alt Hacker NewsRight, but this just seems like underspecification. In my experience as both a team leader and an "agentic engineer" (ugh), I try to blame myself for the lack of clarity of my asks, rather than the person/agent for making the "wrong" choice.
I'm sure plenty of meat humans out there would make the same mistake (sorry, you said to use local storage boss!). You might give them a scolding. And maybe document that policy. Maybe in a markdown file for the next person. IME the latest models are significantly better than the median engineer at following this feedback.
I don't think it's fruitful to blame the LLM any more than it is to blame someone working under you.
In fact I would say this is an excellent example of how engineering does NOT fundamentally change in the era of AI.
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>I try to blame myself for the lack of clarity of my asks, rather than the person/agent for making the "wrong" choice.
I noticed this phenomenon way before LLMs came along. Some people would put a halo around a particular technology and blame everything around it when something went wrong.
Yes but I always have to be on the lookout for this meta pattern that leads to code bloat.
In this case things mostly sorta worked and the simplest way to see the problem was look at the code. And try to take it apart a bit to see where the problem was.
I felt I arrived at a better pattern I could trust that the agent could use much more efficiently this way than asking the agent to do it. I could then test that the pattern was being adhered to and therefore better trust the agent not to go off the rails.
I personally internalized the details a lot better by doing this writing. I wouldn’t have internalized it - or more likely played whack-a-mole - by guiding an agent.
How do I arrive at the patterns to check for without exploring the code? And capturing a real failure case?