On the one hand, natural phenomena of spontaneous pattern formation are generally random and repetitive, whereas, on the other hand, complicated heterogeneous architectures are the product of human design. The only examples of self-organized and structured systems are biological organisms produced by development. Can we export their precise self-formation capabilities to computing systems? This work proposes an "embryomorphic engineering" approach inspired by evo-devo to solve the paradoxical challenge of planning autonomous systems. Its goal is to artificially reconstruct complex morphogenesis by integrating three fundamental ingredients: self-assembly and pattern formation under genetic regulation. It presents a spatial computational agent-based model that can be equivalently construed as (a) moving cellular automata, in which cell rearrangement is influenced by the pattern they form, or (b) heterogeneous swarm motion, in which agents differentiate into patterns according to their location. It offers a new abstract framework to explore the causal and programmable link from genotype to phenotype that is needed in many emerging computational domains, such as amorphous computing or artificial embryogeny.