I think it depends a lot on what you're trying to get them to do and what you're working on. I don't really buy into the whole "prompt engineering" part. I use agents quite a lot, but they have limitations. They excel at writing SQL, kubernetes configuration, bicep and similar... at least so long as you know what you're doing. I also have quite a lot of success with them keeping documentation and tests updated. I wouldn't recommend using them for your tests unless tests are a nice to have feature for you because you actively deal with corrupted states through explicit error handling, but we do.
In my experience every model absolutely sucks at doing any form of software engineering. The thing is though, you don't need to do any software engineering to write software... until you do. Since LLM's got decent at programming we've let them loose among our BI and solar plant engineers and they're building some really useful tools in Python. They have no idea what they are doing, and they are still getting stuff to work. It's obviously absolute horse shit, but for a lot of it, the shit code will be "good enough" for nobody having to touch it for it's lifecycle. For the rest, actual software engineers take over.
If you're working in a field where "good enough" is fine, then I imagine that LLM's can do a lot for you. It might sound like I'm being disrespectful, but I really don't mean to be. In my decades of experience with software in enterprise settings, I've seen so many programs build with horrible code just trundle along. Creating great value for the business while requiring no to minimal maintenance. Bad from a traditionalist software engineering perspective, but great from a business perspective (which is what matters). Not everything needs to scale after all. Anyway, I do question whether the author has actually worked with LLM's a lot. I would think that the strengths failings of LLM's would get obvious rather quickly to anyone who uses them.