Over time, optimization should become a repeatable operating loop: production traces reveal failure modes; failure analysis generates candidates; evaluations identify promising changes; controlled rollouts validate them; and production feedback informs the next iteration. Build and operate systems that continuously search for better AI configurations across prompts, model selection, routing, reasoning budgets, context construction, tool use, agent workflows, retries, caching, parallelism, and workflow-specific policies.