Managing modern computing infrastructure has become increasingly complex. Recent advances in AI agents create an 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), and repeating every task three times reveals that top configurations still pass only a fraction of their attempts. 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. Blogger's Review: InfraBench provides a comprehensive benchmark suite for evaluating the capabilities of infrastructure agents, which can help improve the automation and intelligence of infrastructure management, driving further development and application of AI technology in this field.