Software verification is now costly, taking over half the project effort while failing on modern complex systems. We hence propose a shift from verification and modeling to herding: treating testing as a model-free search task that steers systems toward target goals. This exploits the "Sparsity of Influence" -the fact that, often, large software state spaces are ruled by just a few variables, We introduce EZR (Efficient Zero-knowledge Ranker), a stochastic learner that finds these controllers directly. Across dozens of tasks, EZR achieved 90% of peak results with only 32 samples, replacing heavy solvers with light sampling.