The increasing complexity of engineered systems, coupled with the integration of adaptive computational intelligence, necessitates a comprehensive framework grounded in systems theory, cybernetics, and feedback-based design methodologies. This research investigates the convergence of systems-theoretic principles, cyber-physical systems (CPS), and adaptive software architectures to elucidate mechanisms for effective problem-solving, decision-making, and resilience in complex engineering contexts. Drawing upon seminal works in systems theory, organizational learning, and design science (Ashby, 1958; Beer, 1995; Argyris, 1976), the study examines the interplay between structural and functional complexity in product development, the dynamics of feedback loops in self-regulating systems, and the evolving role of artificial intelligence in networked and distributed systems. A qualitative synthesis of existing literature, combined with conceptual modeling of cyber-physical feedback architectures, provides a detailed exploration of mechanisms through which adaptive intelligence can be operationalized in engineering design. The findings highlight that multi-layered feedback mechanisms, informed by both double-loop learning and requisite variety, are central to managing emergent behaviors, enhancing robustness, and supporting design creativity. Furthermore, the integration of machine learning and transformer-based anomaly detection models within engineering workflows offers a scalable approach for system monitoring, fault detection, and real-time optimization. The research underscores the critical importance of aligning system objectives, design processes, and adaptive control strategies to achieve resilient, sustainable, and self-aware engineering outcomes. Implications extend across software engineering, cyber-physical integration, and organizational design, suggesting avenues for future research in adaptive, self-learning system architectures and automated feedback mechanisms.

In technical domains, particularly asset management and maintenance, vast amounts of valuable information are captured in unstructured text formats such as work orders, maintenance logs, and inspection reports [1, 4, 28, 30]. Extracting actionable insights from this "technical language" is challenging using traditional methods [1, 15]. Concurrently, Failure Mode and Effect Analysis (FMEA) is a widely adopted systematic approach for identifying potential failure modes within a system, analyzing their causes and effects, and prioritizing risks [2, 6]. While powerful, traditional FMEA is often a manual and labor-intensive process, relying on expert knowledge and structured data [8]. This article explores the integration of FMEA principles with Technical Language Processing (TLP) techniques to automate and enhance the extraction, analysis, and utilization of failure-related information from unstructured technical text. We propose a conceptual framework where TLP methods are employed to identify potential failure modes, effects, and causes within technical documentation. These extracted elements can then be structured and analyzed using FMEA principles to provide data-driven insights into asset reliability and maintenance needs [5]. This integration has the potential to improve the efficiency and effectiveness of FMEA, unlock hidden knowledge within maintenance data, and support proactive maintenance strategies.