Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents

TL;DR

This paper introduces a novel framework for extracting and evaluating dialog workflows from customer service conversations, improving accuracy and aligning well with human assessments, thereby enhancing automated service agent performance.

Contribution

The work presents a new two-stage extraction process using QA-CoT prompting and a simulation-based evaluation method for dialog workflows, which has not been explored before.

Findings

QA-CoT improves workflow extraction accuracy by 12.16%.

The evaluation framework aligns closely with human assessments.

Extensive experiments validate the effectiveness of the proposed methods.

Abstract

Automated service agents require well-structured workflows to provide consistent and accurate responses to customer queries. However, these workflows are often undocumented, and their automatic extraction from conversations remains unexplored. In this work, we present a novel framework for extracting and evaluating dialog workflows from historical interactions. Our extraction process consists of two key stages: (1) a retrieval step to select relevant conversations based on key procedural elements, and (2) a structured workflow generation process using a question-answer-based chain-of-thought (QA-CoT) prompting. To comprehensively assess the quality of extracted workflows, we introduce an automated agent and customer bots simulation framework that measures their effectiveness in resolving customer issues. Extensive experiments on the ABCD and SynthABCD datasets demonstrate that our QA-CoT technique improves workflow extraction by 12.16\% in average macro accuracy over the baseline. Moreover, our evaluation method closely aligns with human assessments, providing a reliable and scalable framework for future research.