How did you verify that your AI agent performed the update correctly? I've experienced a number of cases where an AI agent made a change that seemed right at first glance, maybe even passed code review, but fell apart completely when it came time to build on top of it.
> made a change that seemed right at first glance, maybe even passed code review, but fell apart completely when it came time to build on top of it
Maybe I'm not understanding you're point, but this is the kind of thing that happens in software teams all the time and is one of those "that's why they call it work" realities of the job.
If something "seems right/passed review/fell apart" then that's the reviewer's fault right? Which happens, all the time! Reviewers tend to fall back to tropes and "is there tests ok great" and whatever their hobbyhorses tend to be, ignoring others. It's ok because "at least it's getting reviewed" and the sausage gets made.
If AI slashed the amount of time to get a solution past review, it buys you time to retroactively fix too, and a good attitude when you tell it that PR 1234 is why we're in this mess.
> If something "seems right/passed review/fell apart" then that's the reviewer's fault right?
No, it's the author's fault. The point of a code review is not to ensure correctness, it is to improve code quality (correctness, maintainability, style consistency, reuse of existing functions, knowledge transfer, etc).
I mean, that's just not true when you're talking about varying levels of experience. Review is _very_ important with juniors, obviously. If you as sr eng let a junior put code in the codebase that messes up later, you share that blame for sure.
Unit tests, manual testing the final product, PR with two approvals needed (and one was from the most anal retentive reviewer at the company who is heavily invested in the changes I made), and QA.
