Agentic Design

Patterns
💬

Conversational Interface Patterns(CIP)

Advanced conversational UI/UX patterns that move beyond traditional chatbots to agent-driven, multimodal experiences

Complexity: highUI/UX & Human-AI Interaction

🎯 30-Second Overview

Pattern: Agent-driven conversational interfaces that move beyond traditional chatbots to multimodal, adaptive experiences

Why: Enables natural human-computer interaction, reduces cognitive load, and provides accessible interfaces for diverse users

Key Insight: Intent-based outcome specification with seamless multimodal transitions - conversation becomes the universal interface

⚡ Quick Implementation

1Define Persona:Create distinct system and user personas with clear roles
2Design Flow:Map conversation paths with branching and error handling
3Implement Intent:Build intent-based outcome specification interface
4Add Multimodal:Integrate voice, text, visual, and gesture inputs
5Test & Refine:Iterate on user testing with diverse conversation scenarios
Example: persona_design → flow_mapping → intent_interface → multimodal_integration → user_testing

📋 Do's & Don'ts

✅Design for natural human conversation patterns first
✅Create clear system persona with consistent personality
✅Use machine-appropriate language like "processing" not "thinking"
✅Implement progressive disclosure for complex information
✅Provide multiple conversation recovery options
✅Enable seamless modality switching (voice/text/visual)
❌Anthropomorphize AI with human emotions or consciousness
❌Force single-turn interactions for complex tasks
❌Hide AI limitations or pretend system is human
❌Use endless scrolling chat for all interaction patterns
❌Ignore accessibility and diverse user communication needs

🚦 When to Use

Use When

  • • Natural language interaction is primary UX goal
  • • Complex task guidance needed
  • • User education and onboarding scenarios
  • • Accessibility across diverse abilities required
  • • Multi-step collaborative workflows

Avoid When

  • • Simple CRUD operations suffice
  • • Real-time precision control needed
  • • Visual data manipulation required
  • • Users prefer traditional GUI patterns

📊 Key Metrics

Conversation Completion Rate
% users completing intended task flow
Intent Recognition Accuracy
Success rate of understanding user goals
User Satisfaction Score
CSAT rating vs traditional interface
Error Recovery Success
% failed conversations successfully recovered
Modality Switching Smoothness
Friction in voice/text/visual transitions
Accessibility Compliance
WCAG 2.1 AA adherence across modalities

💡 Top Use Cases

Customer Support: Natural troubleshooting → guided resolution → escalation handoff
Educational Tutoring: Adaptive questioning → conceptual explanation → progress tracking
Healthcare Assistance: symptom collection → triage guidance → appointment coordination
Enterprise Workflows: task initiation → step-by-step guidance → completion verification
Content Creation: brainstorming → structured authoring → collaborative editing

Pattern Relationships

Discover how Conversational Interface Patterns relates to other patterns

Prerequisites, next steps, and learning progression

Prerequisites

No prerequisites needed - great for beginners!

Next Steps

(3)
🎤
Multimodal Interaction Patterns
highui ux patterns

Advanced integration of voice, visual, gesture, and text communication

💡 Natural evolution to richer, more accessible interaction modalities

🎯
Adaptive Interface Patterns
highui ux patterns

Dynamic UI adaptation based on user context and behavior

💡 Personalize conversations based on user patterns and preferences

🤝
Agent Collaboration UX
highui ux patterns

Multi-agent coordination with transparent handoffs

💡 Scale conversations to specialized multi-agent workflows

Alternatives

(2)
💭
Chat Interface Patterns
highui ux patterns

Traditional chat interfaces with threading and rich media

💡 More structured approach when conversation flow is predictable

📋
Progressive Disclosure UI Patterns
highui ux patterns

Gradual information revelation without conversation

💡 Better for complex information when conversation adds friction

Industry Applications

Healthcare

Patient-centered conversational interfaces for symptom assessment and care coordination

👤Human-in-the-Loop
🔒Privacy and Security UX

Education Technology

Adaptive tutoring systems with natural conversation-based learning

🎯Adaptive Interface Patterns
🎤Multimodal Interaction Patterns

Enterprise Software

Natural language interfaces for complex business workflows and data analysis

🤝Agent Collaboration UX
🔍Trust and Transparency Patterns

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Designing Dialogic Disclaimers: Principles for Ethical Conversational Design in LLM Interfaces (CHI 2024)

Building Conversational User Interfaces: An Architectural Exploration with Meta Glasses (ACM CUI 2024)

Survey of User Interface Design and Interaction Techniques in Generative AI Applications (ArXiv 2024)

Key Principles for User Experience Design for Conversational User Interfaces (ResearchGate 2023)

Industry Guidelines

Google Conversation Design Principles - What is Conversation Design

Microsoft Generative AI UX Guidance for ISVs (2024)

Microsoft Bot Framework Conversational UX Design Principles

Nielsen Norman Group: AI User Interface Paradigms (2024)

Research Publications

10 Guidelines for Developers of Conversational AI (Microsoft Research)

Microsoft Human-AI Interaction Design Guidelines

Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework

Designing For Conversational User Interfaces (ArXiv)

Tools & Platforms

Google Actions Console - Conversation Design Tools

Microsoft Bot Framework SDK and Composer

Amazon Alexa Skills Kit - Voice Design Guide

Rasa Open Source Conversational AI Framework

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