GitHub Companion Repository

What You'll Find

This repository contains practical AI agent examples and tools for change management. The main components are:

• AgentExamples/: Jupyter notebooks demonstrating AI use cases in change management
• QUICK_START.md: Quick start guide for getting started with the notebooks
• requirements.txt: Python dependencies for the project
• static/: Static assets including the overview diagram

The Jupyter notebooks in the AgentExamples/ directory are practical illustrations of AI use cases in change management at both the individual and organizational levels. These notebooks demonstrate how AI agents can be deployed to support the frameworks and tools described in the book:

• Individual-Level Tools: Notebooks like psychological_safety_coach.ipynb and socratic_partner.ipynb illustrate how AI can serve as a developmental tool for personal growth, providing coaching, Socratic questioning, and reflection support that helps individuals expand their capacity for complexity and ambiguity. Notebooks like interestingness_evaluator_agent.ipynb and left-right-hemisphere-analyzer.ipynb show how AI can support complex analysis, idea evaluation, and multi-perspective thinking that enables better decision-making in uncertain environments. The immunitytochange.ipynb notebook implements Robert Kegan's Immunity to Change framework to help individuals identify hidden barriers and competing commitments that prevent meaningful change. The contrarian_intelligence.ipynb notebook challenges conventional wisdom and identifies hidden assumptions, helping individuals and organizations surface alternative perspectives on complex topics.
• Organizational-Level Tools: Notebooks like multi_agent_conversation.ipynb and focus_group_agent.ipynb demonstrate how AI can facilitate organizational decision-making, simulate stakeholder perspectives, and help diagnose resistance across formal, social, and mental contexts. Notebooks like analogy_finder_agent.ipynb illustrate how AI can help generate cross-disciplinary insights and reframe problems, supporting the kind of creative thinking needed to navigate complex change. The contrarian_intelligence.ipynb notebook challenges conventional wisdom and identifies hidden assumptions underlying organizational narratives, helping surface alternative perspectives that can reveal blind spots in strategic thinking. The resistance_to_change_analyzer.ipynb notebook analyzes user adoption patterns and resistance to change initiatives, providing data-driven insights and actionable recommendations for different user segments. The social_physics_analyzer.ipynb notebook examines how information flows through social networks and how collective intelligence emerges from individual interactions, using network analysis and social physics principles.
• Societal-Level Tools: The notebook dilemma_resolution.ipynb uses LangChain's deep agent framework to implement Charles Hampden-Turner's Dilemma Surfacing techniques for resolving complex organizational or societal dilemmas through multi-stakeholder simulation. The notebook scenario_planning.ipynb provides a comprehensive scenario planning exercise for analyzing societal-level challenges, using established frameworks like Shell's Scenario Planning Method and STEEP analysis to systematically explore future uncertainties and generate actionable scenarios. The survey_simulation.ipynb notebook demonstrates how to simulate social science experiments and surveys using large language models, allowing you to simulate survey responses across different segments (countries, demographics, time periods) and compare results with reference survey data.

Each notebook is a working example that you can run, modify, and adapt for your own change management challenges. They use LangChain's deep agent framework to build sophisticated AI agents that go beyond simple chatbots to provide structured, multi-step reasoning and analysis.