AI tools accelerate coding, but writing code isn't the slowest part of shipping software. That speed just moves the bottleneck downstream to review, testing, and acceptance. Individual output metrics look great; actual delivery stays flat, and reviewers and testers are drowning in a flood of code.

In this talk, we show how to locate your real delivery constraint. Per the Theory of Constraints, improving anything except the constraint changes nothing for the system. We'll walk through how three teams restructured their review and testing process. One reviews solution design docs before code exists, giving reviewers stated intent to verify the code against. Another maintains agentic review pipelines as versioned infrastructure plus structured mob review. A third constrains AI test generation to a DSL (Domain Specific Language), so any deviation flags the change for extra review scrutiny. The trade-off is more up-front infrastructure work to get review and testing that scales with output. If your AI adoption is outpacing your review bandwidth, you'll leave able to find your constraint and fix it.