LLM-based agents are increasingly proposed for network fault diagnosis, but existing benchmarks evaluate them only on accurate tickets and always assume a fault is present, conditions rarely met in pr...
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:2606. 29193v1 Announce Type: cross Abstract: LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data.
By Yuanhong Cai, Xiaohui Nie, Kanglin Yin, Changhua Pei, Yongqian Sun, Shenglin Zhang, Haibin Liu, Guiyang Liu, Xidao Wen, Fang Situ, Dan Pei
arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.
By Chenkai Zhang, Yiran Li, Yifang Tian, Michalis Bachras, Hans-Arno Jacobsen
The paper reports a failure study of a production agentic software‑delivery platform, analyzing 147 incidents across 81 runs. It shows that the standard reliability primitives—retry, timeout, and error‑rate circuit breaking—fail in practice, leading to costly loops, false trips, and blocked work. The authors identify two cross‑cutting causes—identity adequacy and evidence adequacy—and propose seven new reliability primitives that enforce reliability at the delegation level.
By Mazhar Shaikh, Anurag Rajkumar Bombarde, Harshal Pathak
arXiv:2606. 00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication.
By Md Nakhla Rafi, Md Ahasanuzzaman, Dong Jae Kim, Zhijie Wang, Tse-Hsun Chen