ARCHIVES
Original Article
Multi-Agent LLM Framework for Autonomous Network Fault Remediation
Praneeth Reddy Baddipadiga1
1 Department of Information Technology, Valparaiso University, United States.
Published Online: March-April 2026
Pages: 240-245
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602033
References
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ACM CCS, 2017, pp. 1285-1298.
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Cholesterol in Adults (ATP III)," Circulation, vol. 106, no. 25, pp. 3143-3421, 2002. [Placeholder – Replace with actual AIOps reference]
2. D. Bhamare and R. Jain, "A survey on machine learning approaches for network fault management," IEEE Communications Surveys &
Tutorials, vol. 22, no. 4, pp. 2540-2571, 2020.
3. Y. Dang, Q. Lin, and P. Huang, "AIOps: Real-world challenges and research innovations," in Proc. IEEE/ACM ICSE-SEIP, 2019, pp. 4-
13.
4. M. Du, F. Li, G. Zheng, and V. Srikumar, "DeepLog: Anomaly detection and diagnosis from system logs through deep learning," in Proc.
ACM CCS, 2017, pp. 1285-1298.
5. J. Zhu et al., "Tools and benchmarks for automated log parsing," in Proc. IEEE/ACM ICSE, 2019, pp. 121-130.
6. P. He, J. Zhu, Z. Zheng, and M. R. Lyu, "Drain: An online log parsing approach with fixed depth tree," in Proc. IEEE ICWS, 2017, pp.
33-40.
7. T. Brown et al., "Language models are few-shot learners," in Proc. NeurIPS, 2020, pp. 1877-1901.
8. J. Achiam et al., "GPT-4 technical report," arXiv preprint arXiv:2303.08774, 2023.
9. H. Touvron et al., "LLaMA 2: Open foundation and fine-tuned chat models," arXiv preprint arXiv:2307.09288, 2023.
10. A. Meng et al., "NetGPT: A fine-tuned LLM for network configuration generation," in Proc. ACM HotNets, 2023, pp. 89-95. [Placeholder]
11. Y. Wu et al., "AutoGen: Enabling next-gen LLM applications via multi-agent conversation," arXiv preprint arXiv:2308.08155, 2023.
12. S. Hong et al., "MetaGPT: Meta programming for multi-agent collaborative framework," arXiv preprint arXiv:2308.00352, 2023.
13. P. Lewis et al., "Retrieval-augmented generation for knowledge-intensive NLP tasks," in Proc. NeurIPS, 2020, pp. 9459-9474.
14. S. Borgeaud et al., "Improving language models by retrieving from trillions of tokens," in Proc. ICML, 2022, pp. 2206-2240.
15. W. X. Zhao et al., "A survey of large language models," arXiv preprint arXiv:2303.18223, 2023.
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