Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
AgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems
TL;DR
AgentBug-Smith is a novel approach that automates the reproduction of real-world harness bugs in agentic systems, significantly outperforming existing methods. It establishes Live-Harness-Bench, a dynamic benchmark with 200 reproducible bugs, enhancing the repair capabilities of software agents by 6.32%.
✦ Why It Matters
Implement AgentBug-Smith to improve your software agents' bug repair capabilities today.
Key Takeaways
- Utilize AgentBug-Smith for automated bug reproduction.
- Access Live-Harness-Bench for evaluating agent performance.
- Incorporate reusable repair skills from the benchmark.
- Enhance your agents' repair rates by over 6%.
How It Works
AgentBug-Smith employs advanced algorithms to continuously identify and reproduce harness bugs from various open-source agentic systems. By leveraging large language models (LLMs), it automates the bug reproduction process, significantly reducing the time and effort required compared to manual methods.