Experimentation: Your Secret Weapon For Validating AI-Generated Code

AI coding tools are helping teams produce more features faster, but they are also multiplying what happens after the code is written: more changes to test, more rollout decisions to make and more release risk to manage. Harness’s core thesis is that change is the atomic unit of risk, and as AI increases code volume, practitioners need a better way to make every change measurable, reversible and observable before it reaches everyone.