A Survey on AgentOps: Categorization, Challenges, and Future Directions
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866911090244321280 |
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| author | Wang, Zexin Li, Jingjing Zhou, Quan Si, Haotian Liu, Yuanhao Li, Jianhui Xie, Gaogang Sun, Fei Pei, Dan Pei, Changhua |
| author_facet | Wang, Zexin Li, Jingjing Zhou, Quan Si, Haotian Liu, Yuanhao Li, Jianhui Xie, Gaogang Sun, Fei Pei, Dan Pei, Changhua |
| 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 analysis, and resolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on AgentOps: Categorization, Challenges, and Future Directions Wang, Zexin Li, Jingjing Zhou, Quan Si, Haotian Liu, Yuanhao Li, Jianhui Xie, Gaogang Sun, Fei Pei, Dan Pei, Changhua Artificial Intelligence 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 analysis, and resolution. |
| title | A Survey on AgentOps: Categorization, Challenges, and Future Directions |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2508.02121 |