This summer I spent quite a while using a coding agent to help me untangle a deep and complicated data processing pipeline. It had itself been built by agents, in a remarkably short amount of time. But it had also become clear that it was riddled with errors and was producing lots of bad data.
What I quickly discovered was that upwards of half of my questions would receive very confidently wrong answers. And even once I had finally diagnosed whatever problem I was currently working on, it was difficult to trust the agent with any bug fixes. Since it was having an even harder time tracing data flows than I was (I'll take this chance to submit for your consideration that faster is not necessarily better), it was proving to be a bit of a monkey's paw. Yes, it would fix the exact bug I asked it to fix, but typically introduce new defects in the process. And yes, I was having this struggle with all of the latest & greatest models.
I ultimately concluded that, in this codebase, the agent was indeed deeply, hopelessly lost. (edit: And probably this code got so bad in the first place because the agents that were used to build it had been lost for a while, but unable to recognize this problem and call their operators' attention to it.)