Achieving globally desirable outcomes in networked multi-agent systems—such as high social welfare, stable allocations, and widespread cooperation—is a fundamental challenge in AI. This paper outlines a research agenda that explores two complementary pathways to this goal. The first is a top-down approach, where a central mechanism designer proposes rules to guide strategic agents towards theoretically optimal equilibria. The second is a bottom-up approach, where desirable farsighted policies, like cooperation in social dilemmas, emerge from the decentralized interactions of agents via multi-agent reinforcement learning. We argue that the integration of these paths constitutes a promising frontier for creating robust and adaptive multi-agent systems.