Spending computation on structured decision-making about *how* to solve a problem—not just solving it directly—becomes increasingly valuable as agent tasks scale to longer horizons.
This paper introduces agentic meta-reasoning, a control system that helps AI agents manage long, complex tasks by making explicit decisions about which work to pursue, when to restart, and when to stop. A controller tracks progress compactly and decides next steps, while workers execute the actual task.