Biological morphogenesis navigates morphospace while coordinating cellular activity across many spatiotemporal scales where a single developmental system can generate different anatomical outcomes depending on its environmental context. For example, a single human embryo can develop into a human organism consisting of trillions of cells under canonical conditions, but under alternative circumstances, the same embryo can develop into the morphology of a stable organoid. This conditional morphogenetic process leverages environmental conditioning signals to steer the self-organization of a competent cellular substrate together with hierarchical coordination across scales. This mixture has been increasingly recognized as a multiscale competency architecture (MSCA). Inspired by the MSCA, Neural Cellular Automata (NCAs) and their hierarchical extension (H-NCAs) provide minimal models in which shared local rules generate tissue level forms. Prior work has recognized evolvability benefits of hierarchy, but its additional inductive biases on development remain unclear. Here, we evolved two scale H-NCAs and single scale Flat NCAs (F-NCAs) on a conditional morphogenesis task to generate one of five target phenotypes based on a fixed environmental conditioning signal. Selection evaluated only the phenotype at a single maturity time point, leaving developmental dynamics before and after it unselected. Both architectures solved the task, but H-NCAs evolved faster, showed shorter periods of evolutionary stasis, and reached developmental maturity earlier. Posthoc perturbations showed that H-NCAs tolerated larger update noise, generalized better to unseen stochastic schedules, committed earlier during development to their target phenotype, and recovered more accurately after ablation of half the tissue. They also showed sharper transitions under prepattern corruption and reduced morphological drift for four of five phenotypes during extended rollouts. Interestingly, none of these developmental and robustness properties was part of the selection objective, suggesting that the coarse hierarchical regulatory architecture of a developmental system can bias evolution toward more robust and regulative developmental programs without direct selection for these properties.