August 30, 2026AIExplore

How to Use Figma Agent for Conversational Design

Chat with the Figma agent on selected frames, connect libraries, reference components with @, and iterate multi step design edits on the canvas.

Figma AI

The Figma agent lets you collaborate in design files to generate and remix designs, automate busywork, and get design feedback. Official agent help: select a layer and click Agents, or press Cmd or Ctrl + Enter. Full seats with edit access can make edits. View, Dev, or Collab seats, or view only access, can chat without editing. Some capabilities need paid plans. During beta the agent is free to try with monthly capacity limits, and beta help says AI credits are not consumed for agent prompts or actions. Confirm live status. Overview: /explore/figma-ai.

This guide covers library context, @ references, parallel prompts, and undo habits. Pair with First Draft at /blog/how-to-use-figma-ai-for-first-draft-explorations and layer hygiene at /blog/how-to-use-figma-ai-for-prototype-interactions-and-layer-hygiene.

When the agent is the right job

Use the agent for multi step canvas work: apply a system, bulk edit content, critique a frame, or chain several AI tools. Use Actions for a single focused rewrite or rename when chat overhead is unnecessary.

Step by step workflow

1. Select context and open Agents

Select the frame or layers that matter. Open Agents from the canvas prompt, keyboard shortcut, or left navigation sidebar.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Session Selection: checkout frame Entry: Cmd or Ctrl + Enter Seat: Full with edit access Goal: system align primary CTA. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Session Selection: checkout frame Entry: Cmd or Ctrl + Enter Seat: Full with edit access Goal: system align primary CTA" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Inputs:
Use the actual source material required for this task, together with the intended audience, destination, format, and quality requirements. Specify relevant files, text, URLs, structured data, reference assets, dimensions, language, tone, timing, model or generation settings, credentials, field mappings, or integration values when they apply. Keep secrets out of the example and use the product's supported credential or configuration mechanism.

Workflow:
1. Start with one representative input that contains the important characteristics of the real workload.
2. Configure figma-ai specifically for the requested task and make the important settings explicit.
3. Run the first pass and inspect the result against the objective.
4. Correct only the settings or source material responsible for a failed requirement instead of changing everything at once.
5. Validate the intermediate result before passing it to the next step.
6. When the result meets the requirements, save the successful configuration so the same process can be repeated consistently.
7. If this is a multi-step workflow, verify each handoff and keep a clear fallback or review path for failures.

Requirements:
The example must use supported functionality only. Do not invent product features, pricing, limits, integrations, model names, API behavior, or unavailable settings. Preserve important source data and formatting. Validate required fields before processing, handle empty or invalid input explicitly, prevent accidental duplicate processing where relevant, and stop or route the item for review when a required step fails. For credentials, use the supported secure configuration rather than placing secrets directly in the content.

Expected output:
The final result should directly satisfy the "Session Selection: checkout frame Entry: Cmd or Ctrl + Enter Seat: Full with edit access Goal: system align primary CTA" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

2. Connect library and reference elements

Click Add context to connect a library. Type @ to reference components, variables, and styles for closer system match.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Prompt Connect library: Design System Replace ad hoc buttons with @Button/Primary Apply heading text style to price Keep spacing grid. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Prompt Connect library: Design System Replace ad hoc buttons with @Button/Primary Apply heading text style to price Keep spacing grid" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Prompt Connect library: Design System Replace ad hoc buttons with @Button/Primary Apply heading text style to price Keep spacing grid" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

3. Iterate, parallelize, and undo

Run follow ups one constraint at a time. Start prompts on multiple frames in parallel. Use Undo in chat or Cmd or Ctrl Z if a change misses.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Follow up Tighten only the shipping row spacing Then summarize comments from design critique. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Follow up Tighten only the shipping row spacing Then summarize comments from design critique" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Follow up Tighten only the shipping row spacing Then summarize comments from design critique" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Practical agent prompts

System align CTA

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: On selected frame: replace ad hoc buttons with @Button/Primary. Keep label text. No new colors.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "On selected frame: replace ad hoc buttons with @Button/Primary. Keep label text. No new colors." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "On selected frame: replace ad hoc buttons with @Button/Primary. Keep label text. No new colors." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Bulk spacing

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Apply consistent 16 vertical gap between form fields in this frame. Do not change field widths.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Apply consistent 16 vertical gap between form fields in this frame. Do not change field widths." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Apply consistent 16 vertical gap between form fields in this frame. Do not change field widths." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Persona critique

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Critique this onboarding from a first time user persona. List top three friction points. Do not edit yet.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Critique this onboarding from a first time user persona. List top three friction points. Do not edit yet." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Critique this onboarding from a first time user persona. List top three friction points. Do not edit yet." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Comment summary

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Summarize open comments on this page by theme. Suggest which are must fix before handoff.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Summarize open comments on this page by theme. Suggest which are must fix before handoff." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Summarize open comments on this page by theme. Suggest which are must fix before handoff." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Responsive adapt

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Convert this desktop marketing hero to a mobile width layout. Keep hierarchy. Use auto layout where helpful.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Convert this desktop marketing hero to a mobile width layout. Keep hierarchy. Use auto layout where helpful." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Convert this desktop marketing hero to a mobile width layout. Keep hierarchy. Use auto layout where helpful." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Content bulk

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Rewrite all card titles in this selection to sentence case and under 42 characters.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Rewrite all card titles in this selection to sentence case and under 42 characters." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Rewrite all card titles in this selection to sentence case and under 42 characters." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Accessibility pass

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Check this frame for contrast and tap target risks. List issues with layer names. Do not auto fix yet.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Check this frame for contrast and tap target risks. List issues with layer names. Do not auto fix yet." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Check this frame for contrast and tap target risks. List issues with layer names. Do not auto fix yet." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Variable mode

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Switch this frame to the dark mode variable set if available. Report anything that stays unbound.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Switch this frame to the dark mode variable set if available. Report anything that stays unbound." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Switch this frame to the dark mode variable set if available. Report anything that stays unbound." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Image edit ask

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Replace the hero image fill with a calmer abstract background. No text in the image. Match 1440 by 640.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Replace the hero image fill with a calmer abstract background. No text in the image. Match 1440 by 640." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Replace the hero image fill with a calmer abstract background. No text in the image. Match 1440 by 640." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Plugin ask

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: I want a plugin: select any frame and export a PDF contact sheet of nested icons with consistent padding.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "I want a plugin: select any frame and export a PDF contact sheet of nested icons with consistent padding." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "I want a plugin: select any frame and export a PDF contact sheet of nested icons with consistent padding." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

More copyable examples

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Shader ask Shader: subtle paper grain on this fill. Keep base color. Let me control intensity.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Shader ask Shader: subtle paper grain on this fill. Keep base color. Let me control intensity." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Shader ask Shader: subtle paper grain on this fill. Keep base color. Let me control intensity." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Parallel explore On frame A explore a denser card grid. On frame B explore a single column list. Keep brand tokens.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Parallel explore On frame A explore a denser card grid. On frame B explore a single column list. Keep brand tokens." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Parallel explore On frame A explore a denser card grid. On frame B explore a single column list. Keep brand tokens." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Instance props Set all selected Button instances to variant Size=Large. Do not change labels.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Instance props Set all selected Button instances to variant Size=Large. Do not change labels." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Instance props Set all selected Button instances to variant Size=Large. Do not change labels." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: How to question How do I use auto layout with absolute position children? Answer from Figma help. No canvas edits.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "How to question How do I use auto layout with absolute position children? Answer from Figma help. No canvas edits." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "How to question How do I use auto layout with absolute position children? Answer from Figma help. No canvas edits." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: File search Search our files for an existing empty state with illustration and primary CTA. Prefer Design System team files.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "File search Search our files for an existing empty state with illustration and primary CTA. Prefer Design System team files." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "File search Search our files for an existing empty state with illustration and primary CTA. Prefer Design System team files." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Rename assist Rename layers on this page with Frame and Component prefixes that match the visible UI role.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Rename assist Rename layers on this page with Frame and Component prefixes that match the visible UI role." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Rename assist Rename layers on this page with Frame and Component prefixes that match the visible UI role." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Stop and refine Stop. Undo last change. Retry with only spacing updates and no copy edits.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Stop and refine Stop. Undo last change. Retry with only spacing updates and no copy edits." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Stop and refine Stop. Undo last change. Retry with only spacing updates and no copy edits." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Handoff notes Add a comment listing unresolved accessibility risks on this frame for engineering.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Handoff notes Add a comment listing unresolved accessibility risks on this frame for engineering." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Handoff notes Add a comment listing unresolved accessibility risks on this frame for engineering." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Tips

Select before you prompt
Connect libraries for system work
Use @ for components, variables, and styles
One constraint per follow up
Undo early when a change drifts
Confirm seat edit rights and beta capacity limits

Verification notes

Agent facts come from Work with the Figma agent in design files and AI agent beta access help. Feature support tables change. Confirm prototyping and other coming soon rows before relying on them. When plan access, seat rules, or credit behavior differ from memory, open help.figma.com and your AI balance. Limits change, so do not promise client timelines from outdated screenshots.

Keep a short session log beside the file: tool used, selection, prompt, and the one change you will try next. That habit turns four variants or parallel agent threads into learning instead of noise.

When Actions are missing, check whether an admin disabled Figma AI for the workspace before rewriting prompts. Access problems look like prompt problems until you confirm settings.

Treat the first AI pass as a sketch. Compare against your spacing grid, type styles, and accessibility targets before inviting stakeholders.

If you collaborate, store winning prompts on a shared page next to the library link. Reuse the skeleton and change only product nouns or channel constraints.

Separate exploration files from production libraries. Explore freely in drafts, then rebuild keepers with real components before handoff.

Close each session by naming frames clearly and discarding dead ends. A clean page saves time when someone asks for the pricing riff from Tuesday.

Prefer concrete product language over vague vibes alone. Screen type, primary action, and one constraint outperform a pile of adjectives.

Confirm AI credit balance and reset date before large image or agent batches. Shared credits power many Figma AI surfaces.

Connect libraries with Add context and type @ before complex system work. Vague component names invite invented styles.

Run parallel prompts on different frames when exploring, then open each loading indicator to review steps and results.

During beta, confirm whether agent usage is free with monthly capacity limits. Credit rules for general availability may differ.

Think of the agent as a teammate who can see your selection. Without a selection, broad prompts wander across the page and burn capacity on the wrong layers.

Library context is the difference between on brand output and pretty but unusable styles. Connect the library before you ask for system work.

Parallel prompts are powerful for exploration and dangerous for production edits. Use them to compare directions, not to edit the same locked frame twice.

Chat visibility rules can change. Official notes describe newer chats becoming visible to Full seat editors by default after a stated date. Confirm privacy expectations with your team.

Ask how to questions when you are stuck on Figma mechanics. The agent draws from Help Center material and can save a tab hunt without touching the canvas.

Stop running prompts when you hit a monthly beta capacity message. That limit is separate from other AI credit pools according to beta help.

For image heavy or content heavy jobs, decide whether Actions alone is cheaper than a long agent thread. Single tools still win for one off rewrites.

Undo is part of the craft loop. Duplicate a promising AI result before undo if you want a side by side keep versus revert comparison.

Write acceptance checks into the prompt when quality matters: under 40 characters, use @Button/Primary only, no new colors outside tokens.

For critique requests, ask the agent not to edit until you approve the findings. Separation keeps feedback readable.

Attach files only when the extra context is necessary. Large attachments can increase task complexity and credit or capacity cost later.

Teach teammates the keyboard shortcut. Faster access means smaller, more frequent prompts instead of one giant risky request.

Common mistakes

Prompting with no selection when the task is frame specific
Asking the agent to invent a design system instead of connecting one
Stacking five unrelated goals in one message
Assuming View seats can edit the canvas through chat
Ignoring monthly beta capacity messages
Skipping verification of accessibility and copy claims

Related Figma AI articles: /blog/how-to-use-figma-ai-for-first-draft-explorations, /blog/how-to-use-figma-ai-for-prototype-interactions-and-layer-hygiene, and /blog/how-to-use-figma-ai-to-make-and-edit-images. Return to /explore/figma-ai for the broader guide set.