Design Agents Flow: Figma or Idea to Pixel-Perfect Production UI

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Design Request
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[ Agent 1: Planning & Context Capture ]
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[ Agent 2: Review & Validation ]
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[ Agent 3: Discovery & Analysis ]
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[ Agent 4: Execution ]
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[ Agent 5: Visual Verification ]
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[ Agent 6: Completion & Learning ]
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Production-Ready Code

The Problem

AI coding tools promise speed but deliver frustration. You spend 2-3 hours trying to move a filter button, and Cursor breaks working functionality with each iteration.

What you actually need:

• A design agent with taste - Someone who knows good UX and makes the right decisions so you're not debugging layout choices for hours
• Pixel-perfect Figma-to-code translation - Your designs implemented exactly as specified, not "close enough" approximations that require endless refinement
• Context that doesn't disappear - Each change preserves what came before instead of AI forgetting requirements and rewriting working code

The Solution: A 7-Agent Workflow

Stop losing hours to AI amnesia and context fragmentation. This battle-tested workflow turns unreliable AI assistants into a systematic development pipeline that gets UI changes right the first time.

How It Works

Tag the right agent in Cursor, provide your requirements, and follow the automated flow. Each agent has one job and does it perfectly:

Agent 0: Quick Change Assessment

@design-0-quick.md - The fast track for simple UI changes

• Assesses if a change is truly "quick" (padding, colors, text)
• Implements simple changes immediately
• Escalates complex work to the full workflow
• Saves hours on trivial updates that don't need six agents

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👆 The agent above handles 90% of tasks.

👇 Below is the six-agent flow for UX design and UI front-end implementation done right.

It seems long, but following this structured agent flow—rather than expecting instant perfect results and then debugging for hours when it doesn't work—has saved me hours, days, probably months of work as a UX engineer working in Cursor.

Why different agents?

Check at each stage. Move between Cursor and Claude Code to optimise costs. E.g. Planning Sonnet 4.5, Review in Opus, Discovery in Cursor with easy MCP connections, Execute in Sonnet to save costs, etc.

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Agent 1: Planning & Context Capture

@design-1-planning.md - Your design-to-code translator

• Captures ALL requirements (Figma links, screenshots, descriptions)
• Integrates with Figma MCP to extract exact specifications
• Creates detailed implementation plans
• Preserves every decision for downstream agents

Agent 2: Review & Validation

@design-2-review.md - The quality checkpoint

• Validates plans for completeness
• Catches ambiguities before they become bugs
• Asks clarifying questions upfront
• Prevents the "oops, wrong component" problem

Agent 3: Discovery & Analysis

@design-3-discovery.md - Your codebase detective

• Finds the exact files and components to modify
• Maps dependencies and impacts
• Identifies existing patterns to follow
• Documents current implementation

Agent 4: Execution

@design-4-execution.md - The precise implementer

• Follows your exact design system rules
• Preserves ALL existing functionality (no placeholders!)
• Uses documented patterns from discovery
• Implements with surgical precision

Agent 5: Visual Verification

@design-5-visual-verification.md - Automated QA

• Captures screenshots at all breakpoints
• Compares against design specs
• Detects visual regressions
• Applies automated fixes for common issues

Agent 6: Completion & Learning

@design-6-complete.md - The knowledge builder

• Documents what was built
• Updates the system knowledge base
• Captures patterns for future use
• Makes the system smarter with each project

Usage Example

1. Start with design change:
   @design-0-quick.md "Update button padding to match Figma design [figma link]"

2. Agent 0 assesses complexity → escalates if needed to Agent 1

3. Follow the flow:
   - Agent 1 creates plan
   - Agent 2 validates
   - Agent 3 finds files
   - Agent 4 implements
   - Agent 5 verifies visually
   - Agent 6 documents

4. Get working code that's properly integrated

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The Problems That Led to This Solution

After months of AI-assisted development, three critical problems kept destroying my productivity:

Problem 1: Simple Changes Taking Hours

"Just move this button to the right side" - How hard could it be?

In reality: 2-3 hours of repeated prompting, broken layouts, lost context, and the AI creating new buttons instead of moving the existing one. What should take 5 minutes becomes an afternoon of frustration.

The agent workflow solves this with Agent 0 - the quick change specialist that handles simple UI updates in one pass, escalating only when complexity demands it.

Problem 2: AI Amnesia - The 15-30% Context Loss Problem

Every new conversation starts fresh. Every clarification gets lost. Every decision has to be re-explained.

The research is clear: each handoff loses 15-30% of contextual understanding, with token consumption growing 25-40% at each stage. In my old workflow - going from ChatGPT to Cursor to Claude Code - I was losing nearly half the context by the time code was written.

The result? AI creating placeholders where there was existing business logic, building components from scratch rather than editing what was there. Design-wise the output looked good but was completely disconnected from the actual codebase.

The solution: Zero context loss through structured handoffs. Each agent writes everything down in a standardized format. No summaries, no paraphrasing - complete context preservation. Combined with Model Context Protocol (MCP) for Figma and filesystem access, agents can see your actual designs and code, not just descriptions.

Research confirms that structured workflows outperform autonomous agents by 3-5x in reliability for predictable tasks. The sequential agent approach ensures each specialist completes their task perfectly before handoff. There's also a clear human stage to check and validate everything visually.

Problem 3: Can't See Changes While AI is Coding

AI is changing code, breaking things, creating issues - but you can't see it happening. By the time you check, multiple features might be broken and you're debugging in the dark.

The solution: Parallel development environments:

• Stable Version (localhost:3000): Always-working reference for planning
• Active Development (localhost:3001): Current work that can break safely

This stable/active setup means:

• Agent 1 plans against a working version
• Agent 4 implements without blocking your reference
• You can compare changes instantly
• Visual verification happens automatically
• No more "it worked on my machine" surprises

Combined with Obsidian's Kanban plugin for visual task management, you see exactly what's in planning, doing, and done - maintaining flow state while AI processes tasks in parallel.

Complete parallel development setup guide with automated scripts →

Real Results

Before: Traditional AI Approach

• Simple button move: 2-3 hours, multiple iterations, often broken
• Component integration: 5-6 attempts to get right, usually creates duplicates
• Design compliance: 50% accuracy, constant tweaking needed
• Context preservation: Lost after 2-3 messages

After: Design Agents Flow

• Simple button move: 5-10 minutes via Agent 0, correct first time
• Component integration: Single pass, reuses existing code properly
• Design compliance: 95%+ accuracy, automated visual verification
• Context preservation: 100% maintained across entire workflow

Case Studies from Real Client Projects

Animatix: AI Video Platform Transformation
Complete UX architecture redesign across 20+ screens for their V3 investor demo. Three one-week sprints delivered storyboard systems, version management, moodboard features, and collaboration tools - all as production-ready code. Shipped directly to their Next.js codebase, merged same-day, ready for IBC Amsterdam.

Paid24/7: FinTech Onboarding Rescue
User testing revealed 50% onboarding abandonment before launch. Three-stage transformation from validation to deployed React code in 3 weeks. Delivered production-ready onboarding including country-specific flows and KYC adaptation - no Figma handoffs, just working code integrated with their TON blockchain payment platform.

Additional Examples Using This Workflow:

Moodboard Feature: Complex drag-and-drop with multiple interaction states. Traditional approach failed after 6 iterations. Agent workflow delivered correctly in one pass.

Logo Carousel: Continuous animation with hover effects. Visual verification caught and fixed timing issues before they reached review.

Navigation Integration: Discovery agent found existing layout system and integrated properly instead of creating duplicate navigation components.

The Technical Details

Context Preservation Architecture

Research shows Vibe coding now powers 25% of Y Combinator startups with 95% AI-generated codebases. But achieving these results requires solving context fragmentation.

The agent system uses:

• Structured markdown templates for each handoff
• MCP integration for Figma and filesystem access
• Kanban board tracking in status.md
• Individual task files preserving complete context
• Automated visual regression testing

Performance Optimizations

Following research on cascading model selection:

• Quick changes use lightweight models (70-80% cost reduction)
• Complex execution uses Claude 3.5 Sonnet only when needed
• Prompt caching reduces costs by 75-90%
• Parallel processing maintains 2-5x productivity gains from flow state

Time Savings

Traditional development: 40-80 hours for simple pages, 320-960 hours for complex dashboards

With optimized agent workflow:

• 70-80% reduction for routine tasks
• 50-63% reduction for complex applications
• Simple UI changes: 5-10 minutes (was 2-3 hours)
• Feature implementation: 2-4 hours (was 8-16 hours)

Implementation Guide

Prerequisites

• Cursor IDE with MCP support
• Figma API access (for design imports)
• Obsidian with Kanban plugin (for visual task management)
• Two terminal windows (stable and active development)

Quick Start

1. Clone the GitHub repository
2. Copy agent prompts to your documentation/design-agents-flow/ folder
3. Configure MCP servers for Figma and filesystem
4. Set up parallel development environments
5. Start with Agent 0 for your first change

Best Practices

• Start small: Use Agent 0 for simple changes to learn the flow
• Preserve context: Never summarize or paraphrase requirements
• Trust the process: Let each agent complete before moving on
• Visual verify: Always check screenshots before marking complete
• Learn from patterns: Agent 6 makes future work faster

Lessons Learned

After hundreds of iterations, the key insights:

Context is everything: Even a 15-30% performance drop from lost context compounds into hours of wasted work.

Specialization beats generalization: Each agent doing one thing well beats one agent trying to do everything.

Write everything down: If it's not documented, it doesn't exist for the next agent.

Verify early and often: Catching issues during planning is 10x faster than fixing them in code.

Preserve working code: The #1 AI mistake is replacing functionality with placeholders. Agent 4's surgical precision prevents this.

Beyond the Tool

This isn't just about making AI coding faster. It's about creating reliable, repeatable processes that turn AI from an unpredictable assistant into a systematic development pipeline.

The context preservation problem affects every team using AI for development. This workflow is my solution, battle-tested on real projects, refined through actual implementation pain.

If you're tired of re-explaining requirements, debugging AI confusion, and losing hours to context fragmentation, this system will change how you work.

Real-world results prove the impact: Causaly achieved 90% reduction in manual review time, while implementing similar multi-agent workflows shows 40-60% cost reduction and 30-50% faster completion.

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Need help transforming your AI-generated prototype into production-ready code? Let's talk.