Multi-Agent Network Anomaly Troubleshooting With Language Models
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Solution Overview
Problem
Adapting large language models (LLMs) for network anomaly detection and troubleshooting in complex computer networks is challenging due to the high level of complexity, where a single device malfunction can have ripple effects, making it difficult to discern the underlying cause and initiate remediation.
Innovation Solution
A troubleshooting agent uses a language model to determine the root cause of network anomalies and, if allowed by policy, initiates automated remediation through a remediation agent, leveraging plugins to interact with network devices and systems for efficient troubleshooting and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multi-tiered support systems are used for network troubleshooting, then human expertise can identify root causes, but the process consumes excessive time and resources
Solution Approach 1:
The system enables automated self-service troubleshooting through AI agents that independently analyze network anomalies, identify root causes, and execute remediation actions without human intervention. The troubleshooting agent autonomously queries network devices, analyzes telemetry data, and implements fixes based on policy rules, eliminating the need for manual multi-tiered support escalation.
Solution Approach 2:
An AI-powered troubleshooting agent acts as an intermediary between network monitoring systems and remediation systems. This agent receives anomaly detections, performs autonomous analysis using language models, determines root causes, and coordinates with remediation agents to implement fixes, thereby bridging the gap between detection and resolution while reducing human involvement.
2Productivity
If automated remediation is implemented without policy constraints, then response speed increases, but system reliability decreases
Solution Approach 1:
The system applies preliminary anti-action by establishing policy rules and constraints before automated remediation executes. These policies pre-define acceptable remediation actions, approval thresholds, and safety boundaries, preventing potentially harmful automated actions while allowing safe remediations to proceed quickly without human approval.
3Extent of automation
If LLM-based agents are deployed for network troubleshooting, then automation capability improves, but handling network complexity becomes more difficult
Solution Approach 1:
The troubleshooting system is segmented into specialized AI agents with distinct responsibilities: anomaly detection agents monitor specific network parameters, troubleshooting agents analyze anomalies and identify root causes using language models, and remediation agents execute fixes. This segmentation allows each agent to specialize in specific tasks, making the overall complex network troubleshooting process manageable through divided responsibilities.
Solution Approach 2:
The troubleshooting agent serves as an intermediary that coordinates between anomaly detection and remediation processes. It uses language models to interpret complex network telemetry data, translate technical anomalies into understandable root cause analyses, and communicate remediation requirements to appropriate agents, thereby simplifying the handling of network complexity through intelligent mediation.
Data Source
AI summary
In one implementation, a troubleshooting agent executed by a device receives an indication of an anomaly detected in a network by an anomaly detection agent. The troubleshooting agent uses a language model to determine a root cause of the anomaly. The troubleshooting agent determines whether automated remediation for the root cause is allowed by a policy. The troubleshooting agent provides a remediation request to a remediation agent in the network to remediate the root cause of the anomaly, when the policy allows automated remediation for the root cause.