[Silhouette]

Also, what does "code quality" even mean in the age of agentic dev? If the code works, and is secure, what else matters?

AI pricing is mostly based on tokens consumed right now. Shouldn't that mean being able to quickly and reliably analyse existing code and to make only small local changes to implement new functionality is as valuable as ever - if not more so - if you're relying on agentic LLMs to do the grunt work?

A lot of things about writing clear specs and developing systematically and employing lots of different kinds of checks and controls to ensure quality and performance have always been true but used to get brushed under the carpet by a lot of cheap/lazy development teams. If LLMs really do accelerate everything about development - including negative behaviours like acting undesirably based on flawed or ambiguous information and doubling down on mistaken assumptions - then the pattern across all of these areas is that doing things the right way is more important than ever if you want to get good results from AI assistance.