This research paper introduces the Agency Efficiency Principle and a methodology called LIMI (Less Is More for Intelligent Agency), arguing that developing autonomous AI systems requires strategically curating small datasets of high-quality agentic demonstrations rather than scaling data volume. The authors define Agency as the capacity for autonomous reasoning, acting, and tool use in complex workflows, specifically focusing on vibe coding (collaborative software development) and research workflows. Experimental results presented using the AgencyBench benchmark show that the LIMI model, fine-tuned on only 78 curated samples, significantly outperforms state-of-the-art baseline models trained on datasets that are orders of magnitude larger, validating the Less-Is-More hypothesis for agentic intelligence. The document also provides extensive details on the AgencyBench tasks, which involve multi-step, complex problems requiring execution in a command-line interface environment.