Mind the gap: What separates demo agents from production systems

Deploying AI models into production reveals significant gaps not apparent in demonstrations. The "Validation Gap" refers to the difficulty in automatically selecting the correct output from multiple model attempts, even when a correct answer is present. Similarly, the "Contextualization Gap" describes the inefficiency of using large, expensive models for tasks that smaller, cheaper models could solve, pointing to the need for intelligent task routing. Successfully building production AI systems requires addressing these issues of validation, orchestration, and cost, moving beyond simple leaderboard metrics to create a more robust AI system.