The article discusses the concept of an AI engineering stack, outlining the various components and tools necessary for building and deploying AI systems effectively. It emphasizes the importance of a structured approach to integrate AI into existing workflows and highlights key technologies that facilitate this process.

The author shares an experience of using Cursor, an AI coding agent, to autonomously complete a dbt project task by integrating Linear and Supabase MCP servers. Despite some limitations and the need for oversight, the author reflects on the significant advancements in software development workflows and the potential impact of these technologies on various roles within the tech industry.