面向自主云运维（CloudOps）的大语言模型驱动多智能体框架构建
Engineering LLM Powered Multi-agent Framework for Autonomous CloudOps
AI总结：
针对自主CloudOps开发面临的数据源多样、流程编排难等挑战，提出融合GenAI与RAG、兼顾自主与人工管控的多智能体框架MOYA，经评估其在复杂工作流中的表现优于非智能体方案。
摘要：
云运维（Cloud Operations, CloudOps）是一个快速发展的领域，聚焦于云基础设施的自动化管理与优化，对企业应对日益复杂的云环境至关重要。MontyCloud Inc.是CloudOps领域的头部企业之一，利用自主机器人管理云合规、安全与持续运维。为让平台对客户更易用、更高效，我们引入了生成式人工智能（GenAI）。\n 为现有MontyCloud系统开发基于GenAI的自主CloudOps解决方案面临诸多挑战，包括：i）数据源多样；ii）多流程编排；iii）处理复杂工作流以实现日常任务自动化。为此，我们开发了MOYA——一个多智能体框架，它借助GenAI，在自主性与必要的人工管控之间取得平衡。该框架整合了各类内外部系统，针对任务编排、安全性、错误缓解等因素进行了优化，同时利用检索增强生成（Retrieval Augmented Generation, RAG）产出准确、可靠且相关的洞察。我们通过从业者评估与自动化检查对多智能体系统进行了验证，结果表明，在复杂工作流场景下，该系统相比非智能体方法在准确性、响应性与有效性上均有所提升。
英文摘要：
Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots to manage cloud compliance, security, and continuous operations. To make the platform more accessible and effective to the customers, we leveraged the use of GenAI. Developing a GenAI-based solution for autonomous CloudOps for the existing MontyCloud system presented us with various challenges such as i) diverse data sources; ii) orchestration of multiple processes; and iii) handling complex workflows to automate routine tasks. To this end, we developed MOYA, a multi-agent framework that leverages GenAI and balances autonomy with the necessary human control. This framework integrates various internal and external systems and is optimized for factors like task orchestration, security, and error mitigation while producing accurate, reliable, and relevant insights by utilizing Retrieval Augmented Generation (RAG). Evaluations of our multi-agent system with the help of practitioners as well as using automated checks demonstrate enhanced accuracy, responsiveness, and effectiveness over non-agentic approaches across complex workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
