LLM Network Troubleshooting Transparency With Output Validation
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Solution Overview
Problem
The lack of transparency in the operations of large language model (LLM)-based agents in network troubleshooting systems makes it difficult for users to understand how tasks are completed and whether they are done correctly, leading to potential inefficiencies and mistrust.
Innovation Solution
A system that includes a troubleshooting agent leveraging LLMs to identify root causes of network issues, generate troubleshooting plans, and execute tasks with transparency. The system provides step-by-step instructions, allows for user interaction, and includes a critic engine to validate outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM-based agents are used to perform network troubleshooting tasks, then task completion capability is improved, but transparency of operations deteriorates
Solution Approach 1:
A transparency layer is introduced as an intermediary between the LLM-based troubleshooting agent and the user. This layer includes components such as a step-by-step explanation generator that translates the agent's internal reasoning and actions into human-understandable descriptions, and a validation mechanism that verifies each step before execution. This mediator maintains the high task completion capability of the LLM while providing the needed transparency about what the agent is doing and why.
2Extent of automation
If LLM-based agents operate autonomously without transparency, then automation extent is improved, but user trust deteriorates
Solution Approach 1:
A feedback mechanism is implemented where the system continuously provides information back to the user about the troubleshooting agent's actions, reasoning, and confidence levels. The transparency layer generates explanations for each step the agent takes, allows users to query about specific decisions, and provides validation results. This feedback loop maintains autonomous operation while building user trust through visibility and accountability.
3Ease of operation
If detailed transparency mechanisms are added to LLM-based agents, then user understanding is improved, but system complexity increases
Solution Approach 1:
The transparency system is segmented into distinct modular components: a step-by-step explanation generator that creates human-readable descriptions of agent reasoning, a validation module that checks each step's correctness, and an interface layer that presents information to users in appropriate detail levels. This segmentation allows each component to be developed and optimized independently, managing overall system complexity while providing comprehensive transparency.
Data Source
AI summary
In one implementation, a device receives an output of a language model-based troubleshooting agent to perform a task with respect to a computer network. The device determines a level of quality of the output. The device generates an instruction for the language model-based troubleshooting agent, when the level of quality of the output is below a threshold. The device requests that the language model-based troubleshooting agent perform the task using the instruction.