The Defense Department is rethinking engineering design and development of defense systems to targets crucial design decision-making regarding system cost, schedule, capabilities, and robustness. The goal is to increase the quality of communication between engineers and program decision makers in conducting comprehensive systems engineering tradeoff analyses for optimum solution selection. This paper describes a novel paradigm for facilitating thoughtful, informed design that makes systems effective and reliable in a wide range of contexts. A key element lies in increasing the speed and reducing the cost of injecting engineering rigor early in the design process. Advances in model-based systems engineering and widely available high-performance / cloud computing enable the “up-front” engineering to explore a much greater number of alternatives, do so in greater depth, and keep those alternatives active much further into the engineering process. One benefit is that unintended consequences of design decisions would be caught much earlier in the systems life cycle, when recovering is far less expensive. Another benefit is increased ability of decision makers to keep options open when refining requirements and choosing engineering solutions’ essential to better managing requirements growth. Studies show that problems discovered later in the system development lifecycle can be literally as much as 100 times more time-consuming and expensive to fix. We also know that, the less up-front engineering is done, the more likely a program is to fail. But, for program managers to invest in the up-front engineering which is needed to avoid this, we need to significantly reduce the time and cost of doing that early work. This paper describes an envisioned distributed collaborative environment with these properties. It supports design teams, in consultation with users and decision makers, in guiding automatic generation of many design variations, while propagating changes and maintaining constraints across each variation. The paradigm includes learning from live and virtual operational systems and using synthetic environments for experimentation and learning. This helps engineers and users collaboratively introduce and evaluate many usage scenarios. The environment heavily emphasizes aids to analyze alternative designs and help engineers and other stakeholders compare and understand technical and operational tradeoffs. A key use of the environment is to manage requirements creep by enabling engineers, project managers and stakeholders to iteratively refining requirements in light of feasibilities and opportunities, with a full understanding of consequences.