Customer service automation has seen growing demand within digital transformation. Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability. This paper introduces an orchestration-free framework using Task-Oriented Flowcharts (TOFs) to enable end-to-end automation without manual intervention. We first define the components and evaluation metrics for TOFs, then formalize a cost-efficient flowchart construction algorithm to abstract procedural knowledge from service dialogues. We emphasize local deployment of small language models and propose decentralized distillation with flowcharts to mitigate data scarcity and privacy issues in model training. Extensive experiments validate the effectiveness in various service tasks, with superior quantitative and application performance compared to strong baselines and market products. By releasing a web-based system demonstration with case studies, we aim to promote streamlined creation of future service automation.

翻译：客服自动化在数字化转型中的需求日益增长。现有方法要么依赖具有大量智能体编排的模块化系统设计，要么采用过于简化的指令模式，提供的指导有限且泛化能力较差。本文引入一种无需编排的框架，利用面向任务的流程图实现无需人工干预的端到端自动化。我们首先定义了TOF的组件与评估指标，随后形式化了一种高性价比的流程图构建算法，以从服务对话中抽象出流程性知识。我们强调小型语言模型的本地化部署，并提出基于流程图的去中心化蒸馏方法，以缓解模型训练中的数据稀缺与隐私问题。大量实验验证了该方法在多种服务任务中的有效性，其定量指标与应用性能均优于现有强基线模型及市场产品。通过发布包含案例研究的网络系统演示，我们旨在推动未来服务自动化的高效创建。