New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget

Imagine your engineering team just deployed an AI agent to search through internal company documents and answer employee questions. It works perfectly in development, but in production, it consistently hallucinates or misses key constraints. Fixing this is rarely a simple patch. It requires a tedious, trial-and-error process of tweaking chunking strategies, retrieval methods, and system prompts simultaneously. Because these adjustments are entangled, it becomes nearly impossible to attribute whi...