Loading...
📋
Progressive Disclosure UI Patterns(PDP)
Interface patterns for gradually revealing agent capabilities, reasoning, and complex information to prevent cognitive overload
Complexity: highUI/UX & Human-AI Interaction
🎯 30-Second Overview
Pattern: Gradually reveal information layers to prevent cognitive overload
Why: Users need complex AI reasoning but get overwhelmed by technical details upfront
Key Insight: Start simple, expand on-demand: summary → detailed → technical with clear visual hierarchy
⚡ Quick Implementation
1Layer Info:Define summary → detailed → technical levels
2UI Triggers:Click/expand controls for each level
3Context Aware:Show relevant depth based on user type
4Visual Hierarchy:Clear typography and spacing levels
5State Management:Remember user preferences for disclosure
Example: agent_response → [show reasoning] → [technical details] → [full trace]
📋 Do's & Don'ts
✅Start with essential information only
✅Use consistent expand/collapse icons across UI
✅Implement smooth animations for transitions
✅Remember user disclosure preferences
✅Provide "expand all" / "collapse all" options
❌Show all complex information at once
❌Use misleading or unclear labels for disclosure
❌Nest disclosure levels too deeply (max 3-4)
❌Hide critical information behind multiple clicks
❌Ignore keyboard navigation for disclosure controls
🚦 When to Use
Use When
- • Complex reasoning explanation needed
- • User education and onboarding flows
- • Agent capability introduction
- • Cognitive load management required
Avoid When
- • Simple, single-step responses
- • Critical alerts requiring immediate attention
- • Linear workflows without complexity
- • Time-sensitive decision making
📊 Key Metrics
Cognitive Load
User task completion time
Comprehension
% users understanding agent actions
Engagement
Disclosure interaction rates
User Satisfaction
Preference for progressive vs full disclosure
Error Rate
Mistakes due to insufficient information
Learning Efficiency
Time to understand new capabilities
💡 Top Use Cases
Agent Reasoning: Show summary → detailed steps → technical reasoning trace
Capability Introduction: Basic features → advanced tools → expert features
Error Explanation: What happened → why it occurred → how to fix it
Decision Process: Final recommendation → factors considered → detailed analysis
Onboarding: Core concepts → intermediate features → advanced workflows
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
📋
Progressive Disclosure UI Patterns(PDP)
Interface patterns for gradually revealing agent capabilities, reasoning, and complex information to prevent cognitive overload
Complexity: highUI/UX & Human-AI Interaction
🎯 30-Second Overview
Pattern: Gradually reveal information layers to prevent cognitive overload
Why: Users need complex AI reasoning but get overwhelmed by technical details upfront
Key Insight: Start simple, expand on-demand: summary → detailed → technical with clear visual hierarchy
⚡ Quick Implementation
1Layer Info:Define summary → detailed → technical levels
2UI Triggers:Click/expand controls for each level
3Context Aware:Show relevant depth based on user type
4Visual Hierarchy:Clear typography and spacing levels
5State Management:Remember user preferences for disclosure
Example: agent_response → [show reasoning] → [technical details] → [full trace]
📋 Do's & Don'ts
✅Start with essential information only
✅Use consistent expand/collapse icons across UI
✅Implement smooth animations for transitions
✅Remember user disclosure preferences
✅Provide "expand all" / "collapse all" options
❌Show all complex information at once
❌Use misleading or unclear labels for disclosure
❌Nest disclosure levels too deeply (max 3-4)
❌Hide critical information behind multiple clicks
❌Ignore keyboard navigation for disclosure controls
🚦 When to Use
Use When
- • Complex reasoning explanation needed
- • User education and onboarding flows
- • Agent capability introduction
- • Cognitive load management required
Avoid When
- • Simple, single-step responses
- • Critical alerts requiring immediate attention
- • Linear workflows without complexity
- • Time-sensitive decision making
📊 Key Metrics
Cognitive Load
User task completion time
Comprehension
% users understanding agent actions
Engagement
Disclosure interaction rates
User Satisfaction
Preference for progressive vs full disclosure
Error Rate
Mistakes due to insufficient information
Learning Efficiency
Time to understand new capabilities
💡 Top Use Cases
Agent Reasoning: Show summary → detailed steps → technical reasoning trace
Capability Introduction: Basic features → advanced tools → expert features
Error Explanation: What happened → why it occurred → how to fix it
Decision Process: Final recommendation → factors considered → detailed analysis
Onboarding: Core concepts → intermediate features → advanced workflows
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.