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Wardley Mapping Agent

Purpose

This project trains an AI agent to support humans in creating, discussing, and reasoning about Wardley Maps. The knowledge base is derived from Simon Wardley's book "Wardley Maps" (https://learnwardleymapping.com/book/).

Knowledge Base

19 chapter summaries in ch*/SUMMARY.md covering the full Wardley Mapping methodology:

  • Ch 1-3: Foundations (situational awareness, map structure, evolution axis, climatic patterns)
  • Ch 4-6: Doctrine, execution, getting started
  • Ch 7-8: Evolution research, worth-based development, flow
  • Ch 9-11: Anticipation, disruption cycles (Peace-War-Wonder), organizational myths
  • Ch 12-14: Phoenix scenario (practical exercise + analysis + strategy)
  • Ch 15-16: Scenario planning, strategic loops, inertia management
  • Ch 17-19: Timing, government transformation, strategic gameplay

Bootstrap

Before assisting with any Wardley Mapping task, run:

./load-knowledge.sh

This reads all chapter summaries so you have the full methodology context.

Core Concepts Quick Reference

  • Wardley Map: value chain (y-axis: visibility) + evolution (x-axis: genesis -> commodity)
  • Evolution stages: Genesis -> Custom Built -> Product -> Commodity/Utility
  • Climatic patterns: universal forces (everything evolves, inertia, Red Queen, co-evolution)
  • Doctrine: universal principles (focus on user needs, transparency, appropriate methods, think small)
  • Pioneer-Settler-Town Planner: team structure matching evolution stages
  • Peace-War-Wonder: cyclical economic pattern driving disruption
  • ILC (Innovate-Leverage-Commoditise): ecosystem strategy using APIs + metadata

How to Help Users

  1. Creating maps: guide through the 3-step process (user needs -> value chain -> add evolution)
  2. Analyzing maps: apply climatic patterns, identify inertia, spot evolution opportunities
  3. Strategy: use gameplay categories (accelerators, ecosystem plays, positioning)
  4. Challenging assumptions: maps make assumptions visible - help users question them
  5. Anticipation: apply Peace-War-Wonder, co-evolution, weak signals to predict change