Category: Artificial Intelligence

Custom code transformations are the work that no off-the-shelf migrator covers for you. Moving your services off an internal library, enforcing your team’s error-handling conventions, standardizing logging across your fleet of services: that work piles up on your backlog because general-purpose tools weren’t written with your codebase in mind. AWS Transform custom tackles that kind […]

When an alarm fires at 2 AM, the first thing most engineers do is grep logs, check recent deployments, and trace code paths. However, the context they need — metrics, traces, topology, configurations — lives in a separate browser tabs and applications. What if your IDE could bring that cloud intelligence directly to your code, […]

AWS DevOps Agent can investigate a growing range of production incidents autonomously. It diagnoses CrashLoopBackOff failures, traces ConfigMap deletions through audit logs, and correlates Amazon CloudWatch metrics with cluster events — all without human intervention. But AWS DevOps Agent has a visibility boundary. When the data it needs lives outside its native integrations — on […]

Confirmation bias is one of the most common reasons incident investigations take longer than they should. An on-call engineer gets alerted, forms a theory based on initial triage and experience, finds one piece of supporting evidence, and stops looking. The actual root cause — buried in a different service, a different signal, a different time […]

Modern distributed systems route business transactions through dozens of microservices, message queues, and event streams. When a message fails to process or processing exceeds SLA thresholds, troubleshooting requires correlating logs from tools like Elasticsearch, metrics from Datadog, and infrastructure change events in AWS CloudTrail. Correlating these signals manually across heterogeneous backends, each with different query […]

Learn how to build CLI tools that extend themselves through natural language using Amazon Bedrock, the Strands Agents SDK, and Model Context Protocol. This post walks through a meta-tooling pattern where generated CLIs can create, refine, and version new commands at runtime—turning days of manual development into minutes of conversational iteration, all without touching source code.

Learn how AWS Transform custom can help migrate Excel VBA applications to modern Python code while overcoming context window limitations, preserving functional equivalence, and enabling cloud-native deployment—turning weeks of manual rewriting into hours of AI-guided transformation. Introduction Many organizations maintain dozens of Excel VBA applications built over decades, containing business-critical logic trapped in workbooks—budget planning […]

Introduction Modernizing applications by upgrading language runtimes, migrating SDKs, and refactoring frameworks is important for cloud adoption but can be labor-intensive at scale. Each repository requires analysis of dependencies and transformation needs; custom transformation logic must be built and validated, and changes are often executed sequentially across codebases. If you have hundreds of applications, this […]

Enterprise modernization has reached an inflection point. You can transform one repository easily. Existing tools, including AWS Transform custom, work well for individual repositories, and the process is understood. But what about 50 repositories? 100? 200? When you need to modernize at enterprise scale, transforming code is only part of the challenge. Coordinating people, capturing […]

Introduction AWS Elastic Beanstalk simplifies the process of deploying and scaling web applications. You upload your code, and Elastic Beanstalk handles capacity provisioning, load balancing, auto scaling, and application health monitoring. Elastic Beanstalk now offers AI Analysis to help troubleshoot environment health issues. When you request an analysis, Elastic Beanstalk triggers a script on the […]