Mind the Goal: Data-Efficient Goal-Oriented Evaluation of Conversational Agents and Chatbots using Teacher Models

• URL: http://arxiv.org/abs/2510.03696v1
• Date: Sat, 04 Oct 2025 06:22:47 GMT
• Title: Mind the Goal: Data-Efficient Goal-Oriented Evaluation of Conversational Agents and Chatbots using Teacher Models
• Authors: Deepak Babu Piskala, Sharlene Chen, Udita Patel, Parul Kalra, Rafael Castrillo,
• Abstract summary: We propose a comprehensive framework for goal-oriented evaluation of multi-agent systems.<n>Our method segments conversations by user goals and evaluates success using all relevant turns.<n>In an enterprise setting, we apply our framework to evaluate AIDA, a zero-to-one employee conversational agent system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the quality of multi-turn chatbot interactions remains challenging, as most existing methods assess interactions at the turn level without addressing whether a user's overarching goal was fulfilled. A ``goal'' here refers to an information need or task, such as asking for policy information or applying for leave. We propose a comprehensive framework for goal-oriented evaluation of multi-agent systems (MAS), introducing the \textbf{Goal Success Rate (GSR)} to measure the percentage of fulfilled goals, and a \textbf{Root Cause of Failure (RCOF)} taxonomy to identify reasons for failure in multi-agent chatbots. Our method segments conversations by user goals and evaluates success using all relevant turns. We present a model-based evaluation system combining teacher LLMs, where domain experts define goals, set quality standards serving as a guidance for the LLMs. The LLMs use ``thinking tokens'' to produce interpretable rationales, enabling \textit{explainable}, \textit{data-efficient} evaluations. In an enterprise setting, we apply our framework to evaluate AIDA, a zero-to-one employee conversational agent system built as a ground-up multi-agent conversational agent, and observe GSR improvement from 63\% to 79\% over six months since its inception. Our framework is generic and offers actionable insights through a detailed defect taxonomy based on analysis of failure points in multi-agent chatbots, diagnosing overall success, identifying key failure modes, and informing system improvements.

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