{"slug": "infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk", "title": "InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk", "summary": "Researchers introduced InfraBench, a benchmark suite for evaluating AI agents on realistic infrastructure tasks across the full system stack and operational lifecycle with fine-grained risk assessment. Experiments with 15 agent-model configurations showed that even the strongest agent cannot secure a full score, with mean effective scores ranging from roughly 40% to 88% and per-configuration standard errors of 6-12 points. The benchmark, including its live leaderboard, tasks, and evaluation harness, is publicly available at infraben.ch.", "body_md": "arXiv:2608.11234v1 Announce Type: new\nAbstract: Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to automate infrastructure management tasks, but it remains unclear how well such agents can handle real-world infrastructure complexity. We present InfraBench, a benchmark suite for evaluating AI agents on realistic infrastructure tasks across the full system stack and full operational lifecycle with fine-grained risk assessment. Experiments with 15 agent-model configurations show that even the strongest agent cannot secure a full score across all tasks. Mean effective scores range from roughly 40% to 88% (with per-configuration standard errors of 6-12 points), repeating every task three times reveals that top configurations still pass only a fraction of their attempts, and per-check scoring exposes a general failure pattern: agents may routinely satisfy short-term objectives while leaving non-durable changes, broken distributed invariants, unsafe side effects, and uncleaned state behind. INFRABENCH, including its live leaderboard, tasks, and evaluation harness, is publicly available at infraben.ch.", "url": "https://wpnews.pro/news/infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk", "canonical_source": "https://arxiv.org/abs/2608.11234", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:19:41.518464+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-research", "ai-infrastructure"], "entities": ["InfraBench", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk", "markdown": "https://wpnews.pro/news/infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk.md", "text": "https://wpnews.pro/news/infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk.txt", "jsonld": "https://wpnews.pro/news/infrabench-evaluating-infrastructure-agents-across-layers-lifecycle-and-risk.jsonld"}}