Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Invocation-Level Reliability of Tool-Using Agents
TL;DR
Tool-using agents often fail by selecting incorrect tools or forming faulty arguments, leading to significant downstream errors. This study reveals that by depth 6 of multi-step tasks, about 70% of a model's clean-context capability is compromised due to earlier mistakes, highlighting the need for improved scoring methods.
✦ Why It Matters
Implement conditional-on-state scoring to better assess and improve your tool-using agents' performance today.
Key Takeaways
- Understand the two main failure modes of tool-using agents.
- Measure correct-invocation rates to assess model performance.
- Adopt conditional-on-state scoring for better performance estimates.
- Recognize the limitations of exact-match scoring in evaluating models.
How It Works
The proposed conditional-on-state scoring method allows for retrospective evaluation of model performance by analyzing cached completions. This approach helps to decouple severity from fixed scoring rules, enabling more accurate assessments of a model's ability to recover from errors.