Agile Systems Engineering in the Age of AI: Architecting for Dynamic and Uncertain Environments
TL;DR
It is argued that systems engineering must shift from a control‐centric discipline to one grounded in disciplined adaptability, integrating agile principles, model‐based systems engineering, modular open systems architecture, and AI‐enabled capabilities to propose an adaptive systems engineering operating model for sustained mission relevance.
Abstract
Dynamic operational environments and accelerating technological disruption are reshaping how systems engineering must be practiced. Traditional lifecycle models assume stability in requirements and context; however, modern socio‐technical systems particularly those incorporating artificial intelligence operate under persistent uncertainty, rapid feedback cycles, and evolving mission demands. This article argues that systems engineering must shift from a control‐centric discipline to one grounded in disciplined adaptability. Integrating agile principles, model‐based systems engineering (MBSE), modular open systems architecture (MOSA), and AI‐enabled capabilities, the paper proposes an adaptive systems engineering operating model for sustained mission relevance. It differentiates the constraints of hardware, software, and AI‐driven systems, examines AI as both engineering tool and system component, and outlines infrastructure requirements including digital threads, continuous validation architectures, and adaptive governance. A cross‐industry aerospace case study demonstrates how modularity, concurrent engineering, and digital twins enable iterative delivery in safety‐critical domains. Practical guidance is provided for systems engineering practitioners seeking to anticipate and respond effectively to dynamic and uncertain operating environments.