用AI代理自动优化云基础设施成本，提升财务与工程协作效率。
FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization
- 设计自主目标驱动的AI代理处理多源异构云账单数据
- 在真实场景中实现数据整合、分析与优化建议生成
- 效果媲美人工专家，适合云成本管理团队使用
FinOps（财务+运营）是一种通过工程、财务与业务团队协同实现财务责任共担的云价值最大化框架。实践中，从业者面临核心挑战：来自多个云服务商和内部系统的账单数据格式、分类体系与度量标准不统一，难以快速合成可操作洞察并做出及时决策。为此，本文提出利用自主、目标驱动的AI代理实现FinOps自动化。我们构建了一个面向典型IT基础设施与成本优化场景的FinOps代理系统，模拟从多源数据采集、合并分析到生成优化建议的完整工业流程。通过定义一组评估指标，对比多种开源与闭源大模型表现，结果表明该代理在理解任务、规划路径与执行操作方面已达到实际从业者的水平。
原文摘要 · Abstract (English)
FinOps (Finance + Operations) represents an operational framework and cultural practice which maximizes cloud business value through collaborative financial accountability across engineering, finance, and business teams. FinOps practitioners face a fundamental challenge: billing data arrives in heterogeneous formats, taxonomies, and metrics from multiple cloud providers and internal systems which eventually lead to synthesizing actionable insights, and making time-sensitive decisions. To address this challenge, we propose leveraging autonomous, goal-driven AI agents for FinOps automation. In this paper, we built a FinOps agent for a typical use-case for IT infrastructure and cost optimization. We built a system simulating a realistic end-to-end industry process starting with retrieving data from various sources to consolidating and analyzing the data to generate recommendations for optimization. We defined a set of metrics to evaluate our agent using several open-source and close-source language models and it shows that the agent was able to understand, plan, and execute tasks as well as an actual FinOps practitioner.
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