Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures
TL;DR
LongRCA Bench is a new benchmark for diagnosing failures in long-horizon agent executions, providing insights into responsible roles and root causes. It achieves 51.1% accuracy in identifying responsible roles and 24.1% in pinpointing exact root causes.
✦ Why It Matters
Implement LongRCA Bench to improve failure diagnosis in your long-horizon agent systems today.
Key Takeaways
- Utilize LongRCA Bench for comprehensive failure analysis in long-horizon agents.
- Adopt RCTA for improved root-cause identification without training.
- Focus on separate metrics for responsible-role and root-cause localization.
- Explore the 1,140 trajectories to enhance your debugging strategies.
How It Works
RCTA operates by analyzing segments of execution traces to identify potential error steps. It then connects these steps to earlier instructions, allowing for a systematic approach to diagnosing failures in long-horizon agent executions.