Autonomous Networking with Artificial Intelligence Agents: A Review of Agentic AI, Network Automation, and Self-Operating Infrastructure
Abstract
The emergence of agentic Artificial Intelligence introduces a new paradigm in which AI systems can perceive their environment, reason about objectives, use external tools, and execute multi-step actions with limited human intervention. This capability has significant implications for the evolution of autonomous computer networks. This review investigates the emerging concept of agentic AI for network operations, focusing on network monitoring, configuration, troubleshooting, security operations, capacity planning, traffic optimization, and service orchestration. The paper examines how large language models, AI agents, retrieval-augmented generation, tool calling, and knowledge-based reasoning can interact with network controllers, telemetry systems, configuration interfaces, and digital twins. A taxonomy of AI agents for networking is proposed based on their autonomy, decision-making capabilities, communication mechanisms, and operational scope. The review critically examines challenges related to hallucination, unsafe configuration changes, authentication, authorization, explainability, verification, and human oversight. Finally, future research directions toward verifiable, secure, multi-agent, and self-healing autonomous networks are identified
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