Framework for Evaluating Goal-Based AI Agents

This technical white paper presents a framework for evaluating goal-based AI agents, addressing the challenges posed by the lack of domain-specific evaluation methodologies in business process automation. It outlines a dual-metric evaluation framework that measures both task success and reasoning trajectory, ensuring a comprehensive assessment of AI agents. The paper details the development of a purpose-built dataset architecture and the creation of the APA-Eval V5 business process dataset. It describes the evaluation framework's stages, including source ingestion, structured agent definition, high-level scenarios, and executable test classes. Additionally, it discusses the importance of auditability, efficiency, safety, reliability, and explainability in enterprise automation. The empirical results highlight the distinction between successful outcomes and the underlying reasoning processes, revealing common failure patterns such as the 'Scrappy Win'. The findings emphasize the necessity for robust evaluation frameworks to enhance the reliability and effectiveness of AI agents in real-world applications.