SpatialBench, introduced by computational biology researchers, provides a suite of 146 verifiable problems drawn from authentic spatial‑omics workflows to evaluate agents and models on realistic analysis tasks. The benchmark spans five platforms and seven task categories, aiming to close the demo‑to‑deployment gap for AI agents in biology. The authors argue that many current models excel on toy problems but fail on messy, context‑dependent datasets that labs actually produce. SpatialBench supplies ground‑truth tasks that test robustness to noise, diverse outputs, and domain‑specific decisions. Adoption of this benchmark could accelerate meaningful progress in lab‑ready analysis agents, inform tool procurement by core facilities, and set standards for reproducible computational pipelines in spatial biology.