AI Agents for Autonomous Network Operations: Architecture and Evaluation
Dr. Laurent Ciavaglia, Dr. Razvan Beuran
Rakuten Mobile / NICT Japan
Abstract
We present an AI agent architecture for autonomous network operations where LLM-based agents plan and execute multi-step network management tasks. Our system decomposes high-level operator intents into sequences of actions, executes them with safety guardrails, and learns from outcomes to improve future performance. In a production trial managing 200 base stations, the AI agent successfully resolved 78% of common network incidents autonomously, reducing mean time to repair by 65%.
AI Summary
- LLM-based AI agent architecture for autonomous network operations.
- 78% of common incidents resolved autonomously in production trial.
- 65% reduction in mean time to repair across 200 base stations.
- Safety guardrails prevent dangerous actions during autonomous operation.
Key Findings
- 1LLM agents can decompose complex intents into executable network actions.
- 2Safety guardrails using pre/post-condition checks prevent 99.5% of potentially harmful actions.
- 3Agent performance improves 15% over 3 months through continuous learning from outcomes.
Industry Implications
Demonstrates feasibility of autonomous network operations for 6G.
Provides a practical architecture for intent-based network management.
Addresses the operator skills shortage through AI automation.
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