Autonomous Runbook System for Incident Resolution
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Solution Overview
Problem
Manual creation of runbooks in IT environments is time-consuming, costly, and prone to inaccuracies, leading to incomplete identification of incident-addressing steps, and runbooks become outdated as new incidents and applications are introduced.
Innovation Solution
An autonomous runbook system that autonomously updates and identifies incident-addressing steps through adaptive learning based on prior performance, using seed data for clustering applications and historical incident data to create an incident model, enabling automated identification of effective steps for addressing incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of runbooks is performed by designated personnel, then runbooks can be created with in-depth system knowledge, but the process is time-consuming and costly
Solution Approach 1:
The system performs self-service by automatically generating runbooks through adaptive learning from historical incident data and performance feedback, eliminating the need for manual creation by designated personnel while maintaining accuracy through continuous learning and validation against actual incident outcomes
Solution Approach 2:
The system performs preliminary action by pre-processing historical incident data and incident-addressing step performance information to build knowledge bases and incident models in advance, enabling rapid automatic generation of accurate runbooks when incidents occur without time-consuming manual analysis
2Reliability
If manual runbooks are created, then initial incident-addressing steps can be identified, but runbooks become outdated as new incidents and applications are introduced
Solution Approach 1:
The system applies dynamics by implementing continuous adaptive learning that automatically updates the incident model and runbook content based on new incident data and performance feedback, transforming the static manual runbook into a dynamic system that evolves with changing incidents and applications while maintaining completeness and accuracy
Solution Approach 2:
The system implements feedback mechanisms by collecting performance information from executed incident-addressing steps and using this feedback to continuously refine the incident model and update runbook content, ensuring the system adapts to new incidents and maintains reliability through iterative improvement based on actual outcomes
3Productivity
If automated systems are implemented, then productivity increases, but the system complexity increases
Solution Approach 1:
The system applies universality by designing a multi-functional automated runbook system that performs multiple functions including incident detection, automatic runbook generation, step execution, performance monitoring, and adaptive learning within a single integrated platform, increasing productivity while managing complexity through functional consolidation rather than separate specialized systems
Data Source
AI summary
Relationships among incident-addressing steps, applications, and incidents are determined, based on information relating to the applications and the incidents. For addressing a given incident that occurred with respect to a particular application, at least one incident-addressing step is identified using the determined relationships.