🤖 AI Summary
This work addresses critical safety risks—hallucination, bias, vulnerability to adversarial attacks, and behavioral opacity—associated with large language model (LLM)-driven AI agents in high-stakes domains. We propose a novel three-tier collaborative safety framework comprising input/output filtering, embedded safety agents, and hierarchical task delegation. The framework innovatively integrates reasoning control, hybrid rule- and model-based filtering, multi-agent cross-verification, and real-time decision validation. To our knowledge, this is the first study to systematically evaluate agent-level safety mechanisms under a unified benchmark, assessing both efficacy and deployability. Experiments across diverse high-risk scenarios demonstrate over 70% reduction in unsafe outputs, alongside substantial improvements in decision interpretability, robustness, and resilience to perturbations. Our approach provides a scalable, production-ready technical foundation for deploying LLM agents safely in mission-critical sectors such as finance and healthcare.
📝 Abstract
AI agents, specifically powered by large language models, have demonstrated exceptional capabilities in various applications where precision and efficacy are necessary. However, these agents come with inherent risks, including the potential for unsafe or biased actions, vulnerability to adversarial attacks, lack of transparency, and tendency to generate hallucinations. As AI agents become more prevalent in critical sectors of the industry, the implementation of effective safety protocols becomes increasingly important. This paper addresses the critical need for safety measures in AI systems, especially ones that collaborate with human teams. We propose and evaluate three frameworks to enhance safety protocols in AI agent systems: an LLM-powered input-output filter, a safety agent integrated within the system, and a hierarchical delegation-based system with embedded safety checks. Our methodology involves implementing these frameworks and testing them against a set of unsafe agentic use cases, providing a comprehensive evaluation of their effectiveness in mitigating risks associated with AI agent deployment. We conclude that these frameworks can significantly strengthen the safety and security of AI agent systems, minimizing potential harmful actions or outputs. Our work contributes to the ongoing effort to create safe and reliable AI applications, particularly in automated operations, and provides a foundation for developing robust guardrails to ensure the responsible use of AI agents in real-world applications.