Developers often struggle with insufficient evaluation sets that rely on a few successful demo examples, leading to production failures that remain undetected. Patching prompts based on these limited sets fails to address systemic issues, making it difficult to determine if a fix generalizes to broader problem classes. Effective AI agent evaluation requires robust datasets that move beyond simple demonstrations to ensure production readiness.