The text discusses the evolution of knowledge work tools and the integration of software engineering practices into this domain, emphasizing how traditional tools like Microsoft Word, Excel, and PowerPoint are being replaced by more integrated and efficient systems utilizing modern technologies such as Markdown, CSV files, local databases like DuckDB, and version control with Git. The author describes how advancements in artificial intelligence, specifically large language models (LLMs) and the Model Context Protocol (MCP), have transformed their workflow by allowing natural language to serve as an interface for managing tasks across various systems, thus reducing the need for manual context switching. The introduction of FastMCP, a Python framework for connecting LLMs to tools and data sources, allows for seamless integration and orchestration of tasks, thereby enabling more effective and streamlined processes. This new approach leverages AI to maintain a consistent flow of context and automate complex workflows, positioning data engineers as crucial players in building the necessary context layers for AI-driven enterprise systems.