Agentic systems that combine large language models with external tools can complete complex tasks through multi-turn interactions. As these systems move into high-stakes deployments, ensuring they follow explicit policies becomes critical. The paper introduces PROBE, a framework designed to audit whether such tool-using agents remain compliant over the course of a conversation.
The approach is motivated by the gap between policy specification and actual agent behavior. While agents are often constrained by rules, verifying compliance in practice is non-trivial, especially when actions span multiple steps and involve external calls. PROBE appears to offer a learning-based method for auditing these interactions, though the abstract does not detail the underlying mechanism.
The significance lies in the growing need for oversight in autonomous systems. By focusing on policy compliance in multi-turn tool use, the work addresses a practical concern for deploying LLM agents in domains where errors carry real consequences. The paper is available on arXiv, but the abstract is cut off, so further details on methodology and results are not yet accessible from this source alone.