You can't judge an AI agent until you know what the task is worth

This article discusses the challenge of evaluating AI coding agents by comparing their cost to the actual value of the task they perform. It uses examples like scaffolding a project or renaming files to illustrate that a dollar spent on an agent can be a bargain or a rip-off depending on the task's worth. The author argues that our pricing instincts fail with LLMs because they are new, costs are fuzzy, and payment is often delayed or not felt directly. The piece emphasizes that small costs compound across teams, making it crucial to assess task value rather than just agent price. It is relevant to IT/technology as it addresses software development workflows, AI tools, and cost efficiency in engineering.