I think a particular failing with developers embracing AI is fighting the sunk cost fallacy. While you might not have spent as much time putting together a non-working solution, you still did spend time working with the agent to slap together a non-working solution.
Being able to step back and say "this was a failure and we need to discard the day's work and start over" is still hard with LLMs.
If I spent half a day asking an agent to do something and it's a "non-working solution". I can just throw it away. I have sunk close to zero cost in it. I have no emotional attachment to the code.
It's like if I 3D-printed something I haven't modelled myself and the print goes wonky. I don't spend days trying to glue and file it back together. I chuck it in the bin and start a new one.
But if I had handcrafted the same item over multiple days, of course I'd try to salvage it - because there was a sunk cost of me spending time doing it.
Completely disagree. I think this is one of the big wins of agentic engineering. When you look back at your own completed change and realize that you made it too complicated because your initial abstraction was wrong, you have to debate long and hard about whether it's worth going back and redoing the work -- is the abstraction actually that bad? Would you really get a huge win by changing it, enough to justify spending another day on the task?
But with the agent, you know that the change will be relatively quick and easy, so the bar to tell it to shift approaches is much, much lower.