Modern AIOps environments operating within multi-campus institutional infrastructures suffer acutely from topological drift and black-box unmanaged physical network segments. Classical layer-2 discovery pipelines rely on uniform administrative cooperation and ubiquitous SNMP polling, which routinely fail in heterogeneous, multi-vendor, and multi-tenant overlay infrastructures. This paper introduces B.O.D.Y. (Beyond-Overlay Deterministic topologY), a deterministic structural grounding layer for AIOps ecosystems operating under fragmented administrative boundaries. B.O.D.Y. bypasses the NP-hard incomplete Address Forwarding Table (AFT) resolution dilemma by formalizing a multi-modal data fusion pipeline that orchestrates ephemeral MAC address forwarding tables collected via non-privileged terminal sessions, passive OUI fingerprinting, PoE telemetry, and declarative state storage. The resulting topology graph reconstructs unmanaged physical segments and maps logical asset semantics without administrative network privileges, providing downstream reasoning systems with an immutable, auditable source of physical ground truth. Evaluation across five campuses of the Universidade Federal Fluminense, resolving 530 of 541 registered edge devices, demonstrates that deterministic topological grounding eliminates a critical failure mode in prob bilistic AIOps reasoning: confident causal inference decoupled from physical reality.

翻译：运行在多园区机构基础设施中的现代AI运维环境，深受拓扑漂移和不可管理的物理网络黑箱段困扰。经典二层发现协议依赖统一的管理协作和普适的SNMP轮询，在异构、多厂商、多租户的覆盖网络基础设施中常常失效。本文提出B.O.D.Y.（超越覆盖网络的确定性拓扑），为运行在碎片化行政边界下的AI运维生态系统提供了一种确定性结构基准层。B.O.D.Y.通过构建多模态数据融合管道，巧妙地绕过了NP-难的地址转发表不完整解析难题。该管道协调了通过非特权终端会话收集的临时MAC地址转发表、被动OUI指纹识别、PoE遥测数据以及声明式状态存储。由此生成的拓扑图在无需管理网络权限的情况下重构了不可管理的物理网段，并映射了逻辑资产语义，从而为下游推理系统提供了不可篡改、可审计的物理基准源。在弗鲁米嫩塞联邦大学的五个园区进行的评估（成功解析541台注册边缘设备中的530台）表明，确定性拓扑基准消除了概率性AI运维推理中的一个关键失效模式：即自信的因果推断脱离物理现实。