AI 中文总结
针对智能体AI日益增长的安全风险，本文提出利用复值超稀疏流量矩阵，集成DBOS、OneSparse和GraphBLAS，通过模拟器监控智能体网络流量，以审计其行为是否符合用户意图。
AI 中文摘要
随着智能体人工智能在几乎所有行业中的使用日益增加，攻击面也在不断扩大。有必要对智能体进行监控，以确保其行为符合用户的意图。审计智能体的网络流量可提供智能体交互的清晰记录。本工作提出了一种新颖的方法，通过集成DBOS（数据库操作系统）、OneSparse PostgreSQL数据库和GraphBLAS数学库，使用复值超稀疏流量矩阵来监控智能体系统的网络流量。为开发这些概念，构建了一个智能体模拟器，允许不同数量的AI智能体使用不同策略共同勘察虚拟环境。生成的网络流量矩阵便于对AI智能体进行轻松监控。
英文摘要
As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that agents are acting in a way that is aligned with the users intent. Auditing an agent's network traffic provides a clear record of the agent interactions. This work presents a novel approach to monitoring the network traffic of agentic systems using complex valued hypersparse traffic matrices by integrating DBOS (DataBase OS), the OneSparse PostgreSQL database, and the GraphBLAS math library. To develop these concepts an agentic simulator was constructed, allowing a varying numbers of AI agents to collectively survey a virtual environment using different strategies. The resulting network traffic matrices enable easy monitoring of the AI agents.
Comments5 pages, 3 figures, to appear in IEEE URTC 2026
