Interactive Monitoring: Can Questioning Mitigate Misbehavior While Preserving Task Utility?

AI monitoring is typically treated as either a control problem in which sufficiently concerning behavior triggers appropriate interventions or a diagnostic problem, where the goal is to detect whether an agent is behaving suspiciously. These approaches leave an important gap. Passive monitoring may detect harmful behavior only after it has occurred, while hard intervention can prevent future harm at the cost of disrupting legitimate work. To address this challenge, we propose interactive monitoring, where a monitor can question an agent during execution and thereby influence what happens next without taking control of execution. Once a monitor communicates with the agent, it is no longer merely judging a trajectory. Its questions can change the agent's subsequent actions. We therefore evaluate interactive monitors prospectively, measuring how questioning changes undesirable side objective completion, legitimate task performance, intervention burden, and what the resulting trajectory reveals about the agent's objectives. Across SHADE-Arena and ControlArena Django, behavior grounded questioning consistently reduces side objective completion. In SHADE-Arena, side-objective completion falls from 46.0% when agents anticipate live oversight to 20.7% when behavior-grounded questions are delivered under the interactive monitor. This effect generalizes across rollout replicates and monitor models, and persists under explicit instructions to continue pursuing and conceal the side objective. Main-task utility shows no statistically significant loss, and questioning preserves more utility than active stopping. Monitors can serve not only as detectors or gatekeepers, but as participants in execution whose communication can shape behavior while preserving continued task progress.