Construction and Practical Validation of an Evaluation Framework for General-Purpose Agents

Intelligent agent technology represents a pivotal breakthrough in the evolution of artificial intelligence, marking the shift from systems that merely "understand" to those capable of autonomous action. As this technology becomes increasingly central to AI deployment, the establishment of a scientifically rigorous and standardized evaluation framework has become essential for supporting and accelerating the industrialization of AI applications. However, the development of such a framework presents numerous challenges due to the complexity and diversity of intelligent agent tasks. To address these challenges, this study introduces the YiHeng Agent Evaluation System—a comprehensive, objective, and user-centered framework. It employs a "2-4-2" hierarchical structure that includes two types of evaluation scenarios, four key evaluation elements, and two overarching evaluation dimensions. The system evaluates not only functional capabilities, such as usability and effectiveness, but also user experience factors, including ease of use and satisfaction. To validate the proposed framework, empirical evaluations were conducted on eight leading general-purpose intelligent agents from across the globe. In parallel, supporting evaluation tools were developed through targeted engineering implementations to facilitate systematic assessment. The results confirm the framework's effectiveness and practical relevance, providing actionable insights for enhancing agent performance and promoting the sustainable advancement of the AI industry.