> why, or, what is meant by More errors caught at compile time means an agent can quickly check their work statically without unit and other tests.

Thus this desparate "AI native" marketing is probably necessary to even be considered relevant in an "agentic" world. Whether it's enough, only time will tell.

So, agents tend to do better the more feedback they can get. Type checking is pretty good for catching a bunch of dumb mistakes automatically.

The point is more hints for the agent is more better most of the time.

Python+ruff+pycheck and TypeScript are compiled to bytecode instead of machine code. They’re not statically typed in the Rust sense. And yet, I’ve watched model crank out good, valid in both of those without needing to be either strictly “compiled” or “statically typed”. Turns out AI couldn’t care less about those properties as long as you have good tooling to quickly check the code and iterate.

Regarding compilation and static typing, it's extremely helpful to be able to detect issues at compile time when doing agentic programming. That way, you don't run into as many problems at runtime, which of course the agent has more difficulty addressing. Unit tests can help bridge the gap somewhat but not entirely.

What's not stated on their website is that Mojo is likely a bad choice for agentic programming simply because there isn't much Mojo training data yet.