And I have the AI deal with "knowing how to do it" as well. Often it's slower to have it do enough research to know how to do it, but my time is more expensive than Claude's time, and so as long as I'm not sitting around waiting it's a net win.
Gemini running an benchmark- everything ran smoothly for an hour. But on verification it had hallucinated the model used for judging, invalidating the whole run.
Another task used Opus and I manually specified the model to use. It still used the wrong model.
This type of hallucination has happened to me at least 4-5 times in the past fortnight using opus 4.6 and gemini-3.1-pro. GLM-5 does not seem to hallucinate so much.
So if you are not actively monitoring your agent and making the corrections, you need something else that is.
Also instead of just prompting, having it write a quick summary of exactly what it will do where the AI writes a plan including class names branch names file locations specific tests etc. is helpful before I hit go, since the code outline is smaller and quicker to correct.
That takes more wall clock time per agent, but gets better results, so fewer redo steps.
This is exactly the sort of future I'm afraid of. Where the people who are ostensibly hired to know how stuff works, out source that understanding to their LLMs. If you don't know how the system works while building, what are you going to when it breaks? Continue to throw your LLM at it? At what point do you just outsource your entire brain?
> Continue to throw your LLM at it?
Increasingly, yes. If you have objective acceptance criteria, just putting the LLM in a loop with a quality gate tends to have it converge on a fix itself, the same way a human would. Not always, and not always optimally, but more and more often, and with cheaper and cheaper models.
I also tend to throw in an analysis stage where it will look at what went wrong and use that to add additional criteria for the next run.
I'm actually working on a project now where the biggest problem I need to solve is that the verifier that reviews the test harness is too strict.
Yesterday Gemini burned 40 minutes trying to diagnose a failed Expo build and going into loops of changing the Podfile and re-running the build, when the issue was that Xcode needed updating (quick Google search for it).
But my comment on burnout stands. The lack of downtime and dynamic thinking modes (admin, planning, review, actual coding) seems like it would conspire to make you either cram out more work or disconnect from it. Both of these become dangerous, after a while.
(Information workers were productive 4–6 hours a day, and the economy did just fine.)
