Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills
TL;DR
ACES (Agentic Continuous Evaluation of Skills) is a framework designed to evaluate enterprise agent skills through live trials, revealing their effectiveness in real tasks. It demonstrated a mean Skill Lift of 0.2134 across 947 cases, indicating significant value added by the evaluated skills.
✦ Why It Matters
Evaluate your enterprise agent skills using ACES to ensure they effectively enhance task performance.
Key Takeaways
- Implement ACES to evaluate agent skills in real tasks.
- Focus on Skill Lift metrics to assess added value.
- Utilize the open-source NVIDIA SkillEvaluator for testing.
- Analyze skill execution and efficiency for performance insights.
How It Works
ACES evaluates skills by running paired live trials, comparing agent performance with and without the target skill. It normalizes the results into the Agent Trajectory Interchange Format (ATIF) and grades six runtime metrics, ultimately calculating Skill Lift to quantify the skill's contribution to task completion.