InfraBench: Benchmarking AI Agents for Infrastructure Management
InfraBench, a newly launched benchmark suite, is designed to assess AI agents in realistic infrastructure management scenarios. It encompasses the entire system stack and operational lifecycle, incorporating detailed risk evaluation. Tests conducted with 15 different agent-model configurations indicated that even the most capable agent fails to achieve a perfect score across all tasks. Average effective scores varied between approximately 40% and 88%, with standard errors for each configuration ranging from 6 to 12 points. Repeating each task three times demonstrated that even the best configurations only succeed a portion of the time. Scoring per task revealed a consistent failure trend: while agents may meet immediate goals, they often falter in ensuring long-term reliability. This benchmark seeks to tackle the growing complexity of managing contemporary computing infrastructures and the role of AI agents in automating these responsibilities. The findings underscore the existing limitations of AI agents in addressing the complexities of real-world infrastructure.
Key facts
- InfraBench is a benchmark suite for evaluating AI agents on infrastructure tasks.
- It covers the full system stack and operational lifecycle.
- Includes fine-grained risk assessment.
- Experiments with 15 agent-model configurations were conducted.
- Strongest agent could not secure a full score across all tasks.
- Mean effective scores ranged from 40% to 88%.
- Per-configuration standard errors were 6-12 points.
- Top configurations passed only a fraction of attempts when tasks were repeated three times.
Entities
Institutions
- arXiv