Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills

This paper introduces ACES (Agentic Continuous Evaluation of Skills), a comprehensive framework developed by NVIDIA to move beyond static document scanning when assessing AI agent capabilities. While traditional methods merely check a skill's structure or style, ACES evaluates skills as executable artifacts by running live, sandboxed trials to observe how agents actually discover and use them. The methodology centers on Skill Lift, a metric that measures the marginal value a specific skill adds by comparing an agent's performance with and without that skill enabled. This system utilizes a standardized Agent Trajectory Interchange Format (ATIF) to ensure compatibility across different agent harnesses and models. Empirical testing on 145 enterprise skills reveals that static scores correlate poorly with runtime success, highlighting the necessity of live agent evaluation for identifying regressions or routing failures. Ultimately, the framework integrates into CI/CD workflows, allowing developers to refine agent behaviors using evidence-based reports rather than subjective prose.