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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13956 |
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| _version_ | 1866908996571496448 |
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| author | Bai, Zishan Chen, Hanxuan Luo, Jing Ni, Ziyi Ge, Enze Shi, Jiacheng Zhang, Yichao Gu, Jiayi Han, Zhimo Bao, Riyang Hao, Junfeng |
| author_facet | Bai, Zishan Chen, Hanxuan Luo, Jing Ni, Ziyi Ge, Enze Shi, Jiacheng Zhang, Yichao Gu, Jiayi Han, Zhimo Bao, Riyang Hao, Junfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression Bai, Zishan Chen, Hanxuan Luo, Jing Ni, Ziyi Ge, Enze Shi, Jiacheng Zhang, Yichao Gu, Jiayi Han, Zhimo Bao, Riyang Hao, Junfeng Multiagent Systems Artificial Intelligence 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. |
| title | AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2512.13956 |