by Stefan Wolpers|FeaturedAgile and ScrumAgile Transition
TL;DR: The AI Definition of Done
Your team has a Definition of Done for a product increment. It has none for the 20-plus AI-supported outputs that leave the team each week: status reports, stakeholder emails, release notes, and updates for the C-level. Each one carries your team’s name. “I know quality when I see it” is the standard most teams actually run by, and you cannot audit it, teach it to a new colleague, or defend it when a claim turns out to be wrong. The AI Definition of Done fixes that with one page per task class, agreed by the team, before the output ships.
Your team ships AI outputs that nobody fully trusts; you needed to be quick, and “dirty” tagged along. That ungoverned automation becomes AI debt the moment a stakeholder asks who owns it. The AI Delegation Lifecycle turns six agile skills you already practice into six explicit decisions that govern delegated AI work and produce audit-ready evidence without a separate report.
TL;DR: Compounding Systems and Agents Go Hand-in-Hand
Every AI conversation starts from zero because the model forgets who you are. The Claude Cowork Online Course teaches you to change that: build persistent Skills, connect your tools, and assemble agents for recurring work. No coding.
Thesis: “A prompt disappears after one use; a Skill compounds across every session.”
Eric Ries’ new book ‘Incorruptible’ solves a problem most readers will not face for years: protecting a valuable organization from capture once it succeeds. The builders that AI is creating hit an earlier issue: Building software used to force the question of whether it was worth building. That gate has largely collapsed. Eric Ries asks how mission survives success; we, the normals, how judgment survives abundance.
Thesis: Ries’s Incorruptible solves a later problem, protecting a valuable organization from capture; cheap building created an earlier one, where judgment about what is worth building is the only gate left. That is the problem this article addresses.
TL;DR: Write As Little Code As Possible and Agentic Coding
Agentic coding tools have collapsed the friction of producing plausible software; output is no longer an issue. However, they have not collapsed the friction of knowing what is worth building, whether it fits the system, or whether users will change their behavior because of it, the much-desired outcome. When generating plausible code becomes cheap, every hour spent building the wrong thing becomes waste that can now be produced at scale. Discovery, validation, product judgment, and verification are what stand between your team and creating expensive waste at high-speed.
Thesis: AI made generating code cheap enough that weak product judgment can now scale. That is the problem this article addresses.
TL;DR: Why A Former Micromanager Will Make AI Adoption Work
Twenty years of agile coaching failed to fix the micromanager who meddles with every draft, every meeting, every decision. This article shows where their distrust stops damaging teams and starts producing the verification work AI adoption actually needs. Welcome the Verification Architect!
