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Jev-Guard introduces a low-latency architecture combining policy routing and safety classification to address the inference overhead of traditional auto-regressive guardrails in AI agent harnesses. By navigating an editable policy tree, the system achieves sub-60ms latency while maintaining high accuracy across diverse domains like cybersecurity and content moderation. This approach significantly reduces review time for agent tasks without compromising on policy alignment or safety.
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees
arXiv:2610.18326v1 Announce Type: cross Abstract: Agent harnesses require a low-latency guardrail model together with customizable audit policies. However, deploying an auto-regressive guardrail model remains challenging: such models incur high inference latency and are often poorly aligned with customized policies. To address these problems, we present Jev-Guard, a unified model for safety classification and policy routing. We further construct a policy tree over which Jev-Guard performs routing, spanning diverse domains including content moderation, cybersecurity, privacy, and legal compliance. Specifically, Jev-Guard first navigates the policy tree to select the applicable audit policy given the caller's audit context, and then performs safety classification conditioned on the retrieved policies. On 7,033 evaluation cases, Jev-Guard achieves 93.72\% safety accuracy, a mean latency of 58ms, and 93.10\% hierarchical routing accuracy. The full routing-and-classification pipeline averages 240ms. Furthermore, we integrate Jev-Guard into the Hermes harness framework. Across agent trajectories, its separate permission gate cuts mean review time per task from 4.80s for an SFT-LLM judge to 1.33s, with no policy violations are observed for either guard.
Abstract:Agent harnesses require a low-latency guardrail model together with customizable audit policies. However, deploying an auto-regressive guardrail model remains challenging: such models incur high inference latency and are often poorly aligned with customized policies. To address these problems, we present Jev-Guard, a unified model for safety classification and policy routing. We further construct a policy tree over which Jev-Guard performs routing, spanning diverse domains including content moderation, cybersecurity, privacy, and legal compliance. Specifically, Jev-Guard first navigates the policy tree to select the applicable audit policy given the caller's audit context, and then performs safety classification conditioned on the retrieved policies. On 7,033 evaluation cases, Jev-Guard achieves 93.72\% safety accuracy, a mean latency of 58ms, and 93.10\% hierarchical routing accuracy. The full routing-and-classification pipeline averages 240ms. Furthermore, we integrate Jev-Guard into the Hermes harness framework. Across agent trajectories, its separate permission gate cuts mean review time per task from 4.80s for an SFT-LLM judge to 1.33s, with no policy violations are observed for either guard.
Cryptography and Security (cs.CR); Computation and Language (cs.CL)
arXiv:2610.18326 [cs.CR]
(or arXiv:2610.18326v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.18326 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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