Agent System Operations: Categorization, Challenges, and Future Directions
For researchers and practitioners developing LLM-based agent systems, this paper provides a structured overview of operational challenges and a framework for addressing them, though it is a survey without empirical validation.
This survey categorizes anomalies in LLM-based agent systems into intra-agent and inter-agent types, and proposes a novel operational framework (AgentOps) with four stages: monitoring, anomaly detection, root cause localization, and resolution. The paper aims to establish a clear framework for the field, but does not provide concrete experimental results or numbers.
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.