Demystifying evals for AI agents

This post details Anthropic's approach to developing and implementing rigorous evaluation (eval) strategies for AI agents, particularly focusing on coding and conversational agents. It defines key terms like task, trial, grader, transcript, outcome, evaluation harness, agent harness, and evaluation suite. The post emphasizes the transition from manual testing to automated evals, including single-turn and multi-turn evaluations, and highlights the importance of both capability and regression evals. It provides examples of how teams like Descript and Bolt have implemented eval systems, and discusses the benefits of evals for accelerating development, enabling faster adoption of new models, and serving as a communication channel between product and research. The post also touches upon the use of code-based, model-based, and human graders, and the specific evaluation needs for coding agents (e.g., SWE-bench Verified, Terminal-Bench) and conversational agents.