incident_management · manufacturing · workflow
BMW's generative AI solution for cloud incident root cause analysis using Amazon Bedrock Agents
BMW's root cause analysis for cloud incidents was cumbersome and time-consuming, requiring engineers to manually check many interdependent systems, iteratively form and reassess hypotheses, and track down culprits across geographically dispersed teams and components.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Incident described to agent
An on-call engineer gives a description of the issue at hand to the Amazon Bedrock agent.
Tools used
Amazon Bedrock AgentsAWS LambdaAmazon CloudWatchAWS CloudTrailCloudWatch Logs InsightsC4 diagramsStructurizr
Outcome
The Amazon Bedrock ReAct agent correctly identifies root causes in 85% of cases, reduced diagnosis time from hours to minutes, and lowered the barrier for junior engineers to diagnose issues effectively.
Results
Time savedsignificantly lower diagnosis times
Volume85%
Grounding & classification
Source type: vendor customer story
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agentic workflowai agentanomaly detectionknowledge basemetric backednamed customerproduction runtime claimedsource backedtools describedvendor confirmedworkflow describedautomotiveaccuracy improvementcycle time reductionemployee productivitytime savedvendor customer storyincident managementagentic task executionmonitor detect alert