Language Model Network Troubleshooting Agent Testing Framework
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Solution Overview
Problem
Evaluating the performance of language model-based computer network troubleshooting agents is challenging due to the complexities of deployed networks, requiring realistic representations of network deployments with multiple domains and controllers against diverse network states, impairments, and faults.
Innovation Solution
A testing framework is developed that configures impairment scenarios in computer networks, evaluates these scenarios using language model-based troubleshooting agents, and compares the root cause of the impairment with the agent's evaluation, providing an indication of the comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If language model-based troubleshooting agents are deployed in realistic network environments with multiple domains and controllers, then the evaluation becomes more accurate and reliable, but the complexity of the evaluation system increases significantly
Solution Approach 1:
The evaluation framework is divided into separate functional modules: network state generation module, impairment configuration module, agent execution module, and result analysis module. Each module handles a specific aspect of the evaluation process, making the complex system manageable and maintainable while ensuring reliable evaluation across multiple network domains and controllers
Solution Approach 2:
The patent introduces intermediary components including a standardized interface layer between the language model agent and network controllers, and a mediation layer that coordinates evaluations across multiple domains. These intermediaries simplify the overall system complexity by providing standardized communication protocols and coordination mechanisms
2Measurement precision
If the framework tests against a large and diverse set of network states, impairments, and faults, then the measurement precision improves, but the time and computational resources required increase
Solution Approach 1:
The framework pre-generates a comprehensive library of network states, impairments, and fault scenarios before actual evaluation. This preliminary preparation includes creating standardized impairment configurations and pre-configuring test scenarios, allowing the system to efficiently execute evaluations against diverse network conditions without requiring excessive time during actual testing
Solution Approach 2:
The system employs parameterized impairment scenarios that can be dynamically adjusted to test different network conditions. By changing parameters such as impairment type, severity, and location, the framework achieves high measurement precision across diverse network states while reusing the same base test infrastructure, thereby reducing overall evaluation time
Data Source
AI summary
In one implementation, a device configures an impairment scenario in a computer network. The device performs an evaluation of the impairment scenario by a language model-based troubleshooting agent. The device makes a comparison between a root cause associated with the impairment scenario and the evaluation of the impairment scenario by the language model-based troubleshooting agent. The device provides an indication of the comparison.