AWS DevOps

Introduction Running workloads on Amazon Elastic Kubernetes Service (Amazon EKS) can involve managing failures like OOMKilled or IP exhaustion. Engineers must repeatedly collect pod logs, trace events, and check node logs—a process that slows at night/weekends, with critical data lost when pods are deleted or nodes become unhealthy. This collection phase is pure overhead on

Introduction Development velocity has reached new heights with AI-driven development tools and practices. Organizations are generating code faster than ever before. But that speed carries risk. Researchers Anderson, Parker, and Tan warned in MIT Sloan Management Review, “Legacy systems tend to carry hidden debt; layering AI-generated code on top of them creates additional tangled dependencies.”

This post was co-written with Michael Stephan, Senior Principal Product Manager, and Christian Kreuzberger, Principal Software Engineer, at Dynatrace. AI-driven software delivery changes how code gets written, but not what production demands of it. A generated change still has to fit the traffic your service receives, the dependencies it calls, and the capacity limits it

AWS environments that have grown organically over time often share a common characteristic: infrastructure provisioned through the AWS Management Console, SDKs, or CLI without corresponding Infrastructure as Code (IaC) templates. This practice is commonly referred to as “ClickOps,” a term describing any infrastructure change made outside of a codified, version-controlled workflow. Whether changes happen through

Introduction When CI/CD deployment failures occur for GitHub hosted applications,  AWS DevOps Agent reduces the hours that Development and Site Reliability Engineering (SRE) teams typically spend manually investigating across multiple AWS services, logs, and pipeline stages. This process delays critical deployments and impacts software delivery velocity. This is especially true when teams need to correlate data between GitHub commit histories, AWS CodePipeline execution logs, and Amazon

Automating migration pipelines shifts valuable resources toward innovation. In this post, we will help you learn how to extend AWS Transform custom with Model Context Protocol (MCP) server integrations that connect project management, automated testing, and source control. You will discover how turning a code transformation tool into an automated migration pipeline can take you

Introduction Large-scale machine learning workloads: training, fine-tuning, and inference run on clusters of hundreds to thousands of GPU instances for days or weeks at a stretch. Keeping operational visibility across a fleet of this size is a constant challenge: hardware health events, node lifecycle transitions, capacity fluctuations, and workload-level issues appear in the event stream around

“A pull request comes back with a single comment: “This doesn’t follow our circuit breaker pattern. Check the Architectural Decision Record .”  You know the architecture decision record exists somewhere. You open your team’s wiki, search “circuit breaker,” scroll past six irrelevant results, find the document, read through it, switch back to your editor, and fix the

This post is co-written with Govind Menon, Head of MCP Product at ServiceNow. Introduction Enterprise teams managing applications on AWS often rely on ServiceNow as their IT service management (ITSM) system for incident tracking, change management, and configuration management. When incidents occur, engineers must context-switch between AWS, third party observability tools and ServiceNow, manually correlating

Walk through a complete CloudFormation development cycle – authoring, validation, deployment, and troubleshooting – without leaving your AI assistant, using the AWS IaC MCP Server.

Introduction In a recent post, my colleague Micah Walter introduced AWS Transform – continuous modernization in public preview. Today, this capability is generally available in regions supported for AWS Transform . Development velocity continues to increase. But velocity without maintenance accumulates technical debt at speed. The faster software scales, the faster technical debt compounds. At

Guest post in collaboration with Company 3 Director of New Technology, Phil Wortas, and Senior New Technology Engineer, Matthew Galloway Introduction Company 3 provides specialized services for the entertainment industry, including post-production services, visual effects, and color grading for feature films, commercials, and television content. Their teams collaborate globally to Increase workflow efficiency and expand

This post was co-authored by Ayelet Harcz (Product Manager), Hen Perez (CTO Architect), and Shani Gafni (Product Manager) at Wiz. When an on-call engineer receives an alert at 2 AM, a CPU spike, a latency anomaly, or an unexpected API error, the first question is whether this is an operational issue or a security incident.

Introduction Automated incident remediation – turning investigation findings into deployed fixes without manual toil – is the next frontier for operations teams running distributed workloads on AWS. Today, when an incident fires at 2 AM, the on-call engineer must correlate telemetry across Amazon CloudWatch, deployment pipelines, and application logs, then manually write and deploy a

AWS CloudFormation helps you model and provision cloud infrastructure as code using JSON or YAML templates, or through tools like the AWS Cloud Development Kit (CDK) and AWS Serverless Application Model (SAM). Whether you create stacks directly, use change sets for preview, or deploy through CI/CD pipelines and AI agents, the speed of your deployment

AWS CloudFormation helps you model and provision cloud infrastructure as code using JSON or YAML templates, or through tools like the AWS Cloud Development Kit (CDK). Whether you create stacks directly, use change sets for preview, or deploy through CI/CD pipelines and AI agents, fast feedback on template errors is critical to development velocity. Previously,

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

Introduction Organizations that use feature flags alongside incident response tooling often connect the two manually. When an outage occurs, engineers must identify which flags are relevant, decide whether to disable them, and coordinate the change across teams. This manual process adds latency at the moment it matters most. You can use AWS DevOps Agent and

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,

When something breaks in production, you find out fast. Understanding why it broke, before the damage spreads, is the hard part. That is where Site Reliability Engineering (SRE) teams lose the most time. Think about the last time you got paged at 2 a.m. The alert said something broke, not why. You open four or