Intelligent systems ought to be distinguished as a special type of systems that require distinctive engineering processes. While this distinction is informally acknowledged by some, practical systems engineering (SE) methodologies for intelligent systems remain primarily rooted in traditional SE paradigms centered around component aggregation. Initially, this dissertation posits that the traditional approach is grounded in the notion of open systems as the fundamental precept, whereas engineering intelligent systems necessitates an alternative approach founded on the principles of closed systems. This dissertation endeavors to identify potential gaps within the current SE foundations concerning the accommodation of the unique characteristics of intelligent systems, such as continuous learning and sensitivity to environmental changes. Furthermore, it argues for the mitigation of these gaps through the formalization of closed systems precepts. It adopts a systems-theoretic perspective to elucidate the phenomena of closed systems and their intricate interplay with engineering intelligent systems. This research contends that, given the intricate coupling between intelligent systems and their environments, the incorporation of closed systems precepts into SE represents a pivotal pathway to construct engineered intelligence. In pursuit of this objective, this dissertation establishes a formal foundation to delineate closed systems precepts and other fundamental practices. Subsequently, it provides formalism to discern two important categories of closed systems, informationally and functionally closed systems, and their relevance in the domains of engineering and design across diverse levels of system abstraction. Additionally, it explores the practical application of the closed systems precepts through the novel paradigm of core and periphery, followed by its examination within real-world contexts. This dissertation is organizes as follows: Chapter 1 initiates the dissertation by presenting the problem formulation and motivation. It subsequently delves into a thorough literature review and outlines the research's scope and objectives, contributing to the essence of this work. In Chapter 2, a narrative unfolds, elucidating the contributions of the provided papers to the objectives outlined in Chapter 1. This chapter illuminates how each paper aligns with and furthers the overarching goals set forth in the Chapter 1. Chapter 3 serves as a culmination, offering a summary of the accomplishments, acknowledging limitations, and delineating potential avenues for future research within this domain. Paper A is devoted to substantiating the closed notion of intelligence property. In the realm of artificial intelligence (AI), systems are often expected to exert influence upon their environments and, reciprocally, to be influenced by their surroundings. Consequently, a profound interdependence exists between the system and its environment, transcending the confines of conventional input-output relations. In this regard, Paper A postulates that the engineering of intelligent systems mandates an approach that elevates closed systems as foundational precepts for characterizing intelligence as a property contingent upon the system's relationship with its context. The ensuing discussion will juxtapose the viewpoints of open and closed systems, illustrating the limitations of the open system perspective in representing intelligence as a relational property. In response, this paper will advocate for the adoption of the closed system view to establish intelligence as an inherent relational property arising from the system's dynamic interactions with its environment. Paper B is dedicated to the formalization of the closed systems paradigm within SE. In this paper, formalism is proffered for the closed systems precepts, drawing upon systems theory, cybernetics, and information theory. A comprehensive comparison of two closure types, informational and functional closure, within closed systems is presented, underpinned by a common systems-theoretic formal framework. This dissertation contends that by grounding these initiatives in the core and periphery concept, we can facilitate the design and engineering of intelligent systems across multiple levels of abstraction. These levels may span a spectrum from informational closure to a synthesis of informational and functional openness. It posits that this approach represents a versatile, method-agnostic solution to some of the principal challenges encountered when engineering multiple tiers of intelligence for complex systems. Paper C delves into the rise of the concept of core-periphery from some cybernetics principles, such as variety and "The Law of Requisite Variety" and provides a formalism that is a derivation of the mentioned principles in Cybernetics. Later, it elaborates on the practical implications of such concepts in intelligent systems from biological systems and entails an engagement with a CNN model to explore the core and periphery concept within AI-enabled systems. Paper D proposes the practical implementation of the closed systems doctrine in SE, offering frameworks that rigorously define the boundaries between closed systems and their environment. These frameworks are meticulously designed to account for stakeholder requirements and the inherent design constraints of the system. This paper illustrates practical applications of informational and functional closure within SE processes, leveraging a hypothetical example for elucidation. It focuses on two aspects of engineering intelligence, scope and scale to provide a platform for the utilization of closed systems precepts. / Doctor of Philosophy / There has been a longstanding call within the Systems Engineering (SE) community for the development of a comprehensive SE theory. This endeavor seeks to bestow upon the field of SE the requisite credibility to stand autonomously as an engineering discipline, capable of addressing the contemporary engineering challenges that confront us. In the pursuit of establishing SE as a distinct engineering field, it becomes imperative to furnish precise and formal definitions for the fundamental concepts that underpin SE processes. Presently, the absence of concrete formalism and clear distinctions surrounding certain core concepts introduces ambiguity into various SE practices. Until recently, the immediate necessity for such foundational formalism was not universally acknowledged or appreciated, as engineers predominantly relied on established practices to design traditional engineered systems. These conventional SE practices had withstood the test of time, until the emergence of a new generation of complex systems characterized by distinctive features. Among these emergent systems, Artificial Intelligent (AI) systems have garnered significant attention, bearing unique attributes that call into question the adequacy of the current SE practices in supporting their development. Consequently, it has been asserted that intelligent systems necessitate the incorporation of new characteristics that render them incompatible with conventional SE practices. This assertion underscores the need for a thorough reevaluation of SE, potentially entailing an expansion of the formalism underpinning its fundamental principles. However, despite these pressing concerns, SE currently lacks a solid theoretical foundation capable of facilitating a paradigm shift away from current practices. The primary objective of this dissertation is to identify the existing gaps responsible for the misalignment between the characteristics of AI systems and prevailing SE practices. Additionally, it seeks to propose innovative methodologies to bridge these gaps effectively. In alignment with this objective, the dissertation provides formalism for these methodologies. Finally, this dissertation aims to provide practical implication of such formalism to validate their applicability. In summary, the central research question, along with the ensuing objectives of this dissertation, can be articulated as follows: What aspects of SE are insufficient for engineering the new characteristics demanded by intelligent systems? What specific actions need to be undertaken to rectify the gaps within SE for intelligent systems? What theoretical foundation and formalism are essential to address these deficiencies within the SE process? What are the practical implications of these efforts for SE processes, as exemplified by real-world scenarios?