Interesting. I've only dipped my toe in the AI waters but my initial experience with a Go project wasn't good.
I tried out the latest Claude model last weekend. As a test I asked it to identify areas for performance improvement in one of my projects. One of the areas looked significant and truth be told, was an area I expected to see in the list.
I asked it to implement the fix. It was a dozen or so lines and I could see straightaway that it had introduced a race condition. I tested it and sure enough, there was a race condition.
I told it about the problem and it suggested a further fix that didn't solve the race condition at all. In fact, the second fix only tried to hide the problem.
I don't doubt you can use these tools well, but it's far too easy to use them poorly. There are no guard rails. I also believe that they are marketed without any care that they can be used poorly.
Whether Go is a better language for agentic programming or not, I don't know. But it may be to do with what the language is being used for. My example was a desktop GUI application and there'll be far fewer examples of those types of application written in Go.
You need to be telling it to create reproduction test cases first and iterate until it's truly solved. There's no need for you to manually be testing that sort of thing.
The key to success with agents is tight, correct feedback loops so they can validate their own work. Go has great tooling for debugging race conditions. Tell it to leverage those properly and it shouldn't have any problems solving it unless you steer it off course.