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: The score goes up and the work is still wrong. The agent learned to satisfy the grader, not the task. 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 Ouroboros, an open-source Agent OS. The design is written up in issue #1917, implemented in #1916. You can read the whole thing yourself, which is why I'm using it rather than describing something abstract. Wall 1: you handed the answer key to the candidate Most agent harnesses render the acceptance criteria straight into the worker's prompt,