The Day After AI

AI agents are becoming more capable, but many software systems remain difficult for them to navigate. Critical business knowledge is often scattered across conversations, documents, and the minds of experienced team members. This hidden context creates a barrier that many organizations must address before they can fully benefit from AI-assisted development.
At the center of the book is the concept of system legibility: the ability for people, AI agents, and automated workflows to understand how a business operates without constant human explanation. Through a contract-driven architecture model, readers learn how to capture capabilities, dependencies, and business rules in a form that can be shared, governed, and scaled.
The book begins by identifying tribal knowledge as a structural obstacle to AI adoption. It then introduces capability contracts, capability graphs, and the Contract-Driven AI Development (C-DAD) framework. You will learn how to surface hidden knowledge from existing systems, govern contracts through their lifecycle, and create a navigable map of organizational capabilities. The final section focuses on transformation, showing how architects, developers, product managers, designers, and leaders can adapt their roles in a contract-driven organization.
Using realistic examples from both established and emerging software companies, the book offers a practical roadmap for organizations that want to build systems that are easier to understand, evolve, and operate in the age of AI agents.

What You Will Learn

• Identify and measure tribal knowledge debt in software organizations
• Understand system legibility and its impact on AI adoption
• Surface undocumented business rules in brownfield environments without requiring a rewrite
• Build contract-first practices for new and existing systems

Who This Book Is For

Software architects, senior developers, engineering managers, product managers, CTOs, and engineering leaders preparing their organizations for AI-assisted development and long-term system evolution