AI coding agents nail the code. They break on the scaffolding.

Signup here for the newsletter to get the weekly digest right into your inbox. When AI coding agents fall short, the failure is almost never "it generated bad code." The failure is everything else — the environment state, the implicit decisions baked into a config three years ago, the reason that dependency is pinned to that version. Joel Andrews published one of the more grounded take-downs of AI coding agents this week. Not a hype piece in either direction — just a practitioner's account of where these tools consistently fall apart in real production environments. The pattern he describes maps to something Daniel Miessler flagged in his April AI synthesis: most of what developers call "work" was actually maintaining an elaborate, fragile state required for work to happen. The AI exposed it by stumbling directly into it. This is what "scaffolding" means in practice. Your agent can write a correct function. It cannot know why your test suite is configured with that specific mock, or wh