New research on inverse rubric optimization shows that basic prompt elicitation and scaffolding can double agent performance. The study reveals that LLMs are under-elicited by default, failing to use available resources effectively. Practitioners can achieve substantial gains by intervening in how agents iteratively optimize metrics. This suggests current agentic workflows remain significantly under-optimized.