Abstract
Artificial intelligence (AI) for systems engineering has evolved through six paradigms in five decades: expert systems, machine learning, deep learning, large language models, agentic AI, and agentic swarms, with each paradigm shorter than the last. This position article traces the arc through four systems the authors constructed across the paradigms, Houston (rule‐based requirements), TurboArch (large‐language‐model architecture decisions), GenGroves (agentic model‐based systems engineering), and MACQ (multi‐agent acquisition support), supplemented by published work from researchers across the Systems Engineering Research Center (SERC) community for the machine‐learning and deep‐learning paradigms. A central observation runs through this history: each new paradigm has become a durable substrate for the next. Ontology and machine learning persist into later paradigms, and the article's portfolio of sixteen AI‐for‐SE systems shows how agentic swarms now compose those layers across the engineering lifecycle. The article does not attempt a comprehensive survey of AI; rather, the article identifies a critical inflection point through the lens of the authors and the SERC community. A critical observation from this article is that when organizational adoption lag exceeds paradigm duration, organizations risk investing in yesterday's frontier while missing the durable investment in the layers themselves. The systems engineering community must compress its own adoption cycles or accept permanent strategic lag.