Some AI Influencer Told Me I Didn't Need Evals (agent-ci.com)

Travis Dent, CEO of Agent CI, argues that evaluations are as essential to AI agent development as unit and integration tests are to traditional software. Early-stage agent work feels iterative and “magical,” but as prompts, tools, context windows and instructions evolve, behaviors that once seemed repeatable will drift. Without systematic evaluations, teams lose confidence that core tasks still work after changes, increasing the risk of subtle regressions and production failures. Technically, Dent urges teams to build evaluation suites alongside the application: codified checks for early use cases, regression tests that quantify behavior over time, and continuous monitoring of interactions and edge cases. Treating evaluations like automated unit tests—run in CI, measuring metrics and preserving expected behaviors—helps catch unintended interactions between new features and legacy behaviors. The piece is a practical reminder that dismissing evals as optional is a risky, novice stance; robust, repeatable evaluation practices are necessary to scale AI agents safely and reliably.