Agent System Operations: Categorization, Challenges, and Future Directions

Fuente: arXiv
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Auteurs principaux: Wang, Zexin, Pei, Changhua, Liu, Yuanhao, Li, Jingjing, Huo, Yintong, Zhou, Quan, Si, Haotian, Cui, Hang, Liu, Zihan, Xie, Gaogang, Sun, Fei, Pei, Dan, Lo, David
Format: Preprint
Publié: 2026
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author Wang, Zexin
Pei, Changhua
Liu, Yuanhao
Li, Jingjing
Huo, Yintong
Zhou, Quan
Si, Haotian
Cui, Hang
Liu, Zihan
Xie, Gaogang
Sun, Fei
Pei, Dan
Lo, David
author_facet Wang, Zexin
Pei, Changhua
Liu, Yuanhao
Li, Jingjing
Huo, Yintong
Zhou, Quan
Si, Haotian
Cui, Hang
Liu, Zihan
Xie, Gaogang
Sun, Fei
Pei, Dan
Lo, David
contents As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional systems, garnering increasing attention. However, despite the widespread research interest and industrial application of agent systems, these systems, like their traditional counterparts, frequently encounter anomalies. These anomalies lead to instability and insecurity, hindering their further development. Therefore, a comprehensive and systematic approach to the operation and maintenance of agent systems is urgently needed. Unfortunately, current research on the operations of agent systems is sparse. To address this gap, we have undertaken a survey on agent system operations with the aim of establishing a clear framework for the field, defining the challenges, and facilitating further development. Specifically, this paper begins by systematically defining anomalies within agent systems, categorizing them into intra-agent anomalies and inter-agent anomalies. Next, we introduce a novel and comprehensive operational framework for agent systems, dubbed Agent System Operations (AgentOps). We provide detailed definitions and explanations of its four key stages: monitoring, anomaly detection, root cause localization, and resolution.
format Preprint
id arxiv_https___arxiv_org_abs_2606_01581
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Agent System Operations: Categorization, Challenges, and Future Directions
Wang, Zexin
Pei, Changhua
Liu, Yuanhao
Li, Jingjing
Huo, Yintong
Zhou, Quan
Si, Haotian
Cui, Hang
Liu, Zihan
Xie, Gaogang
Sun, Fei
Pei, Dan
Lo, David
Multiagent Systems
As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional systems, garnering increasing attention. However, despite the widespread research interest and industrial application of agent systems, these systems, like their traditional counterparts, frequently encounter anomalies. These anomalies lead to instability and insecurity, hindering their further development. Therefore, a comprehensive and systematic approach to the operation and maintenance of agent systems is urgently needed. Unfortunately, current research on the operations of agent systems is sparse. To address this gap, we have undertaken a survey on agent system operations with the aim of establishing a clear framework for the field, defining the challenges, and facilitating further development. Specifically, this paper begins by systematically defining anomalies within agent systems, categorizing them into intra-agent anomalies and inter-agent anomalies. Next, we introduce a novel and comprehensive operational framework for agent systems, dubbed Agent System Operations (AgentOps). We provide detailed definitions and explanations of its four key stages: monitoring, anomaly detection, root cause localization, and resolution.
title Agent System Operations: Categorization, Challenges, and Future Directions
topic Multiagent Systems
url https://arxiv.org/abs/2606.01581