Key Value Indicators (KVIs) provide a decision oriented view of a service by summarizing how operational performance translates into stakeholder value, risk, and outcomes. However, in many domains KVIs are difficult to compute in practice because they require selecting relevant KVI categories, defining measurable Key Performance Indicators (KPIs), collecting KPI values, and applying consistent calculation logic, all of which is typically performed manually and inconsistently from unstructured service documentation. This paper presents KPI2KVI, a tool that transforms a natural language service description into computed KVI estimates by orchestrating a deterministic multi agent workflow powered by Large Language Models (LLMs) that (i) elicits missing service context, (ii) extracts and finalizes relevant KVI categories from a taxonomy, (iii) generates service specific KPIs with units and descriptions, (iv) collects KPI values through an interactive dialogue and also supports intelligent estimation for KPI values that are unavailable, and (v) computes interval valued KVI outputs (minimum, exact, maximum) with traceable explanations for each KVI code. Simulations with representative service descriptions demonstrate that KPI2KVI consistently produces a complete end to end mapping from description to KVI intervals and provides transparent calculation narratives that support post hoc auditing and interactive advisory queries.

翻译：关键价值指标通过总结运营绩效如何转化为利益相关者价值、风险与成果，为服务提供了面向决策的视图。然而，在许多领域中，关键价值指标在实践中难以计算，因为其需要选择相关的关键价值指标类别、定义可量化的关键绩效指标、收集关键绩效指标数值并应用一致的计算逻辑——这些流程通常需人工完成，且依赖非结构化的服务文档，存在不一致性。本文提出KPI2KVI工具，通过编排由大语言模型驱动的确定性多智能体工作流，将自然语言服务描述转化为计算所得的关键价值指标估计值。该工作流能够：(i) 挖掘缺失的服务上下文；(ii) 从分类体系中提取并最终确定相关的关键价值指标类别；(iii) 生成带有单位和描述的服务专属关键绩效指标；(iv) 通过交互式对话收集关键绩效指标数值，并支持对不可获取数值进行智能估计；(v) 为每个关键价值指标编码计算区间型关键价值指标输出（最小值、精确值、最大值），并提供可追溯的解释。基于代表性服务描述的仿真实验表明，KPI2KVI能够持续生成从描述到关键价值指标区间的完整端到端映射，并提供透明的计算叙事，支持事后审计与交互式咨询查询。