Safety-Bounded Autonomy in Distributed Autonomous Systems

This paper introduces the concept of safety-bounded autonomy as an architectural framework for constraining autonomous behavior in distributed autonomous systems. Autonomous systems increasingly operate in dynamic, uncertain, and partially unpredictable environments. Traditional safety approaches—based on design-time validation, static policies, or post-hoc monitoring—become insufficient as systems incorporate learning-based components, runtime adaptation, and distributed coordination. This work proposes safety-bounded autonomy as a system-level architectural paradigm in which autonomous decision-making is structurally separated from execution and continuously constrained through runtime governance mechanisms. Rather than assuming that system behavior can be fully anticipated in advance, the architecture enforces safety at the point of execution through dedicated control and validation layers. At its core, the approach introduces a strict separation between:- capability definition (what the system is allowed to do)- execution control (how actions are carried out)- runtime safety enforcement (whether actions remain admissible under current conditions) This separation ensures that autonomous decisions cannot directly result in physical actions without passing through enforceable governance boundaries. The proposed architecture is guided by three fundamental principles: Separation of Autonomy and Execution Autonomous modules may generate candidate actions based on perception, planning, or learning processes. However, these actions are not executed directly. Execution is mediated through governance mechanisms that evaluate admissibility before actuation. Governance Before Execution All capability activations are subject to prior validation, including authorization, policy compliance, and contextual constraint evaluation. This establishes a control boundary between decision generation and execution. Runtime Safety Supremacy Safety enforcement operates independently of decision-making processes and may override, modify, or halt execution at runtime. Safety constraints take precedence over performance or optimization objectives. The architecture extends naturally to distributed autonomous systems by introducing coordinated governance mechanisms across multiple agents. Safety evaluation may occur locally, centrally, or through distributed control structures, enabling scalable enforcement across robotic fleets and multi-agent environments. Safety-bounded autonomy is intentionally defined at the architectural level. It does not prescribe a specific technical implementation, but rather defines enforceable design principles that can be realized through different system-level mechanisms, including software-based control layers, hardware-assisted enforcement, or distributed governance infrastructures. This work further introduces:- a structured capability lifecycle model for governing system functionality- runtime constraint validation mechanisms for enforcing operational boundaries- fleet-level governance concepts for coordinated safety enforcement across distributed systems By embedding safety enforcement directly into the system architecture—rather than relying solely on monitoring or external supervision—the proposed approach provides a deterministic and enforceable foundation for runtime safety in autonomous systems. This contribution supports ongoing research in robotics, AI safety, and cyber-physical systems, and is particularly relevant for safety-critical, learning-enabled, and distributed autonomous environments. This work is part of a broader research program on governance and execution control in autonomous systems, including capability lifecycle governance, non-bypassable execution control, and architecture-level safety enforcement. The concept of safety-bounded autonomy provides a foundational design paradigm for building controllable, trustworthy, and verifiable autonomous systems operating under real-world conditions.