Scaling Autonomous Decision Systems: A Framework for Organizational Deployment - Suganya Nagarajan (International Journal of Computational and Experimental Science and Engineering, 2026)
Autonomous decision systems have become foundational infrastructure in large-scale digital environments, mediating customer interactions across retail, payments, and subscription services at throughputs that demand both organizational coherence and operational precision. Yet the predominant failure mode in these systems is not algorithmic, it is structural, arising from ambiguous ownership boundaries, duplicated safety controls, and inconsistent enforcement across heterogeneous product teams operating on shared customer-facing surfaces. This article presents a system-level framework that treats autonomy as an organizational capability rather than a property of individual services. The framework introduces a three-layer separation of responsibilities: decision intent, execution permission, and content selection distributed across product teams, a centralized autonomy platform, and presentation layers respectively. The autonomy platform is responsible for enforcing execution safety consistently across teams while remaining independent of business logic and presentation decisions.To support low-latency execution environments, the framework distinguishes between control-plane policy computation and runtime policy enforcement, allowing policy decisions to be computed asynchronously while enabling lightweight enforcement during request execution. The article also identifies scheduled execution as a significant source of latent risk and proposes runtime permission evaluation as a mechanism for maintaining safety under changing system conditions.