LLM-Orchestrated Network Diagnostics for Shorter Troubleshooting Intervals
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Solution Overview
Problem
Maintaining and diagnosing large networks of field-deployed access devices, such as cable modem termination systems and consumer premises equipment, requires significant time and effort from technicians, with existing AI methods not effectively reducing diagnostic intervals or improving network performance.
Innovation Solution
Implementing generative artificial intelligence techniques using large language models and vector databases to convert user queries into embedded versions, analyze them with orchestrators, retrieve contexts, and generate answers, thereby facilitating faster and more accurate network diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional diagnostic methods are used by technicians, then diagnostic accuracy can be maintained through human expertise, but diagnostic time and service downtime increase significantly
Solution Approach 1:
An AI intermediary system is introduced between the technician and the network devices. This AI system processes diagnostic queries, analyzes network data, and provides diagnostic recommendations, acting as a mediator that accelerates the diagnostic process while maintaining accuracy through its ability to rapidly process and analyze technical information.
Solution Approach 2:
The system enables self-service diagnostics where the AI automatically analyzes network device data, generates diagnostic reports, and provides troubleshooting recommendations without requiring extensive human intervention. This allows the diagnostic system to serve itself by autonomously processing information and generating insights.
2Productivity
If more field technicians are deployed to diagnose issues, then diagnostic coverage improves, but operational costs and complexity increase
Solution Approach 1:
The AI diagnostic system is designed as a universal platform that can handle multiple types of network devices, various diagnostic scenarios, and different troubleshooting tasks through a single unified interface. This multi-functional system replaces the need for multiple specialized technicians and tools, increasing productivity without proportionally increasing system complexity.
3Measurement precision
If expert engineers are consulted for complex issues, then diagnostic accuracy improves, but the time to obtain responses and implement fixes increases
Solution Approach 1:
The AI system is trained on extensive expert knowledge and diagnostic patterns, creating a digital copy of expert engineering capabilities. This allows the system to replicate expert-level diagnostic accuracy and provide recommendations instantly, eliminating the need to wait for actual expert engineers to review and respond to each issue.
4Reliability
If traditional network monitoring intervals are used, then system stability is maintained, but diagnostic responsiveness to new issues decreases
Solution Approach 1:
The AI diagnostic system dynamically adapts its analysis speed and depth based on the specific diagnostic situation. For routine issues, it provides rapid automated responses maintaining stability, while for complex emerging problems, it intensifies analysis resources to provide fast accurate diagnostics, thus achieving both reliability and speed through dynamic adjustment.
Data Source
AI summary
At a large language model, convert a user query to an embedded version of the user query. Analyze the embedded version of the query with an orchestrator to obtain a notion of the query. Query a vector database with the notion of the query. Responsive to the querying of the vector database, retrieve a context with the orchestrator. Provide the context from the orchestrator to the large language model. Generate an answer to the user query with the large language model based on the user query and the context. Return the generated answer to the user.