The growing complexity of digital services and large-scale computing environments has accelerated the need for software systems capable of adapting autonomously to changing operational conditions. Traditional software architectures, which rely heavily on static configurations and manual optimization, are increasingly insufficient for managing dynamic and data-intensive digital ecosystems. Autonomous software systems introduce a new architectural paradigm in which platforms continuously monitor their own behavior, analyze operational data, and adjust system performance through intelligent decision mechanisms. This paper explores architectural principles for designing self-optimizing digital platforms capable of autonomous adaptation. It examines the integration of distributed architectures, real-time data processing, and machine learning–driven decision mechanisms within modern software infrastructures. The study further discusses how feedback-driven optimization cycles and system observability enable software platforms to maintain performance, scalability, and resilience in complex environments. By outlining key architectural design principles, this research contributes to the emerging field of autonomous software engineering and provides guidance for building adaptive digital platforms capable of supporting next-generation digital services.