Work

MeshAgent: Enabling Reliable Network Management with Large Language Models

Year: 2025

Type: article

Abstract: Large language models (LLMs) are promising for domain-specific agents, but adapting them to network management remains difficult because existing specialization methods depend on large amounts of high-quality task data, while real network queries are diverse and unpredictable. To address this, we propose MeshAgent, a workflow that extracts reusable domain invariants from sample queries and encodes them as constraints to guide LLM generation and validation. Across three network management applications and a user study with industry professionals, MeshAgent consistently improves accuracy and reliability, including the ability to abstain when confidence is low. MeshAgent achieves over 95% accuracy, reaches 100% when combined with fine-tuned agents, and improves accuracy by up to 26% over baselines. These results show that constraint-based adaptation is a practical and scalable alternative to data-heavy LLM specialization for network management.

Source: Proceedings of the ACM on Measurement and Analysis of Computing Systems

DOI: 10.1145/3771567

Authors: Yajie Zhou, Kevin Hsieh, Sathiya Kumaran Mani, Srikanth Kandula, Zaoxing Liu

Institutions: University of Maryland, College Park, Microsoft (United States), Amazon (United States)

Language: English

Topic: Mobile Agent-Based Network Management

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: Collaborative Research: CNS Core: Medium: FROOT: Future-Proof, Trustworthy Telemetry on Heterogeneous Networks, Collaborative Research: SaTC: CORE: Medium: ONSET: Optics-enabled Network Defenses for Extreme Terabit DDoS Attacks, Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management