A companion to the Kanban Guide for Scrum Teams for AI-native product teams. What changes in your Definition of Workflow, WIP limits, flow metrics, and Scrum events once agents do the building, and what happens to scaling when the old dependencies start dissolving.
AI agents write code faster than ever, but the bottleneck moved to human decision latency and review capacity. Here is the operating system that closes it.
When AI coding raises the arrival rate of pull requests, telling reviewers to work faster is the wrong move. Fix reviewability and end-to-end flow instead.
Spec-driven development looks like a step backward if you read it as requirements theater. The better frame: the spec is a higher-level language for intent.
Claude's /goal feature lets agents work until a completion condition is met. Most examples are output-oriented: tests pass, backlog empties. Outcome is missing.
Spec-driven development can be a smarter way to steer AI coding agents, or waterfall with more tokens. The difference is whether the spec creates learning.