The agentic structured graph traversal approach can be adapted for real-time automated incident prevention in cloud applications by predicting potential code-related issues before they occur.

Motivation

While the current approach focuses on root cause analysis after incidents occur, preventing incidents could significantly improve the operational efficiency of cloud applications. By predicting potential issues, organizations can preemptively address them, reducing downtime and associated costs.

Proposed Method

Develop a predictive model that uses historical incident data and the structured graph traversal technique to identify patterns that precede incidents. Implement a real-time monitoring system that continuously evaluates these patterns and flags high-risk areas for proactive investigation. Validate the system by comparing incident rates in test environments with and without the predictive model over several months.

Expected Contribution

This research could lead to a significant reduction in the frequency and impact of code-related incidents in cloud environments, enhancing operational reliability and cost efficiency.

Required Resources

Access to large datasets of historical cloud application incidents, cloud infrastructure for real-time testing, expertise in predictive modeling and real-time data processing.

Source Paper

Agentic Structured Graph Traversal for Root Cause Analysis of Code-related Incidents in Cloud Applications

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