How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads

Building reliable AI agents requires a robust evaluation framework that evolves alongside the product. Establishing a strong foundation with optimized, LLM-friendly tools and remediation loops is essential before scaling. Early-stage development benefits from an intuition-based "vibing" approach, allowing for rapid iteration and a deeper understanding of failure patterns. As systems mature, transition to structured evaluation methods by providing human raters with clear rubrics, specific examples, and requirements for detailed explanations. Rather than hyper-fixating on isolated model failures, prioritize analyzing broader performance patterns across expansive golden sets. Continually refine these evaluations using online data and cross-functional feedback to ensure the agent remains aligned with production goals and maintains high-quality outputs despite the inherent non-determinism of generative models.