PACE Pattern

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Research & Publications ​

Academic and technical research on the PACE Pattern.

Overview ​

The PACE Pattern is grounded in research spanning:

  • UX Design — Conversational interfaces and user experience
  • AI Systems — Agentic design patterns
  • Cognitive Science — How users process information
  • Biological Inspiration — Cormorant foraging behavior

Official Publications ​

PACE Pattern v1.0.1 (2024) ​

DOI: 10.5281/zenodo.18049371

Title: Pattern for Agentic Conversational Experience (PACE)

Author: Michael Shatny / SemanticIntent

Published: December 23, 2024

Platform: Zenodo

Abstract:

The PACE Pattern (Pattern for Agentic Conversational Experience) is a UX architecture pattern for building AI-guided interfaces where conversation replaces traditional navigation. Instead of browse-and-click interactions, users engage with an intelligent guide that understands intent and surfaces relevant options through natural dialogue.

This paper presents the pattern's four core principles (Proactive, Adaptive, Contextual, Efficient), its four essential components (Product, About, Chat, Executive Summary), and the biological inspiration drawn from cormorant foraging behavior.

Citation:

bibtex
@misc{pace_pattern_2024,
  author       = {Michael Shatny},
  title        = {Pattern for Agentic Conversational Experience (PACE)},
  year         = {2024},
  month        = {December},
  publisher    = {Zenodo},
  version      = {1.0.1},
  doi          = {10.5281/zenodo.18049371},
  url          = {https://zenodo.org/records/18049371}
}

View Publication →

Research Themes ​

1. Conversational UX ​

Key Questions:

  • How do users prefer to discover products: browsing vs. conversation?
  • What makes a good conversational guide?
  • How can AI adapt to different user expertise levels?

Relevant Research:

  • Conversational commerce
  • Dialog systems
  • User intent recognition
  • Natural language interfaces

2. Agentic Design ​

Key Questions:

  • What defines an "agentic" interface?
  • How proactive should AI guides be?
  • When should systems initiate vs. wait?

Relevant Research:

  • Autonomous agents
  • Recommendation systems
  • Proactive assistance
  • Human-AI collaboration

3. Cognitive Load ​

Key Questions:

  • Does conversation reduce cognitive load vs. browsing?
  • How many options should guides present?
  • What's the optimal conversation flow?

Relevant Research:

  • Information overload
  • Decision paralysis
  • Choice architecture
  • Attention economics

4. Biological Inspiration ​

Key Questions:

  • How do animal foraging strategies map to UX patterns?
  • What can we learn from cormorant hunting behavior?
  • How does pursuit diving relate to intent discovery?

Relevant Research:

  • Optimal foraging theory
  • Cormorant hunting intelligence
  • Bio-inspired algorithms
  • Behavioral ecology

Explore Cognitive Drift Research →

Design Principles Paper ​

A comprehensive 1,500+ line technical document covering:

  • Philosophy and core beliefs
  • Architectural patterns
  • Component design
  • Extensibility strategies
  • Framework comparisons
  • Implementation guidelines

Read Design Principles →

Conversational Commerce ​

  • Amazon Alexa Shopping — Voice-based product discovery
  • Google Shopping Assistant — AI-guided shopping
  • Shopify Inbox — Conversational customer service
  • Drift — Conversational marketing platform

How PACE differs:

  • Guide-first from initial landing
  • Pattern-driven (not platform-specific)
  • Open source framework
  • Biological inspiration

Agentic Systems ​

  • AutoGPT — Autonomous AI agents
  • LangChain Agents — AI agent framework
  • Microsoft Copilot — AI assistant
  • Claude Projects — Contextual AI assistance

How PACE differs:

  • UX pattern (not just AI capability)
  • Designed for product discovery
  • Four specific components
  • Executive summary for meta-awareness

Recommendation Systems ​

  • Netflix — Personalized recommendations
  • Spotify Discover — Music discovery
  • Amazon Recommendations — Product suggestions
  • TikTok For You — Content discovery

How PACE differs:

  • Conversational (not algorithmic alone)
  • User controls discovery through dialogue
  • Explains recommendations
  • Adapts to user expertise

Metrics & Studies ​

User Engagement ​

Future research areas:

  • Time to decision — How long to find the right product?
  • Conversation depth — How many messages exchanged?
  • Conversion rate — Browse vs. conversation
  • User satisfaction — NPS scores, feedback

Cognitive Load ​

Research questions:

  • Does PACE reduce mental effort vs. traditional browsing?
  • How many product options should guide present?
  • What's the optimal conversation flow?

Adaptation Effectiveness ​

Metrics:

  • Accuracy of expertise detection
  • User satisfaction by expertise level
  • Response appropriateness
  • Conversation coherence

Open Research Questions ​

1. Optimal Conversation Flow ​

Question: What's the ideal conversation structure for product discovery?

Current hypothesis:

Greeting → Intent discovery → Recommendation (2-3 options) →
Follow-up → Decision support

Research needed:

  • A/B testing different flows
  • User preference studies
  • Conversion rate analysis

2. Proactivity Limits ​

Question: How proactive should the guide be?

Spectrum:

Passive ←→ Balanced ←→ Aggressive

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                       SQL MCP might help."

Research needed:

  • User tolerance for proactivity
  • Context-dependent proactivity
  • Cultural differences

3. Multi-Modal PACE ​

Question: How does PACE work with voice, gesture, or AR?

Possibilities:

  • Voice-first PACE (Alexa/Siri)
  • AR product visualization
  • Gesture-based navigation
  • Multi-modal combination

Research needed:

  • Voice UX patterns
  • AR integration
  • Accessibility considerations

4. Cross-Cultural Adaptation ​

Question: How should PACE adapt to different cultures?

Variables:

  • Conversation style (direct vs. indirect)
  • Formality levels
  • Pace of interaction
  • Visual preferences

Research needed:

  • Cultural UX studies
  • Localization patterns
  • Global user testing

Future Publications ​

In Development ​

PACE Pattern v2.0

  • Multi-modal support
  • Enhanced accessibility
  • A/B testing results
  • Production metrics from MillPond

The Cormorant Paper

  • Deep dive into biological inspiration
  • Mapping hunting behaviors to UX patterns
  • Comparison with other bio-inspired algorithms

PACE.js Technical Architecture

  • Framework implementation details
  • Performance benchmarks
  • Comparison with React/Vue/Alpine
  • Best practices guide

Contributing to Research ​

We welcome research contributions:

Academic Researchers ​

  • Conduct user studies on PACE implementations
  • Publish findings (cite PACE Pattern v1.0.1)
  • Collaborate on future versions
  • Share datasets and results

Industry Practitioners ​

  • Share production metrics
  • Contribute A/B test results
  • Document implementation lessons
  • Provide user feedback

Open Source Community ​

  • Build example implementations
  • Create integrations
  • Develop tools and plugins
  • Document use cases

Learn how to contribute →

Citation ​

If you use PACE in your research, please cite:

bibtex
@misc{pace_pattern_2024,
  author       = {Michael Shatny},
  title        = {Pattern for Agentic Conversational Experience (PACE)},
  year         = {2024},
  month        = {December},
  publisher    = {Zenodo},
  version      = {1.0.1},
  doi          = {10.5281/zenodo.18049371},
  url          = {https://zenodo.org/records/18049371}
}

Resources ​

Contact ​

For research inquiries:

Academic rigor meets practical implementation. 📚