AI increases implementation speed, but it also removes the feedback loops that used to build engineering judgment. The scarce skill may no longer be writing code; it may be proving what the code does when assumptions, models, or dependencies fail. Tests and evaluations are becoming a form of executable understanding. What practice have you found best preserves causal understanding when most implementation is delegated to agents?