How TReNDS automates root-cause analysis with Amazon Bedrock

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This is a guest post co-written with Vitaly Omelchenko from the TReNDS Center at Georgia State University. 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 develop and apply advanced analytical methods and neuroinformatics tools for brain health research. We’ve been running our infrastructure on Amazon Web Services (AWS) since 2019, and over the years we’ve built a diverse set of applications, including research tools and APIs, all running on Amazon Elastic Kubernetes Service (Amazon EKS) with logs shipped to Amazon CloudWatch using FluentBit. As our application grew, so did the volume of errors we needed to investigate. When we started exploring Amazon Bedrock, we saw an opportunity we had wanted for a long time. We could automate the most time-consuming part of incident response, the root-cause investigation itself. In this post, we share the architecture we built and use in production at TReNDS. It 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 to our team. The architecture and recommendations in this post reflect our team’s experience at the TReNDS Center and do not represent official guidance from Georgia State University, Georgia Institute of Technology, or Emory University. The problem we wanted to solve Like many teams, we had alerting and …

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