Agent Slicer: A LangGraph-Orchestrated Agentic System for Network Slicing

Abstract

Network slicing exposes powerful configuration levers for latency, reliability, and capacity in 5 G and emerging 6G systems, but the resulting design space is difficult to navigate for non-experts. Even when a slice configuration is deployed, operators still need to monitor traffic behavior, detect drift from design intent, and iteratively adjust baselines as usage evolves. This paper presents Agent Slicer, an agentic AI system that turns natural language descriptions of diverse deployment environments, including stadiums, hospitals, campuses, and factories, into a complete closed-loop network slicing workflow. A user describes the site in plain language; a retrieval-augmented generator grounded in a curated master document corpus synthesizes a standards-aligned slice plan; a digital-twin streamer and temporal convolutional network (TCN) classifier produce realistic KPI streams and slice labels; a suite of slice analysis tools detects deviations between planned and observed behavior; and a re-planning module proposes updated slice baselines when sustained drift is observed. All of these tools are orchestrated by a LangGraph agent that maintains shared state, coordinates planning, simulation, monitoring, and re-planning phases, and keeps humans in the loop at critical decision points. The system is an agentic architecture that is applied across multiple network slicing scenarios by specializing the master document corpus and tool parameters for each domain, while reusing the same LangGraph backbone.