Conversational Incident Remediation Using Error-to-Code Matching
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Solution Overview
Problem
Incident response systems lack visibility into key information about incidents, leading to a lack of context and understanding of system impacts, and responders spend significant time piecing together data points to resolve issues, with no guidance on next steps, resulting in prolonged mean time to resolution (MTTR).
Innovation Solution
An AI-based assistant integrated with a central knowledge base provides interactive incident management, automatically evaluating prompts, generating remediation scripts, and deploying them seamlessly within existing processes, using a conversational interface and machine-learning models to match error messages with code changes and suggest remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If incident response systems use traditional manual analysis methods, then responders can understand incident context through human judgment, but responders spend significant time piecing together data points resulting in prolonged mean time to resolution
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between incident data and human responders. The AI assistant automatically aggregates incident context, analyzes log files, reviews code changes, and synthesizes findings into actionable insights, eliminating the manual time consumers while preserving comprehensive context analysis
Solution Approach 2:
The system enables self-service incident analysis through automated AI-driven investigations. The AI assistant independently performs data collection, analysis, and synthesis without requiring human intervention for each step, allowing responders to receive pre-analyzed incident context ready for immediate action
2Loss of information
If incident response systems provide comprehensive data analysis, then responders gain complete understanding of incident context, but the system complexity increases requiring multiple data sources and processing layers
Solution Approach 1:
The AI assistant serves multiple functions within a single system component: it aggregates data from multiple sources, analyzes log files, reviews code changes, synthesizes findings, and provides actionable recommendations. This multi-functionality consolidates what would otherwise require separate complex systems into one unified assistant
Solution Approach 2:
The patent merges multiple incident analysis functions (data aggregation, log analysis, code review, context synthesis) into a single AI assistant workflow. This consolidation simplifies the system architecture by integrating previously separate processing layers into one cohesive intelligent agent
3Loss of time
If incident response systems automate remediation processes, then mean time to resolution decreases through faster response, but the risk of incorrect automated actions increases requiring precise accuracy
Solution Approach 1:
The AI assistant implements feedback loops to verify remediation actions before execution and to learn from outcomes. The system monitors the effectiveness of automated actions and uses this feedback to improve future decisions, ensuring accuracy while maintaining speed
Solution Approach 2:
The system performs preliminary analysis and validation of remediation actions before automated execution. The AI assistant prepares and validates remediation strategies in advance, ensuring accuracy is established before rapid automated deployment occurs
4Ease of operation
If incident response systems integrate AI assistants with conversational interfaces, then ease of operation improves through natural language interaction, but the extent of automation increases requiring sophisticated machine learning models
Solution Approach 1:
The conversational interface acts as an intermediary that translates simple user commands into complex AI operations. Users interact through natural language while the AI intermediary handles the sophisticated machine learning model interactions, maintaining ease of operation despite high automation levels
Data Source
AI summary
In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.