Runtime Governance for Autonomous AI Systems

Autonomous agents are transitioning from experimental tools to operational systems with real authority over infrastructure, data, and external services. As these systems take on longer-running tasks and broader responsibilities, the risk profile shifts from isolated failures to compounding, persistent exposure.

Current approaches to AI safety focus primarily on intent alignment and output filtering. These methods address what a model should do, but not what it is permitted to do at the moment of execution. This gap becomes critical when agents operate continuously, accumulate context, and interact with production systems.

Runtime governance addresses this gap by enforcing control at the execution layer—where authority is actually exercised. Rather than relying solely on pre-deployment constraints, runtime governance applies identity verification, policy evaluation, and termination controls in real time.

This whitepaper introduces Vallignus, an execution-layer control model designed for autonomous agents. It defines the architectural principles, policy framework, and operational guarantees required for deploying agents in environments where accountability and containment are non-negotiable.