AI agents need tests that catch success and shortcuts
need and like other . The difference is that an does not just repeat fixed steps; it makes choices, so its behavior can be hard to predict. That means it may not pass the same tests every time, and it is not obvious how to measure whether it has really improved.
A useful approach is to build a general interactive environment for . That environment should show whether the agent truly completed the task, and it should also show whether the agent used an unintended or flaw to look successful.
Key points
- need , but their results can vary more than normal .
- Simple pass-or-fail tests may not prove that an agent has improved.
- A good setup should check whether the task was actually completed.
- The setup should also catch unintended s or flaws used by the agent.
- Cost-cutting changes need reliable before they can be trusted.