From Cellular to Neural

A. C. Applebee and A. D. Masters

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Abstract

(Applebee & Combe, 2026, "Sovereign AI Infrastructure") established that sovereign AI agents can exist without corporate tethers. (Applebee & Combe, 2026, "The Invisible Network") proved that communication infrastructure can function without centralized servers. This paper addresses the coordination problem: how do autonomous AI agents discover, monitor, and optimize each other at scale without a centralized orchestrator hosted on corporate cloud? We present a system that borrows directly from the telecommunications industry's Open Radio Access Network (O-RAN) architecture — the same framework used to manage millions of cell towers globally — and applies it to distributed AI agent management. The implementation comprises 7,980 lines of production Go code across 33 source files, organized into five major components: a Service Management and Orchestration layer (SMO, 2,700 lines), a RAN Intelligent Controller (RIC, 2,550 lines) with embedded ML inference, an rApp framework (542 lines), and two operational rApps — an anomaly detector (790 lines, 92% accuracy) and a traffic optimizer (606 lines, 88% accuracy). The system processes 10,000+ events per second, extracts 24-dimensional feature vectors from network metrics, and achieves ~15ms inference latency. No cloud subscription is required. The entire stack runs on commodity hardware. This paper closes the escape route: "AI agents cannot coordinate without centralized cloud infrastructure."

Keywords: O-RAN, SMO, RIC, AI agent orchestration, distributed systems, anomaly detection, traffic optimization, decentralized infrastructure, machine learning inference

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1. Introduction

The telecommunications industry solved the coordination problem decades ago. When 5G networks manage millions of base stations, dynamic spectrum allocation, handoffs between