Layered feedback is an optimization strategy in feedback control designs widely used in electrical and mechanical engineering. Layered control theory suggests that the performance of controllers is bound by the universal robustness-efficiency trade-off limit, which could be overcome by layering two or more feedbacks together. In natural biological networks, genes are often regulated with redundancy and layering to adapt to environmental perturbations. Control theory hypothesizes that this layering architecture is also adopted by nature to overcome this performance trade-off. In this work, we validated this property of layered control with a synthetic network in living E. coli cells. We performed system analysis on a node-based design to confirm the trade-off properties before proceeding to simulations with an effective mechanistic model, which guided us to the best performing design to engineer in cells. Finally, we interrogated its system dynamics experimentally with eight sets of perturbations on chemical signals, nutrient abundance, and growth temperature. For all cases, we consistently observed that the layered control overcomes the robustness-efficiency trade-off limit. This work experimentally confirmed that layered control could be adopted in synthetic biomolecular networks as a performance optimization strategy. It also provided insights in understanding genetic feedback control architectures in nature.