The paper introduces EvidenceNet, a runtime assurance layer designed to verify that coordinated AI agent operations achieve an operator’s intended network-wide outcomes across multiple administrative domains. EvidenceNet collects post-change observations from the required authority scopes, checks their freshness and validity, and uses a verifier agent to assess observation content. Experiments on live routing networks demonstrate that this approach can detect successful outcomes that configuration-action logs alone miss, and it rejects completions when observations are sourced incorrectly, substituted, or stale.
By Tianzhu Zhang, Chih-Kai Huang, Meikang Qiu
The paper introduces NetArtifactBench, a benchmark designed to evaluate whether AI agents can detect and repair inconsistencies in network experiment records while preserving supported claims. It tests 23 agent configurations on 52 instances with injected inconsistencies, finding an average pass rate of 65.3 % but no runtime exceeding 30 % for complex repairs that require recovering implicit relations and propagating changes across artifacts. The results highlight a clear distinction between local corrections and full record-level repair, leading the authors to argue that artifact integrity should be a primary design and evaluation criterion for AI agents in network systems.
By Tianzhu Zhang, Weichen Tao, Changgang Zheng, Yusheng Zheng, Long Chen, Xiaoyi Fan, Meikang Qiu
arXiv:2607. 22947v1 Announce Type: new Abstract: A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change.
By Hongyu H\`e, Maria Apostolaki
arXiv:2606. 19356v1 Announce Type: cross Abstract: When multi-agent LLM systems produce bad answers, not all failures are equal: some answers are grounded in the right material but incomplete, while others are simply ungrounded and should be stopped.
By Anantha Sharma
arXiv:2605. 12729v2 Announce Type: replace-cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing.
By Muhammad Bilal, Jon Crowcroft, Ruizhi Wang, Xiaolong Xu, Schahram Dustdar
arXiv:2606. 09122v1 Announce Type: cross Abstract: Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures.
By Arun Malik
arXiv:2608. 12002v1 Announce Type: new Abstract: Agents are increasingly considered for automating network operations and maintenance, where engineers must diagnose network faults, optimize configurations to enhance services, and reduce operational costs while acting under strict constraints.
By Xingyu Yan, Tingting Dai, Antonio De Domenico, Mohamed Sana, Nicola Piovesan, Changchang Li, Bowen Liu, Kun Jiang, Mengjie Zhang, Dingcheng Shan, Jing-Cheng Pang, Chenwei Wu, Sijie Wu, Lianying Chao, Haoran Cai, Jiantao Ye, Xubin Li, Simon Mark Lucas, Xin Chen
arXiv:2609.17930v1 Announce Type: new
Abstract: As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether t...
By Salman Rahman, Yubin Kim, Mihir Parmar, A. Ali Heydari, Genglin Liu, Simon A. Lee, Weizhi Zhang, Arian Hosseini, Ahmed A. Metwally, Yuzhe Yang, Baharan Mirzasoleiman, Xin Liu, Pavel Izmailov, Saadia Gabriel, Mark Malhotra, Shwetak Patel, Daniel McDuff, Hamid Palangi
arXiv:2608.23179v1 Announce Type: cross
Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are po...
By Chang Liu, Xiaohui Xie, Xinyi Chen, Yong Cui
arXiv:2607. 20005v1 Announce Type: new Abstract: In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all.
By Chengxiao Dai, Zhaokun Yan, Chenjun Lei, Qiao Li, Luyan Zhang
arXiv:2604. 09523v2 Announce Type: replace Abstract: Training reinforcement-learning agents for cyber defense requires an environment that reflects the operational setting: noisy, partial observations, several defenders coordinating across a network, and an adaptive adversary realized through self-play.
By Igor Jankowski
arXiv:2609.06835v1 Announce Type: cross
Abstract: Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these s...
By Chaoyu Zhang, Hexuan Yu, Heng Jin, Shanghao Shi, Ning Zhang, Yi Shi, Yulia R. Gel, Y. Thomas Hou, Wenjing Lou