Coding agents as runtime systems

One of the most technically interesting AI and coding stories from the last couple of weeks is the continued shift from code completion to full agent runtimes. OpenAI’s recent platform documentation emphasizes agent workflows with tools, logic nodes, trace grading, datasets, and the Agents SDK, which is a very different abstraction from the old “generate a function from a prompt” model. The center of gravity is moving upward, from token prediction to orchestration.  That matters because the hard part of serious coding is rarely local syntax. It is state, tool use, evaluation, and recovery after failure. Once an agent can call tools, inspect outputs, branch on conditions, and feed traces back into evaluation loops, the software problem starts to look less like autocomplete and more like a distributed runtime with an LLM inside it. In that architecture, prompts matter less than execution semantics.  The deeper point is that coding AI is becoming an infrastructure problem. Reliability n