using existing components, variables, frames, auto layout, and tokens.

Write access is currently limited to Full and Dev seats on paid plans, and Dev seats are read-only outside drafts. The write tool does not yet support image assets or components containing images or video. Generated designs may still need manual correction, and reading a design system does not guarantee that an agent selected the right component or preserved the intended behavior.

What Google Stitch does now

Google describes Stitch as an experimental AI design workspace. Its March 2026 canvas update supports images, text, and code as context as well as parallel design directions. Its May 2026 update adds text or voice input, existing codebases and design files, and real-time steering.

Stitch is appealing when the question is still broad: What could this flow look like? Which information hierarchy reads best? How might the same product idea work on mobile and desktop? The canvas makes it easy to keep alternatives visible instead of replacing one draft with the next.

Google directs users to Antigravity when they are ready to add backend logic. Public publishing through Netlify is a deployment option; the team still owns the application code and release review.

Figma Make vs Google Stitch: choose by the job

There is no universal winner. Start with the artifact and decision your team needs next.

  • Choose Figma Design AI when the team already works in a governed Figma file and needs editable layers, content help, asset search, or quick visual exploration.
  • Choose Figma Make when an existing Figma direction should become a code-backed, interactive prototype and access to team style context matters.
  • Choose Google Stitch when you want to explore several interface directions on an open canvas or start from mixed inputs such as a prompt, codebase, image, and design file.
  • Choose Figma MCP when a supported coding or design agent needs structured context from a Figma file, or when a team wants an agent to write back to the canvas.

A practical workflow may use more than one. A team can explore a rough interaction in Stitch, rebuild the selected direction with its actual Figma components, and use MCP to give an implementation agent the approved design context. Automatic transfer does not preserve every design-system rule, interaction, or engineering constraint, so review the result at each handoff.

A reliable workflow for AI-assisted interface design

1. Define the decision before writing the prompt

"Design a finance app" is too vague to evaluate. State the user, task, platform, and decision. For example: "Create three mobile directions for a returning user who needs to freeze a lost card in under one minute. We are deciding how much confirmation is necessary."

This turns generation into a design exercise instead of a screenshot contest. It also gives reviewers a shared standard.

2. Supply real constraints and content

Include the viewport, navigation model, supported languages, brand rules, existing components, and required states. Use representative text lengths and data instead of lorem ipsum. If the tool can access a design library, name the relevant components and tokens rather than asking it to infer the whole system.

Do not paste secrets, private customer records, or unreleased commercial information into a prompt. Check the organization's AI and content-training settings before attaching internal files.

3. Generate a few distinct directions

Ask for a small number of alternatives with a reason for each structural choice. Three meaningfully different flows are more useful than twenty cosmetic variations. Keep rejected directions visible so the team can explain what it learned.

4. Bring the selected direction into the real system

Replace generated buttons, fields, typography, spacing, and icons with the team's actual components and tokens. Check component properties rather than judging by appearance alone. A frame can look right while using detached copies, hard-coded colors, or spacing values that will fail during implementation.

5. Add the states the prompt skipped

Generated happy paths tend to look convincing. Concepts often break down in the missing states: loading, empty results, validation, permission denial, slow networks, long names, expired sessions, and destructive actions. List those states in the acceptance criteria and review each one.

6. Test the flow, not the image

Give the prototype a realistic task and observe where people hesitate. Ask what they expect to happen next. Do not explain the interface while they use it. A generated layout may follow common patterns and still be wrong for the audience, vocabulary, or risk level of the product.

7. Hand off evidence with the design

Record the accepted flow, component mapping, responsive behavior, edge cases, content decisions, and known limitations. Link the research or test evidence that changed the design. Developers need those decisions more than they need another generated screen.

What a human review must cover

  • User need: Does the flow solve a verified problem, or only look familiar?
  • Content: Are labels specific, accurate, and understandable without insider language?
  • Interaction: Are loading, error, empty, success, disabled, and destructive states defined?
  • Responsive behavior: Does the hierarchy survive narrow screens, zoom, long text, and larger text settings?
  • Accessibility: Can the task be completed with a keyboard and assistive technology? Are focus order, names, semantics, contrast, targets, and error messages reviewed?
  • System integrity: Are real components, variables, tokens, and approved assets used without detached or duplicated substitutes?
  • Engineering quality: Has the code been reviewed for dependencies, performance, maintainability, testing, and browser support?
  • Security and privacy: Are authentication, authorization, secrets, data collection, retention, and third-party services handled by the appropriate specialists?
  • Rights: Does the team have permission to use generated or attached text, images, fonts, icons, and code?

No automated score covers that entire list. AI can help expose variants and repetitive gaps, but a person accountable for the product must make the final call.