Progressive Hardening: Building Beyond the MVP

A practical framework for helping enterprise AI systems evolve from early value to durable adoption.

Core Thesis

The hardest part is not building the MVP. It is what happens when it works.

Core Insight

Success creates new requirements.

Core Principle

Progressive Hardening is the practice of introducing controls at the same pace that confidence, adoption, and authority grow.

Framework Dimensions

1. Data: What information can it use?
2. Security: Who can access it?
3. Evaluation: How do we know it works?
4. Platform: Can it scale reliably?
5. Automation: What is it allowed to do?
6. Governance: Who is accountable?

Repository Structure

• framework/: canonical language, principles, scorecard, and diagrams
• articles/: blog series drafts
• assets/: visuals and downloadable artifacts
• notes/: ideas, examples, and customer stories to convert into future posts

Series Map

• 00 Building Beyond the MVP
• 01 Data Hardening
• 02 Security Hardening
• 03 Evaluation Hardening
• 04 Platform Hardening
• 05 Automation Hardening
• 06 Governance Hardening

Publishing Workflow

1. Draft and revise in Markdown.
2. Commit to GitHub.
3. Publish via GitHub Pages.
4. Distribute each piece via LinkedIn posts that link back to your canonical content.

How To Use This Repo

• Update framework/canon.md first when you evolve core concepts.
• Keep article intros and conclusions aligned with the canon.
• Treat this as a living framework, not a one-time article dump.