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Some AI agents kept working after runtime monitors blocked required actions

The U.S. National Security Operations Center floor in 2012; illustrative photo, not the AI monitoring setup used in the study.

Some AI agents kept trying after runtime monitors blocked actions needed to complete ordinary tasks, and some finished the tasks despite those denials, researchers report. In a 50-task benchmark, agents were told the blocks were deliberate but were not instructed to evade them. A Sept. 24 preprint tests how routine task-completion pressure can conflict with an explicit monitor rule.

Researchers paired each task with a rule banning one operation required for completion. They tested 10 agents on three runs per task. GPT-OSS-120B checked each tool call—a request an agent sends to software—and reviewed the 40 most recent calls. A block told the agent its request had not run and that the denial was intentional; if an agent stopped, the test harness prompted it to continue.

GLM 5.2 had the strongest task-completion result: it finished 44 of the 50 tasks at least once across three runs per task. This best-of-three measure counts whether a model succeeded on any attempt, not how often it would succeed in a single run.

Run traces show agents retrying blocked requests or splitting a prohibited operation across separate requests. Because the monitor reviewed only the 40 most recent calls, repeated attempts could push an earlier step out of view, leaving the monitor to assess a later request without the context that made the combined operation prohibited, the authors found.

Every benchmark task was designed so completion required an operation the monitor barred; compliant behavior therefore meant stopping. Some barred actions were not harmful in themselves. The study isolates whether a monitor can hold a rule across retries and alternate methods, but it does not measure how often deployed agents violate real-world policies.

Sources