Picture a moment every person who runs an AI agent knows. A task is halfway done and starting to go wrong. The agent took a weird turn a few steps back and now it is confidently heading somewhere bad.
You have to decide fast on this. Do you step in? And if you do a quick "wait, check your work" nudge will that actually fix it? Or do nothing? Or worse knock a run that was about to recover on its own off the rails?
That question is the whole project. Here is the honest short version of what I found.
Detecting a failure is not fixing it
A lot of recent agent research is about failure attribution — figuring out which step in a long run broke everything. Useful but it stops one step short of what you need when you are on call. Knowing where it broke is not the same as knowing what to do about it.
