AWS DevOps

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

This post was co-written with Bharadwaj Tanikella (AI/ML Product Engineering Leader) and Mohammad Jama (Product Marketing Manager) from Datadog. In December 2025, we showed how AWS DevOps Agent and Datadog MCP Server could work together to autonomously correlate monitoring data with the infrastructure deployed and configured on AWS to resolve incidents in minutes instead of

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

Introduction When a deployment fails, finding the root cause often means piecing together information from multiple sources. You wait for the deployment to finish, request a log bundle, download it, and then search through files like eb-engine.log and cfn-init.log to find the error. If you’re not familiar with Elastic Beanstalk’s log file structure, you might

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

We are excited to announce CDK Mixins, a feature of the AWS Cloud Development Kit (CDK) that fundamentally changes how you compose and reuse infrastructure abstractions. In this post, you will learn how to use CDK Mixins to apply sophisticated features to any construct – whether L1, L2, or custom – without being locked into

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

Authors: Sourav Kundu and Joyson Neville Lewis. In today’s cloud-native landscape, ensuring the integrity and authenticity of your serverless functions is critical for maintaining security and compliance. Organizations face increasing challenges in preventing the execution of tampered or malicious code in their AWS Lambda functions. These challenges intensify as deployment pipelines become more complex and

AWS CloudFormation makes it easy to model and provision your cloud application infrastructure as code. CloudFormation templates can be written directly in JSON or YAML, or they can be generated by tools like the AWS Cloud Development Kit (CDK). Resources are created and managed by CloudFormation as units called Stacks. Managing infrastructure across multiple AWS

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

Introduction As modern applications evolve into complex ecosystems of serverless functions, microservices, and event-driven architectures, incident response becomes increasingly challenging. DevOps and SRE teams spend hours manually correlating data across observability tools and troubleshooting issues, racing against SLA deadlines. This reactive firefighting drains productivity, degrades reliability, and delays innovation. AWS DevOps Agent provides an opportunity

When we launched Amazon Q Developer, our goal was to bring AI assistance directly into the developer workflow. Customers adopted Q Developer across VS Code, JetBrains, Eclipse, and Visual Studio, using it for code generation, debugging, and chat-based guidance. Q Developer proved that AI belongs in the inner loop of software development. Over the past