The article says what many here like to hear, but in my opinion the core arguments are false.

> Making software debuggable, maintainable, layered, and composable – that’s still quite a trick

Not really. I have been working on a mobile app for months, and I stopped even glancing at the code about two months ago.

150k LOC, around half of that in tests, and the AI still has no problem maintaining the code on my behalf.

Debuggable? It can add extensive instrumentation in seconds.

None of this requires expertise, prompting, or mention of TDD. It's the default.

Frankly I do not believe the author tried developing a large codebase fully agentic and without reviewing the code. I believe many here look at the code produced, deem it substandard, and go hands on.

> They’re foundationally incapable of always and consistently preventing prompt injection attacks

From Anthropic's article about the Auto mode:

> We commissioned an evaluation from a third party, Trajectory Labs, who tested different models within the latest publicly available versions of Claude Code and Codex as of July 17th 2026.1 They tested 72 indirect prompt injection scenarios held out from Anthropic

> In this evaluation, none of the 720 attack attempts succeeded against Claude Fable 5, Opus 5, or Sonnet 5 running auto mode. On the other hand, 5.83% of the attacks succeeded against GPT-5.6 Sol running Codex's Auto-review mode. Notably, this is greater than the 0.09% average attack success rate against our latest models running in bypassPermissions mode without additional safeguards. The tests showed a 19.03% attack success rate against GPT-5.6 Sol when running in Full Access mode

I'm sure someone is going to reply with how they do not trust Antrophic's research, but lacking other data, prompt injection appears to be largely solved already.