How to Run Better Research With Agent Fleets

The author outlines a practical approach to conducting AI research using fleets of autonomous agents, proposing a mental model of research as a double loop. The outer loop remains human-driven for tasks like defining goals and measures, while the inner loop is operationalized and frozen so agent fleets can efficiently test hypotheses in parallel. The author argues that freezing pipelines and protecting core scripts prevents agents from making arbitrary, hard-to-compare methodological choices. Open article →