Non-Bypassable Execution Control in Autonomous Systems: Architectural Principles for Runtime Safety Enforcement

This paper presents the execution control layer within a broader governance architecture for autonomous robotic systems. The execution control layer ensures that all system actions are subject to non-bypassable enforcement mechanisms. Related works: - A Governance Architecture for Safe and Bounded Autonomous Robotics Systems (core architecture) - Capability Lifecycle Governance in Autonomous Robotic Systems (capability layer) - Safety-Bounded Autonomy: Architectural Safety Enforcement for Distributed Robotic Systems (safety layer) This paper introduces the concept of non-bypassable execution control as a foundational architectural principle for ensuring runtime safety in autonomous systems. As autonomous and AI-driven systems become increasingly adaptive, traditional control mechanisms—based on pre-defined rules or post-hoc monitoring—become insufficient. The core challenge is no longer only to define what a system should do, but to guarantee that only authorized actions can ever be executed at runtime, regardless of learning, external inputs, or system evolution. The proposed approach establishes an execution control architecture in which all system actions must pass through a mandatory, non-bypassable control layer. This layer acts as a structural enforcement point, ensuring that execution is strictly constrained by predefined and validated capability boundaries. Key architectural principles include: Mandatory Execution Path EnforcementAll actions must traverse a controlled execution pathway. Direct or indirect bypass mechanisms are structurally eliminated, ensuring that no component can execute actions outside the governed control flow. Capability-Gated ExecutionActions are only executable if they are explicitly authorized within a predefined capability framework. This enforces a strict linkage between capability definition and runtime execution. Runtime Safety SupremacySafety enforcement mechanisms operate independently and override any execution process if constraints are violated, ensuring that safety cannot be subordinated to performance or optimization goals. Separation of Decision and ExecutionDecision-making components (e.g., AI models) are decoupled from execution authority, preventing learned or emergent behaviors from directly triggering actions without control validation. The architecture is designed to be robust against adaptive, learning-enabled, and distributed system behaviors, where traditional validation and control approaches fail due to system complexity and non-determinism. By enforcing execution control at the architectural level, this work provides a framework for building provably constrained and trustworthy autonomous systems, applicable across domains such as robotics, industrial automation, and safety-critical AI environments. This work is part of a broader research program on governance and execution control in autonomous robotic systems, including capability governance, safety-bounded autonomy, and non-bypassable execution control architectures.