Large Language Models typically fail long-horizon tasks due to persistent error rates, often derailing after only a few hundred steps. This episode explores MAKER, the first system to successfully solve a task requiring over one million LLM steps with zero errors. Discover how Massively Decomposed Agentic Processes (MDAPs) utilize extreme decomposition into focused microagents and efficient error-correcting voting to provide a reliable, scalable paradigm for future AI systems.