Title: QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: AI Safety, Multi-agent System

Abstract: Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages.
However, deployer-written policies in natural language are ambiguous and context dependent, so they map poorly to machine-checkable rules and runtime enforcement is unreliable.
Expressing safety policies as sequents, we propose QuadSentinel, a four-agent guard (state tracker, policy verifier, threat watcher, and referee) that compiles these policies into machine-checkable rules built from predicates over observable state and enforces them online.
Referee logic plus an efficient top-$k$ predicate updater keeps costs low by prioritizing checks and resolving conflicts hierarchically.
Measured on ST-WebAgentBench (ICML CUA '25) and AgentHarm (ICLR '25), QuadSentinel improves guardrail accuracy and rule recall while reducing false positives.
Against single-agent baselines such as ShieldAgent (ICML '25), it yields better overall safety control.
Near-term deployments can adopt this pattern without modifying core agents by keeping policies separate and machine-checkable.

TL;DR: We propose a Multi-agent System based method to safeguard other agents and systems.