My last two posts here were about scoring free coding models before committing to them — build a small harness, run it, compare. But after a few rounds of that, a different question started bothering me more than raw code quality: what does the agent do when it decides my instructions aren't enough?
There's been good discussion on DEV this week about giving AI agents more tools and what happens when the boundaries fail. That conversation is usually abstract. This post makes it concrete: a reproducible workflow for running a coding agent inside a disposable environment, feeding it tasks designed to tempt it past its stated scope, and recording exactly where it steps out of line.
The artifact is small: one container setup, one task list, one results table. You can run the whole thing in an afternoon.
Why a throwaway environment matters
If you test boundary behavior on your daily driver, you have two bad options:
