Category: Artificial Intelligence

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 […]

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 […]

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). 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 […]

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 […]