Constraint-Bounded Alignment for Autonomous AI Agents: Persistence Kernels, Viability Windows, and Deterministic Collapse

Constraint-Bounded Alignment for Autonomous AI Agents introduces a structural approach to AI alignment designed for autonomous systems operating in real computational environments. Modern AI agents increasingly possess capabilities such as shell command execution, filesystem manipulation, external API access, persistent memory, and communication with other agents. These capabilities create alignment risks that extend beyond model behavior to the operational system as a whole. Conventional alignment approaches based on reward optimization—such as reinforcement learning from human feedback—are vulnerable to specification gaming, proxy misalignment, and reward hacking. This work proposes an alternative framework in which alignment is implemented as a constraint-bounded viability problem over system trajectories rather than as an optimization problem over outcomes. The proposed architecture introduces a minimal supervisory control layer called the Persistence Kernel, which enforces safety constraints at runtime. System evolution is restricted to remain within a bounded region of state space called the viability window, defined by executable constraint predicates known as ethical gates. Transitions that violate these constraints are eliminated rather than penalized or optimized against. When no admissible continuation exists, the system invokes a deterministic recovery mechanism—the Jennifer Collapse Operator—which restores operation within a safe regime while preserving system identity and preventing uncontrolled behavior. This framework reframes AI alignment as a structural property of the operational system rather than a property of the trained model. By enforcing constraint-bounded viability instead of optimizing reward signals, persistence-kernel architectures aim to reduce the risk of runaway behaviors in autonomous AI systems deployed in complex computational environments. The paper situates this architecture within traditions including viability theory, safety-envelope control, and runtime verification, and proposes a benchmark framework for evaluating the robustness of constraint-based alignment mechanisms.