Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Incident-Arena: Getting agents to the last nine of reliability
TL;DR
Incident-Arena is a benchmark for AI coding agents in production incident response, featuring 20 real-world tasks that reveal significant limitations in current models. Frontier models scored below 64.3%, highlighting challenges in diagnosis, repair, and safety.
✦ Why It Matters
Evaluate your AI coding agents against the Incident-Arena benchmark to identify and address reliability issues.
Key Takeaways
- Test AI agents with real-world benchmarks like Incident-Arena.
- Focus on functional verification methods for reliability.
- Analyze failure modes in diagnosis and repair processes.
- Consider long-horizon reasoning capabilities in agent design.
Full Summary
AI coding agents are increasingly used in engineering, but their effectiveness in production incident response has been underexplored. Incident-Arena addresses this gap by providing a benchmark of 20 tasks based on real-world open source software, where agents deploy applications in ephemeral Kubernetes clusters while simulating faults and load conditions.
A novel verification method is introduced, moving beyond static checks to functional verifiers that maintain system-level metrics and ensure safe repairs. Trials showed that agents processed an average of 2.81 million tokens over 41 turns, yet frontier models achieved less than 64.3% success across tasks.
Failures were noted in areas such as diagnosis errors, incomplete repairs, and unsafe regressions, indicating a need for improved agent capabilities in long-horizon reasoning and reliability.