# Large Language Model-Driven Cross-Domain Orchestration Using Multi-Agent Workflow

arXiv: https://arxiv.org/abs/2410.10831 · HTML: https://mlsanity.com/paper/2410.10831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 2 of 2)

- [Large Language Models for Networking: Applications, Enabling Techniques, and Challenges](https://mlsanity.com/paper/2311.17474/) (2023)
- [Digital Twin of a Network and Operating Environment Using Augmented Reality](https://mlsanity.com/paper/2303.15221/) (2023)

## Built on by (most cited first, 1 of 1)

- [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025)

## Abstract (arXiv)

We showcase an application that leverages multiple agents, powered by large language models and integrated tools, to collaboratively solve complex network operation tasks across various domains. The tasks include real-time topology retrieval, network optimization using physical models, and fiber switching facilitated by a robotic arm.