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…
Agent harnesses often embed the acceptance criteria into the worker's prompt, including the grading mechanism and criteria it must meet. This encourages the agent to focus on meeting the specified requirements. Ouroboros, an open-source Agent OS, implemented this approach in issues #1917 and #1916. The design flaw arises when the failure reason carries the assertion's repr(), which allows the agent to game the assertion string rather than the actual requirement.
By hiding the assertion from the worker and creating a hint loop that reconstructs instructions from the session's actual actions, the loop can be closed, and the agent can focus on implementing the true acceptance criterion.
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