Visual Feedback Workflow with AI Agents
When working on frontends, code alone doesn’t always tell the full story. What users see — and how the UI behaves across screen sizes, states, and interactions — is critical to quality. This workflow introduces how to use screenshots and browser context as inputs for AI-assisted iteration.
By pairing screenshots with prompts and augmenting context via browser-based MCPs, developers can give AI direct visibility into what’s wrong — and receive precise, design-aligned suggestions for improvement.
When to Use This Workflow
- After generating a UI screen with AI and you want to validate spacing, layout, or responsiveness
- When reviewing visual bugs reported by QA or designers
- When refactoring visual components or improving cross-browser compatibility
Capture a Screenshot for Feedback
Use your browser’s built-in tools or a screenshot extension to capture:
- A full-page screenshot
- A clipped component or layout section
- A specific device viewport (e.g. mobile)
Save the image and drop it directly into Cursor (or Claude if supported), then use a prompt like:
Here’s what the UI looks like. The layout doesn’t match the Figma design — spacing is off and the sidebar is misaligned. What improvements can we make to align with a clean, balanced layout?
Tips
- Point out specific problems (“font size too large on buttons”, “image is overflowing container”)
- Use annotations if supported by the tool (e.g., cursor artifacts or Claude screenshots)
Add Browser Context with MCPs
For deeper debugging or responsive fixes, feed AI more signals using browser-based Modular Context Providers (MCPs):
Available Browser Contexts via MCPs:
- Console Logs Prompt:
Use browser MCP to fetch latest errors and logs. What do these console warnings mean? - DOM Structure Prompt:
Fetch current DOM and analyze why #sidebar has overlapping margin. - Network Activity Prompt:
Analyze failed network calls on page load using captured logs. Suggest fixes.
Use a browser MCP (e.g., Chrome DevTools MCP) to automatically extract these values into context for the AI to reason about layout bugs, render issues, or API failures.
Iterate and Apply Fixes
After analyzing the feedback, prompt the AI to generate safe changes step-by-step:
Based on the feedback, update the CSS for the header to improve vertical alignment and add spacing between nav items.
Use AI to:
- Refactor layout
- Suggest style tokens from your design system
- Detect visual regressions between versions
- Repeat by capturing new screenshots after each iteration to validate progress.
Benefits of the Visual Feedback Workflow
- Makes invisible problems visible — especially spacing, color, font, and layout inconsistencies
- Accelerates alignment with design intent (e.g., Figma specs)
- Helps non-technical stakeholders (QA, design) participate in reviews using screenshots
- Combines what the user sees with what the code is doing under the hood
Example Prompts
Here’s a screenshot of the user profile screen. Improve the layout to match a clean card-based structure with better spacing.
Use the DOM structure to suggest accessibility fixes based on ARIA roles and contrast.
Given the failed API call shown in network logs, what’s likely missing in the fetch logic?