Essential dimensions of complex systems research are the hard limits on what is achievable (laws), the organizing principles that succeed or fail in achieving them (architectures and protocols), the resulting behavior observed in real systems (behavior, data), and the processes by which systems evolve (variation, selection, design). Hard limits on measurement, prediction, communication, computation, decision, and control, as well as the underlying physical energy and material conversion mechanism necessary to implement these abstract processes are at the heart of modern mathematical theories of systems in engineering and science. Unfortunately, these subjects remain largely fragmented and incompatible, even as the tradeoffs between these limits are of growing. The principle aim of this talk is to motivate an integrated theory based on optimization. Insights can be drawn from three converging research themes. First, detailed description of components and a growing attention to systems biology and neuroscience, the organizational principles of organisms and evolution are becoming increasingly apparent. While the components differ and the system processes are far less integrated, advanced technology’s complexity is now approaching biology’s and there are striking convergences at the level of organization and architecture. Determining what is essential about this convergence and what is merely historical accident requires a deeper understanding of architecture — the most universal, high-level, persistent elements of organization — and protocols. Protocols define how diverse modules interact, and architecture defines how sets of protocols are organized. Finally, new mathematical frameworks for the study of complex networks suggests that this apparent network-level evolutionary convergence within/between biology/technology is not accidental, but follows necessarily from their universal system requirements to be fast, efficient, adaptive, evolvable, and robust to perturbations in their environment and component parts