When we let AI agents work on complex tasks, we often want to know whether to step in and check or change what they're doing. This is hard because the effect of an intervention depends on what happens later in the task. The authors treat this as a causal question: does intervening at a particular moment actually improve the final outcome?
They develop a method that works by randomly deciding to intervene or not at each decision point during many agent runs. From this data, they estimate how beneficial different intervention policies would be, even policies they didn't test directly. The method does not need to look inside the AI, just at what the agent proposes and what happens next.
The main finding is that intervention effects vary greatly across different actions and states, meaning a one-size-fits-all review policy is likely suboptimal. This matters because it provides a principled way to prioritize which agent decisions to audit or correct in real-world deployments, improving safety and reliability without slowing everything down.