Multi-Agent Root Cause Analysis Across Distributed System Domains
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Solution Overview
Problem
Conventional troubleshooting methods for distributed systems are inefficient and resource-intensive, requiring multiple investigators with specialized knowledge to manually check each domain, leading to high maintenance costs and user dissatisfaction.
Innovation Solution
A multi-agent system utilizing domain AI agent modules and a system AI agent module to automatically troubleshoot system operations through machine learning, coordinating domain queries and responses to identify root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple investigators manually check each domain one at a time, then specialized knowledge and domain expertise can be applied, but troubleshooting time and resource consumption increase significantly
Solution Approach 1:
The patent creates virtual copies of domain experts in the form of AI agents. Each domain AI agent is trained on domain-specific data and can answer queries about that domain, replicating the expertise of human investigators without requiring their physical presence or manual intervention for each check.
Solution Approach 2:
The patent replaces the mechanical process of manual investigation with automated AI-based querying. Instead of human investigators physically checking domains sequentially, the system automatically sends domain queries to appropriate AI agents, processes their responses, and synthesizes findings to identify root causes.
2Reliability
If multiple investigators collaborate to analyze issues, then comprehensive domain coverage is achieved, but maintenance costs increase
Solution Approach 1:
The system enables self-service troubleshooting where the multi-agent system autonomously investigates issues without requiring human maintenance personnel. The domain AI agents independently query their respective domains, and the system AI agent coordinates the overall investigation, freeing human resources from routine maintenance tasks.
Solution Approach 2:
The patent transforms the operational parameters from human labor-based to AI-compute-based. Instead of measuring maintenance effort in terms of human hours and costs, the system operates using computational resources that can be scaled and managed differently, potentially reducing long-term maintenance expenses while maintaining comprehensive coverage.
3Ease of operation
If conventional troubleshooting checks each domain sequentially, then individual domain issues can be identified, but overall system performance analysis becomes inefficient
Solution Approach 1:
The patent segments the troubleshooting task into independent domain queries that can be executed in parallel. Each domain AI agent operates independently on its specific domain, and the system AI agent aggregates the results. This segmentation enables concurrent processing of multiple domains rather than sequential checking, significantly improving productivity.
Solution Approach 2:
The patent transitions from a single-dimensional sequential checking approach to a multi-dimensional parallel execution model. Instead of checking domains one after another in a linear fashion, the system simultaneously queries multiple domains through different AI agents, adding the dimension of parallelism and improving overall efficiency.
Data Source
AI summary
Multiple agent root cause analysis techniques are described. In an implementation, a processor performs operations to troubleshoot performance of a system operation by a plurality of domains (e.g., of a distributed system). The processor executes a system artificial intelligence (AI) agent to determine which domains of the plurality of domains include domain functions in support of the system operation, and to generate, using machine learning, queries to the determined domains based on the system operation. Domain responses are received from domain AI agents associated with the determined domains responsive to the queries and generated based on domain data associated with respective domains. A system response is generated by the system AI agent using machine learning based on the domain responses.