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Wardley Maps in AgentArmy

Strategic mapping of competitive landscapes and organizational positioning using Wardley analysis.

What is a Wardley Map?

A Wardley Map visualizes four key dimensions of strategy:

  1. User Needs — What the user fundamentally wants
  2. Value Chain — Components/capabilities required to meet needs
  3. Evolution — Where each component sits in its lifecycle (Genesis → Commodity)
  4. Doctrine — Universal principles guiding decisions
  5. Climate — External forces shaping strategy

Generating Wardley Maps

Use the /wardley skill in Claude Code to automatically generate strategic analysis:

/wardley social media platform
/wardley fintech payment system
/wardley enterprise software infrastructure

Example: Social Media Platform

Value Chain Decomposition

graph TD
    A["User Need: Share<br/>content with friends"]
    A --> B["Discovery"]
    A --> C["Content Management"]
    A --> D["Social Features"]
    A --> E["Monetization"]

    B --> B1["Search"]
    B --> B2["Recommendations"]
    B --> B3["Trending"]

    C --> C1["Upload"]
    C --> C2["Storage"]
    C --> C3["Processing"]

    D --> D1["Notifications"]
    D --> D2["Messaging"]
    D --> D3["Comments"]

    E --> E1["Ads"]
    E --> E2["Subscriptions"]
    E --> E3["Sponsorships"]

Evolution Positioning

Genesis        Custom         Product        Commodity
(Novel)        (Bespoke)      (Packaged)     (Utility)
   |              |               |              |
   v              v               v              v

        Recommendation Engine
        (Differentiation)

                                Upload (HTTP)
                                Storage (AWS S3/GCS)
                                Compute (Kubernetes)
                                Database (PostgreSQL)

Key Strategic Insights

Doctrine Alignment

  • Focus on user outcomes — Engagement metrics
  • Know your users — Behavior patterns, demographics
  • Understand competition — TikTok, Instagram, Threads
  • Design for ubiquity — Cross-platform, accessibility
  • Embrace new technologies — ML recommendations, video

Competitive Climate

  • Regulation — Data privacy (GDPR, CCPA), child safety
  • Market Competition — TikTok dominance, Instagram decline
  • Technology Shift — AI-driven recommendations, video-first
  • User Behavior — Declining text, rising video, ephemeral content
  • Monetization — Ad market consolidation, subscription adoption

Strategy Recommendations

Invest in Commodity

  • Migrate custom infrastructure to cloud (Genesis → Commodity)
  • Use managed PostgreSQL, S3, Kubernetes instead of self-managed
  • Focus engineering on differentiation, not infrastructure

Differentiate at the Edge

  • Recommendation algorithm — Proprietary, ML-driven
  • User experience — Fast, intuitive, mobile-first
  • Community features — Unique interactions, creator tools
  • Safety & moderation — AI-powered, fast response

Consolidate & Reduce Complexity

  • Monolith (Genesis) → Microservices (Custom) → Serverless (Product)
  • In-house ML → Use CloudML, Vertex AI, SageMaker (Commodity)
  • Custom indexing → Elasticsearch, OpenSearch (Product)

Using Wardley Maps in AgentArmy

For Architecture Decisions

  1. Run /wardley [domain] to analyze landscape
  2. Identify commodity vs. custom components
  3. Document decision in Architecture Decision Record (ADR)
  4. Reference Wardley analysis in rationale

For Roadmap Planning

  1. Map current state (what we have)
  2. Map desired state (where we're going)
  3. Identify gaps and investments needed
  4. Prioritize based on strategic importance + evolution gap

For Competition Analysis

  1. Map competitor value chains
  2. Identify differentiation points
  3. Find commodity shifts creating opportunity
  4. Anticipate future moves

Ward

ley Map Example: Data Pipeline Architecture

User Need: Transform raw data into insights
    |
    v
Components:
- Data Extraction (APIs)        → Product/Commodity (dlt, Fivetran)
- Data Storage (Data Lake)      → Product/Commodity (S3, GCS)
- Data Warehouse (Analytics)    → Product (Snowflake, BigQuery)
- Transformation (ETL/ELT)      → Custom (dbt, Airflow)
- Visualization (BI)            → Product (Looker, Tableau)
- ML Models (Predictions)       → Custom (scikit-learn, TensorFlow)

Strategy:
- Use commodity extraction tools (dlt)
- Use managed data warehouse (BigQuery)
- Build custom transformations (dbt)
- Buy BI tool, don't build

Creating Your Own Wardley Map

Step 1: Define User Need

What does the user fundamentally want?
Example: "Share photos with friends"

Step 2: List Value Chain

User Need
├── Photo Capture
├── Upload
├── Storage
├── Discovery
├── Sharing
└── Monetization

Step 3: Position on Evolution

Genesis (Invent)  → Custom (Build)  → Product (Consume)  → Commodity (Integrate)

Step 4: Assess Doctrine

  • User outcomes over features
  • Know your competitive landscape
  • Invest in differentiation, commodity outsource

Step 5: Analyze Climate

  • Regulatory environment
  • Technological shifts
  • Market competition
  • User behavior changes

Resources