AWS Database Blog
Author: Imen Grida Ben Yahya
Imen is a Principal AI/ML Specialist Architect for Telco in Amazon Web Services. She is tech. Leading and building cloud native AI and ML including Generative AI on AWS services with key telco players to accelerate the move towards Smarter, Greener and Cloudified Networks. She holds a PhD on Cognitive Networks from Telecom SudParis, Institut Polytechnique de Paris, in 2008. She co-authored several research papers on AI for Networks (6G, 5G, Software Networks, Cloud Native Net). Imen participated in several conferences as organizer, member of several Technical Program Committee and is a public speaker.
Beyond Correlation: Finding Root-Causes using a network digital twin graph and agentic AI
When your network fails, finding the root cause usually takes hours of investigations, going through correlated alarms that often lead to symptoms rather than the actual problem. Root-cause analysis (RCA) systems are often built on hardcoded rules, static thresholds, and pre-defined patterns that work great until they don’t. Whether you’re troubleshooting network-level outages or service-level degradations, those rigid rule sets can’t adapt to cascading failures and complex interdependencies. In this post, we show you our AWS solution architecture that features a network digital twin using graphs and Agentic AI. We also share four runbook design patterns for Agentic AI-powered graph-based RCA on AWS. Finally, we show how DOCOMO provides real-world validation from their commercial networks of our first runbook design pattern, showing drastic MTTD improvement with 15s for failure isolation in transport and Radio Access Networks.