InfraBench is a new benchmark suite for testing AI agents on realistic infrastructure management tasks across the system stack and operational lifecycle.
The paper argues that infrastructure work is a useful stress test because success is not just whether a command ran. Agents must make durable changes, preserve distributed invariants, avoid unsafe side effects, and clean up after themselves. The benchmark includes fine-grained risk assessment rather than a single pass-fail score.
In experiments with 15 agent-model configurations, the authors report mean effective scores ranging from about 40% to 88%. Even top configurations did not secure full scores across all tasks, and repeated runs showed that strong agents still passed only a fraction of attempts.
The finding is important for teams considering autonomous operations tools. Agents may look capable when judged by immediate task completion, yet still leave systems in states a human operator would consider risky. Infrastructure automation needs evaluation that checks the aftermath, not only the final message.