Evaluations Close the Trust Gap for AI Agents—But Architecture Starts It
Monte Carlo argues that evaluating AI agents is only the final barrier to trust; the real work lies in designing robust data pipelines, prompting strategies, and monitoring systems. Skipping these foundations leads to brittle agents, no matter how thorough the evals. Their framework blends engineering and evaluation to achieve reliable, trustworthy agents.