At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), a joint center of Georgia State University, Georgia Institute of Technology, and Emory University, we developed an automated root-cause analysis system using Amazon Bedrock. Running our infrastructure on Amazon Web Services (AWS) since 2019, we built applications on Amazon Elastic Kubernetes Service (Amazon EKS) with logs shipped to Amazon CloudWatch using FluentBit. As our application grew, the volume of errors increased, and we sought to automate the most time-consuming part of incident response: the root-cause investigation. The architecture combines Amazon CloudWatch subscription filters, AWS Lambda, the Strands Agents SDK, and Amazon Bedrock to detect errors in real time, enrich them with log context and source code from GitHub, and deliver AI-powered root-cause analysis. Previously, engineers had to open Amazon CloudWatch Logs, read stack traces, find relevant source files, and trace execution paths, which took 15–30 minutes for straightforward errors and longer for complex issues. The system works by having applications on EKS send logs to CloudWatch using FluentBit. A CloudWatch subscription filter watches for error-level patterns (ERROR, Exception, FATAL, CRITICAL) and invokes a Lambda function when a match occurs. The Lambda runs a Strands Agent powered by Amazon Bedrock that investigates the error, then publishes the analysis to an Amazon Simple Notification Service (Amazon SNS) topic for delivery to our team