From Intents to Actions: Agentic AI in Autonomous Networks

TL;DR

This paper presents an agentic AI framework for autonomous networks that interprets high-level intents, reasons over trade-offs, and autonomously executes control actions to meet diverse service requirements.

Contribution

It introduces a novel multi-agent AI system combining language models, optimization, and reinforcement learning to translate network intents into actionable control strategies.

Findings

Enables autonomous interpretation of diverse network intents.

Balances multiple objectives near Pareto optimality.

Adapts to changing network conditions dynamically.

Abstract

Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that is, performance objectives, constraints, and requirements specific to each service. However, transforming high-level intents -- such as ultra-low latency, high throughput, or energy efficiency -- into concrete control actions (i.e., low-level actuator commands) remains beyond the capability of existing heuristic approaches. This work introduces an Agentic AI system for intent-driven autonomous networks, structured around three specialized agents. A supervisory interpreter agent, powered by language models, performs both lexical parsing of intents into executable optimization templates and cognitive refinement based on feedback, constraint feasibility, and evolving network conditions. An optimizer agent converts these templates into tractable optimization problems, analyzes trade-offs, and derives preferences across objectives. Lastly, a preference-driven controller agent, based on multi-objective reinforcement learning, leverages these preferences to operate near the Pareto frontier of network performance that best satisfies the original intent. Collectively, these agents enable networks to autonomously interpret, reason over, adapt to, and act upon diverse intents and network conditions in a scalable manner.