The proliferation of cloud-native architectures, characterized by microservices and dynamic orchestration, has rendered modern IT infrastructures exceedingly complex and volatile. This complexity generates overwhelming volumes of operational data, leading to critical bottlenecks in conventional systems: inefficient information processing, poor task coordination, and loss of contextual continuity during fault diagnosis and remediation. To address these challenges, we propose AOI (AI-Oriented Operations), a novel multi-agent collaborative framework that integrates three specialized agents with an LLM-based Context Compressor. Its core innovations include: (1) a dynamic task scheduling strategy that adaptively prioritizes operations based on real-time system states, (2) a three-layer memory architecture comprising Working, Episodic, and Semantic layers that optimizes context retention and retrieval. Extensive experiments on synthetic and real-world benchmarks show that AOI achieves 72.4\% context compression while preserving 92.8\% critical information, improves task success to 94.2\%, and reduces MTTR by 34.4\% over the best baseline. This work presents a paradigm shift towards scalable, adaptive, and context-aware autonomous operations, enabling robust management of next-generation IT infrastructures with minimal human intervention.

翻译：云原生架构（以微服务和动态编排为特征）的普及使得现代IT基础设施变得极其复杂且多变。这种复杂性产生了海量的运维数据，导致传统系统面临关键瓶颈：信息处理效率低下、任务协调能力差、以及在故障诊断与修复过程中情境连续性缺失。为应对这些挑战，我们提出AOI（面向人工智能的运维），一种新型多智能体协同框架，该框架集成了三个专用智能体与一个基于大语言模型的情境压缩器。其核心创新包括：（1）动态任务调度策略，可根据实时系统状态自适应地确定运维任务优先级；（2）由工作记忆层、情景记忆层和语义记忆层构成的三层记忆架构，可优化情境信息的保留与检索。在合成数据集和真实基准上的大量实验表明，AOI在保持92.8%关键信息的同时实现了72.4%的情境压缩率，将任务成功率提升至94.2%，并较最佳基线方法将平均修复时间降低了34.4%。这项工作提出了向可扩展、自适应且情境感知的自主运维范式转变，为下一代IT基础设施的稳健管理提供了最小化人工干预的解决方案。