Convergence, as described by Adrian Brudaru, focuses on utilizing a framework that emphasizes building robust, scalable systems using a declarative approach rather than fragile, imperative methods. This method contrasts "crutches" with "scaffolds," where crutches represent temporary, rigid solutions that fail when models update, while scaffolds are flexible structures that guide models toward desired outcomes regardless of changes. The concept of "Engineered Convergence" involves integrating context, goals, and validation processes, enabling systems to effectively adapt and self-correct. The approach democratizes data engineering by enabling non-experts to safely contribute, reducing bottlenecks and maintenance burdens, and empowering teams to evolve with advancing AI capabilities. This methodology is exemplified by the "Generate running connector" loop and a pipeline dashboard that together ensure both technical correctness and business relevance, allowing teams to focus on innovation instead of constant adaptation to model updates.