Smarter Network Troubleshooting with Graph Technology and AI Agents

Introduction

With all we have tied together in the intensely connected world we inhabit today, the requirement for robust, always-on communications networks has never been more acute. Network operators and enterprises have their work cut out: networks are growing more complex, traffic volumes are rising, and services are becoming more dependent. When something goes down or degrades, the first hurdle is to catch it, naturally, but to diagnose it under stress.

Legacy root cause analysis (RCA) is a correlation engine and static rule-based, giving you the region where the problem is, but not necessarily what the problem is and why it occurred in the first place. These systems correlate alarms from diverse layers and attempt to infer the root cause. However, these techniques fail in real-world cascading failure and nonlinear interdependency scenarios. They tend to be proficient at reporting what’s happening but not at reporting why.

It is here that bringing network digital twins, graph technology, and Agentic AI together serves to break through an important step forward. By constructing a living, graph representation of a network and providing it with reasoning-capable intelligent agents, organizations can break out of barrelling down similar alerts and instead work systematically towards identifying actual root causes.

Overview

Root cause analysis in practice is a coded procedure. The theory is simple: peel the layers off an issue back to the root cause that triggered the chain of events. Telecommunications networks generate a torrent of alarms, warnings, and performance data, many of which are only symptoms of a root problem.

For example, one cut fibre will alert dozens of downstream nodes, leading engineers astray in chasing false positives. Correlation products will correlate those alarms into one, but do not usually possess contextual intelligence to carry to the broken link.

What Is a Network Digital Twin?

A digital twin of a network is a virtual replica of a real network, but while a static topology drawing will be unchanging and static, the digital twin will be live and data-bound. It merges live telemetry, alarm data, and dependency data with a constantly updated model. When the digital twin is built as a graph, its true power emerges.

Graphs fit naturally into modelling interdependencies: one network component dependent upon another, services traversing some of them, and a breakdown in one level cascading to another. The analytical knowledge base is the topology-sensitive graph. Instead of stand-alone alarm treatment, it interprets them as an interdependencies’ part.

The three steps for building the digital twin are:

1. Ingestion – Extracting KPIs, alarms, and configuration data from different network layers, which is used to detect the fault in the system.
2. Graph transformation – Mapping such data into nodes, edges, and attributes that truly depict real-world relationships between things.
3. Analysis – Applying reasoning, graph analytics, and machine learning algorithms to detect anomalies, reason about root causes, and trigger a fix flow.

Agentic AI

As opposed to traditional machine learning models that residually predict, Agentic AI is a collection of intelligent agents. Agents can traverse the graph, run playbooks, and dynamically adapt their strategies based on observation.

At the center of RCA is Agentic AI. Upon the occurrence of a fault, agents traverse the network graph, reason about dependencies, and implement runbook design patterns and fault isolation template workflows. Instead of depending on formal if-then rules, they reason their way to likely causes adaptively.

AWS Solution Architecture

AWS built a reference architecture that integrates digital twin graphs with Agentic AI. At its core are scalability and intelligence services:

• Amazon Neptune and Neptune Analytics are used to store the graphs and conduct graph analysis of the network.
• Amazon SageMaker and Amazon Bedrock, to support generative AI models and intelligent agents.
• Orchestration services are used to initiate and execute runbooks upon identifying anomalies detected.

In this architecture, the system observes, perceives, comprehends, and responds. Graph analytics and reasoning aided by AI offer an adaptive and automated RCA process.

Runbook Design Patterns

AWS identifies four runbook design patterns determining Agentic AI’s interaction with the digital twin. They represent best practice for distinguishing between different classes of faults, e.g., transport layer faults or radio access idiosyncrasies. Each runbook is a playbook, but with intelligence, IP AI agents may opt to run, in order, and with reasoning.

This modular framework allows operators to extend or alter runbooks for their ecosystems. Runbooks may also be extended as the networks grow, enabling continuous tuning without the rigidity of hard-wired rules.

Real-World Deployment: NTT DOCOMO

The theory becomes practicable when it is proven in practice. NTT DOCOMO, a global top-tier telecom operator, applied this approach to its operational business network. DOCOMO attained exceptional results by implementing the first runbook pattern to RAN and transport layers.

The biggest highlight achievement was reducing the mean time to detection (MTTD) for faults to 15 seconds. This means that faults can be detected and isolated in real time, reducing downtime and service interruption.

This rollout demonstrates that digital twin, graph, and Agentic AI as a package are not an intellectual exercise in some ivory tower but an empirically validated method of real-world benefit at scale.

Conclusion

Networks are the foundation of our online being, bringing them alive with trustworthy calls to observe over shallow perusal. Legacy RCA methods, rooted in static rules and correlation, fall behind the dynamics of modern telecommunication networks. Organizations require the ability to move from correlation to causation, not only to detail what has happened, but also why it happened.

AWS enables a new RCA paradigm by creating network digital twins as graph-based knowledge graphs and filling them with Agentic AI. This approach enables adaptive reasoning, fast detection, and automated fault isolation. The practicability is validated with the success at NTT DOCOMO with the 15-second detection.

To telecommunications operators and enterprises, this solution is a template for the future: intelligent, actively managed, and resilient network management. It’s a shift from reactive firefighting to active assurance, and it gives assurance that networks will be able to cope with the rising demands of our networked world.

Drop a query if you have any questions regarding RCA and we will get back to you quickly.

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