Exploring Quality Evaluation Metric Selection and Calculation for Agent Applications

On the morning of November 6, 2026, the Chinese developer platforms Juejin and cnblogs each published an article carrying the same title, on how to choose and calculate evaluation metrics for AI agents. Both pieces focus on agent-based applications such as customer-service bots, coding assistants, and knowledge-base question-answering systems, in which an agent carries out tasks on the user's behalf.

The two articles open with essentially the same premise. In the AI era, agents are being deployed in products at scale, but whether an agent actually works well cannot be judged by subjective impression alone. Accurate assessment, the authors argue, requires a metrics system whose numbers can be computed rather than guessed.

Both pieces frame the discussion around two questions: which evaluation metrics apply to agents, and how those metrics should be calculated. The Juejin article sets out how to build an objective quality-assessment system and walks through the underlying calculation logic. The cnblogs article addresses the same ground, presenting specific metrics within an agent quality-assessment framework and aiming to help developers solve the practical evaluation problems of applications such as customer-service bots and coding assistants. Neither summary names particular metrics or offers worked calculation details, so the verifiable content stops there, with no further developments reported since.