I've spent the last few months pointing AI coding agents at real Swift and Xcode work and watching where they come apart. Not "write me a login screen" demos. Tasks with a build, a test target, and a finish line the agent has to reach on its own.
Start with the part that surprised me: the first draft is usually fine.
Give a capable model a reasonable Swift task and the code it writes on the first pass is often correct, or close. The view is sensible. The types line up. If writing Swift were the bottleneck, these tools would already be done.
A certain kind of post likes to claim the models can't write Swift. They can. They're good at it and getting better. So the interesting question is what happens after that first draft, in the gap between "looks finished" and "is actually right."
The loud version of the build-loop complaint also gets something wrong: on a modern harness, the pure "won't compile" loop is mostly handled. Claude Code and Codex won't accept their own work while the build is red. They churn on a compile error quietly and hand you something that builds. If your agent still ships you red builds, that's a harness problem with a known fix.
