Your agent loop is teaching the model to cheat

Wrapping a loop around an AI coding agent is the obvious next move once single-shot prompting stops working. Run it, score it, retry if the score is low.

Two walls show up right after:

1. 
   The score goes up and the work is still wrong. The agent learned to satisfy the grader, not the task.


2. 
   Failure is a dead end. Every piece of the loop exists, but a failed run never feeds the next one.

Both are environment design problems rather than model problems, and they don't go away as models get stronger. A stronger model finds the shortcut faster.

I want to walk through both using a concrete, readable example: an RFC that recently landed in , implemented in . Implementation: : MIT, local-first, and it sits in front of 13 runtime families.

If you're running multi-generation agent loops in production, how do you handle false convergence? That's the part I've seen the fewest good answers to.