This paper proposes a hierarchical architecture for long-horizon agents, addressing the need for agents to learn and operate over extended periods without context resets. The architecture consists of three parts: levels indexed by time scale, a clocked tick as the unit of autonomous action, and cascaded intelligence. The authors demonstrate the effectiveness of this architecture through a ten-day campaign, showcasing the agent's ability to reproduce a published reinforcement-learning result with minimal human intervention.