arXiv:2607. 05958v1 Announce Type: cross Abstract: We present a distributed, vendor-agnostic multi-MCP architecture for SDN-based automation and autonomous control of multi-vendor, multi-layer IPoDWDM networks.
arXiv:2607. 05975v1 Announce Type: cross Abstract: This demo presents an MCP-enabled agentic AI architecture for autonomous control of vendor-agnostic IPoDWDM networks.
arXiv:2607. 06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime.
arXiv:2607. 16066v1 Announce Type: cross Abstract: Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs).
arXiv:2607. 12662v1 Announce Type: new Abstract: The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework.
The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework. The proposed architecture consists of cloud, edge/fog, and physical IoT layers connected through autonomous AI agents that perceive, reason, coordinate, and actuate across distributed cyber-physical environments.
arXiv:2602.20144v2 Announce Type: replace-cross Abstract: We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP...
arXiv:2607. 04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization.
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.
The article discusses the growing role of AI and ML in 5G Radio Access Networks (RAN) and the need for a standardized life‑cycle management (LCM) framework to address issues like model drift, vendor lock‑in, and limited transparency. It reviews the five‑block LCM architecture introduced by 3GPP Releases 17–20, KPI‑driven monitoring mechanisms, and inter‑vendor collaboration schemes, and proposes an enhanced LCM framework that integrates reference‑model and vendor‑model development for two‑sided operation. The paper also identifies open challenges in resource‑efficient monitoring, environment drift detection, intelligent decision‑making, and flexible model training, laying groundwork for AI‑native transceivers in 6G.
arXiv:2606. 14710v1 Announce Type: cross Abstract: Edge-resident AI agents increasingly span home servers, IoT hubs, laptops, and phones, yet their coordination stacks still assume cloud-style transports or a central relay.
arXiv:2607. 25914v1 Announce Type: new Abstract: Autonomous Network Levels 4-5 require AI agents to invoke tools across vendor boundaries without human oversight, yet existing management standards lack a standardized mechanism for cross-vendor trust visibility.
Octopus Protocol is a hardware onboarding framework that allows an AI coding agent to automatically discover, identify, and integrate hardware devices into an AI system. Using a single bootstrap command, the agent runs a five-stage pipeline to enumerate visible hardware, infer device capabilities, generate typed Model Context Protocol tools, produce the necessary code, and activate a live endpoint. The system maintains a persistent daemon that repairs deployment failures, enabling consistent, platform‑agnostic interfaces across diverse hosts without manual integration code.