Ankur Goyal argues that AI agents can outperform human engineers in rigorous benchmarking, such as testing different database indexes and execution engines to optimize slow queries, a task often skipped due to its tedious nature. He introduces the 'agent line' framework for deciding which decisions to hand off to an agent, emphasizing that AI maintains focus on hard problems where human attention decays. The conversation demystifies evals as a methodology for defining success, encoding 'what good looks like' so a model can figure out the 'how'. A key insight is turning personal taste, like a designer's aesthetic, into a repeatable scoring function to scale quality beyond one person. For AI-accelerated teams, fixing CI is the highest-leverage way to speed up engineering velocity, as the primary job becomes building a feedback loop to turn real-world data into evals. When agents fail, the strategy is to close the session, improve the evals, and retry from scratch.