Autonomous Systems Architecture defines the structural framework for self-governing systems capable of sensing their environment, making decisions, and taking actions without human intervention, encompassing the components, connections, and protocols that enable autonomous operation.

Autonomous systems present profound architectural challenges due to their inherent complexity and mission-critical nature. For enterprise architects, designing these systems requires a multi-layered approach that integrates perception systems (sensors, data fusion), cognition components (AI/ML decision models), actuation mechanisms, and safety subsystems. The architecture must address both functional aspects (how the system performs its tasks) and non-functional requirements like fault tolerance, graceful degradation, and explainability. Architectural patterns typically include hierarchical decision-making structures with different levels of autonomy and human oversight, particularly for high-consequence decisions. Communication frameworks between autonomous agents and with external systems must be designed for resilience, incorporating robust fail-safes and fallback mechanisms. Enterprise architects should pay particular attention to the verification and validation architecture, including simulation environments, digital twins, and continuous testing frameworks that ensure autonomous behavior remains within defined boundaries. As regulatory frameworks evolve around autonomous systems in various domains, architects must also design for compliance, transparency, and auditability, ensuring systems can demonstrate adherence to relevant standards and explain their decision processes when required.