Engineering in the AI era
The rules are changing. Story sizing, feedback loops, team structure, architecture, and how we verify business value — all of it needs to evolve.
Software engineering practices were designed for a world where building was expensive and slow. Every methodology — Agile, Scrum, SAFe, Kanban — was created to manage the constraints of human-speed development.
AI is dissolving these constraints. When a task that took a sprint takes a day, the entire planning and estimation model breaks. When you can prototype three approaches in the time it used to take to debate one, the decision-making process needs to change.
This methodology section documents the new operating model for engineering teams. It's not theoretical — it's based on building AI-first products and directing agentic AI systems for Fortune 500 companies. The patterns are real. The practices are tested.
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