In this episode, we discuss Solving a Million-Step LLM Task with Zero Errors by Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz Shahrzad, Olivier Francon, Conor F. Hayes, Xin Qiu, Babak Hodjat, Risto Miikkulainen. The paper presents MAKER, a system that achieves error-free execution of tasks requiring over one million steps by decomposing them into subtasks handled by specialized microagents. This modular approach enables efficient error correction through multi-agent voting, overcoming the persistent error rates that limit standard LLM scalability. The findings suggest that massively decomposed agentic processes offer a promising path to scaling LLM applications to complex, large-scale problems beyond individual model improvements.