Doop

Prompt

Build an AI-Powered Collaborative Design Canvas

Create a sophisticated, production-oriented web application where human designers and AI agents collaborate on the same infinite visual canvas in real time.

The product should fundamentally rethink the relationship between people, AI and digital design. Instead of generating a static image from a text prompt, AI agents should become active collaborators capable of creating, editing, reviewing and improving fully editable designs.

Build an original visual identity and product experience, taking inspiration from collaborative AI design workflows without copying existing branding or interface assets.

1. Infinite Design Workspace

Develop a high-performance infinite canvas supporting smooth panning, zooming, drag-and-drop positioning and multiple independent design frames.

Each frame must support HTML and CSS rendering, allowing users and AI agents to create real interface components rather than flat screenshots.

Include a minimap, frame-selection tools, alignment controls, responsive artboards and an organized layers panel.

Support landing pages, mobile interfaces, product dashboards, marketing graphics and individual UI components.

2. Multi-Agent AI Integration

Connect multiple AI providers through a secure, extensible architecture.

Support Anthropic Claude, OpenAI models and other compatible providers through their respective APIs or external agent integrations.

Implement Model Context Protocol (MCP) so external AI coding assistants can discover available canvases, inspect elements, create frames, edit HTML, read screenshots and update their work.

AI-generated edits must be attributable to the agent performing them.

3. Real-Time Collaborative Editing

Implement WebSocket-powered collaboration where multiple users and agents can participate in the same workspace.

Display live cursors, participant identities, editing activity and synchronized frame updates.

An AI agent should be able to generate a landing-page layout while another agent improves typography and a human designer changes colors.

All participants must see the updated design without manually refreshing the browser.

Include synchronization safeguards, reconnect handling and appropriate edit-conflict resolution.

4. Natural Language Design Commands

Build a conversational interface connected directly to the canvas.

Users should be able to request changes such as:

"Create a premium landing page for an architecture studio."

"Make this dashboard more compact and improve the visual hierarchy."

"Design three mobile variations of this desktop layout."

"Review the selected frame for accessibility issues."

AI agents should inspect the relevant frame and modify its actual structure rather than merely describing how it could be improved.

5. Autonomous Design Review

Implement an iterative design workflow in which an AI agent can inspect its own output, identify visual defects and propose corrections.

The process should include:

- Generate the initial frame.
- Render the frame in an isolated browser environment.
- Capture a screenshot.
- Analyze alignment, readability, contrast and responsiveness.
- Modify the frame to address detected problems.
- Present the updated result for human approval.

Keep the review process observable and retain earlier revisions where feasible.

6. Specialized AI Design Roles

Create optional specialist agents for interface design, copywriting, accessibility, brand consistency and visual quality.

Allow users to assign work to a particular role or coordinate several roles across one design task.

Specialist agents should share relevant workspace context so that their work remains visually consistent.

Require human approval before publishing final deliverables.

7. Design Memory

Implement persistent project-specific design guidelines.

Allow users to save brand colors, typography systems, spacing rules, reusable components and approved reference designs.

AI agents should consult those guidelines before generating new layouts.

Provide controls for reviewing and correcting automatically suggested design rules.

8. Collaboration, Sharing and Export

Allow users to create private projects, invite collaborators and share selected canvases.

Provide export functionality for PNG, JPG, PDF and editable HTML where technically supported.

Maintain revision history and an activity feed showing which human or agent made each change.

9. Security and Reliability

Render generated HTML in sandboxed environments.

Secure authentication, workspace permissions, agent authorization and API keys.

Prevent unauthorized cross-workspace edits and restrict potentially dangerous generated code.

Handle failed AI requests gracefully without losing the existing canvas.

10. Technical Architecture

Use React and TypeScript for the interface, a Node.js-compatible backend, WebSockets for synchronization and PostgreSQL for persistent storage.

Build an MCP server with authenticated tools for canvas operations.

Use containerized deployment, appropriate server-side model integrations and background task processing for longer-running design jobs.

Optimize the canvas for smooth interactions and efficient synchronization even with many frames.

11. Visual Design Direction

Create an original, premium creative workspace with minimalist controls, strong typography, subtle motion, generous working space and refined micro-interactions.

The design should feel like a professional application for designers rather than a generic chatbot.

Prioritize the actual canvas and make every AI interaction understandable through visible status indicators and activity records.

Final Product Vision

Deliver a genuinely collaborative design environment where human creativity and AI capabilities operate together.

The key experience should be watching an AI agent create a design, inspecting its work, providing feedback and seeing the actual design change in response.

The final result must be interactive, technically credible, visually polished and meaningfully different from conventional prompt-to-image generators.