LLM Orchestration for Network Troubleshooting Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Selecting the optimal set of objectives for large language model (LLM)-based network troubleshooting and monitoring agents is challenging due to varying use cases and scenarios, and existing techniques have limited impact on optimization criteria such as speed, accuracy, and cost.
Innovation Solution
A system that receives input requests for LLM-based troubleshooting agents, selects optimization criteria based on the request, and provides these criteria to the agent to choose the appropriate LLM for processing, thereby optimizing for specific objectives such as speed, accuracy, or cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single LLM is used for all troubleshooting tasks, then the system is simple to manage, but it cannot optimize for different objectives (speed, accuracy, cost) across different use cases
Solution Approach 1:
The patent implements a multi-LLM architecture where each LLM is specialized for specific optimization objectives (speed, accuracy, cost). An orchestration layer provides a universal interface that routes different troubleshooting requests to appropriate LLMs based on the desired optimization criterion, enabling the system to handle multiple objectives without requiring separate dedicated systems for each.
Solution Approach 2:
The patent introduces an orchestration layer as an intermediary between the user and multiple LLMs. This mediator receives troubleshooting requests, determines the appropriate optimization objective, selects the suitable LLM, and manages the interaction between the LLM and external tools. This intermediary simplifies the complexity by providing a unified interface while enabling access to multiple specialized models.
2Productivity
If multiple LLMs are used to handle different optimization criteria, then speed, accuracy, and cost can be optimized for specific use cases, but the system complexity increases
Solution Approach 1:
The patent segments the troubleshooting system into multiple specialized LLMs, each optimized for specific objectives (e.g., one LLM for speed optimization, another for accuracy, another for cost efficiency). This segmentation allows each component to be highly optimized for its specific function while the orchestration layer coordinates them to provide comprehensive functionality.
3Ease of operation
If LLM parameters such as temperature are adjusted, then some control over output randomness is achieved, but the impact on optimization objectives is moderate and limited
Solution Approach 1:
The patent goes beyond adjusting single parameters like temperature by selecting different LLMs that are fundamentally optimized for different objectives. Each LLM may have different architectural parameters, training objectives, and optimization criteria built-in, providing a much broader range of control over the optimization outcomes compared to merely tuning hyperparameters of a single model.
Data Source
AI summary
In one implementation, a device receives an input request for a large language model-based troubleshooting agent for a network. The device selects an optimization criterion for the large language model-based troubleshooting agent based on the input request. The device provides the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion. The device sends, to a user interface, an indication of a result of the particular large language model processing the input request.