AI 中文总结
研究自主网络中跨供应商代理工具信任管理问题，提出AgentToolMO模型，含信任状态机等内容。模拟评估显示该模型能减少传播范围、保证级联收敛，为可信多供应商自主网络管理提供标准化途径。
AI 中文摘要
自主网络的4-5级要求人工智能代理在无人监督的情况下跨供应商边界调用工具，但现有管理标准缺乏跨供应商信任可见性的标准化机制。当供应商B的工具受损时，供应商A的代理仍继续调用，导致级联服务影响。我们提出了AgentToolMO，一种用于代理工具信任管理的3GPP NRM信息模型。该模型包括形式化定义的信任状态机、阻尼级联传播、跨供应商信任通知及追溯影响评估。基于多供应商拓扑的模拟评估表明，标准化跨供应商通知可减少传播范围，保证级联收敛，并为可信多供应商自主网络管理提供标准化途径。
英文摘要
Autonomous Network Levels 4-5 require AI agents to invoke tools across vendor boundaries without human oversight, yet existing management standards lack a standardized mechanism for cross-vendor trust visibility. When a tool from Vendor B is compromised, agents from Vendor A continue invoking it -- unaware of the trust degradation -- causing cascading service impact. We present AgentToolMO, a proposed 3GPP NRM information model for agent tool trust management. The model comprises: a formally defined trust state machine with provable graduated enforcement, damped cascade propagation with bounded convergence, cross-vendor trust notifications via existing Management Services (MnS) interfaces, and retroactive impact assessment through NRM dependency graph traversal. Simulation-based evaluation across multi-vendor topologies shows that standardized cross-vendor notifications reduce blast radius from hours-scale undetected propagation to near-real-time containment bounded by MnS notification delivery, with cascade convergence guaranteed in bounded iterations and sub-linear notification scaling across vendor domains. The framework operates within existing 3GPP management infrastructure, leverages existing protocols, and provides a standardization pathway for trustworthy multi-vendor autonomous network management.
Comments22 pages, 7 figures, 9 tables, 4 algorithms
