Computing is undergoing a fundamental transition, with performance and efficiency gains increasingly driven by specialized accelerators. Yet a longstanding disconnect remains between how these accelerators are designed, how they are modeled and characterized, and how they are programmed. This gap slows hardware innovation, complicates the software stack, and makes accelerators far harder to evolve than the rapidly changing applications they are meant to serve. While increasingly capable coding agents can help alleviate some of these challenges, many key pieces are still missing to truly close this loop. In this talk, I will share lessons from our recent work on (1) workload mapping for emerging accelerator architectures, (2) abstractions that help unify accelerator design and programming, and (3) agentic approaches to compiler construction. I will also discuss how these directions may collectively move us closer to a future of more autonomous accelerator design.