Contributors · 6
- Sherif Koussa
- Travis Cronin
- Sergey Bobryka
- Damien Mourot
- John Moore
- Nick Balnaves
Require Plain Rationale on Risky Changes
The most effective AI guardrails start with the understanding that code quality is not only about syntax, coverage, or passing builds. Quality also means predictability under pressure, especially when systems handle customer data, privilege boundaries, and audit evidence. Generated code can move quickly, but speed becomes expensive when teams inherit logic they cannot explain, defend, or safely modify six months later.
A policy I used was requiring a plain language rationale for every AI assisted change above a set risk threshold. Engineers had to explain what the code did, why it was safe, and what assumptions it relied on. That preserved delivery pace, improved reviewer focus, and exposed fragile patterns early, before they became recurring security debt.
