Demystifying evals for AI agents

What this post added

This post introduces comprehensive strategies and definitions for evaluating AI agents, particularly in the context of software engineering. It details the importance of rigorous evaluations to move beyond reactive debugging and ensure agent reliability. Key contributions include defining evaluation terminology (task, trial, grader, transcript, outcome, harness, suite), differentiating between single-turn, multi-turn, and agent evaluations, and highlighting the progression from manual testing to sophisticated automated evals. It also provides specific examples and techniques for evaluating coding agents, emphasizing the use of deterministic graders (unit tests) and model-based graders for code quality and interaction assessment. The post underscores the value of capability and regression evals in the agent lifecycle.