Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different.  Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so, these systems push token consumption, context length, and latency requirements into extremely demanding regions  — exactly the pressures now shaping the NVIDIA extreme co-design stack and the NVIDIA Vera Rubin platform. This post analyzes that evolution across three parts:  How agents consume tokens Why their economics break under conventional serving What an infrastructure stack purpose-built for agents looks like Transition to agents from chatbots As shown in Figure 1, below, the popularization of generative AI began with a simple interaction model: one user message, one chatbot message, repeat.…