When an incident hits at 2 AM, the on-call engineer’s effectiveness depends on what they know about the system, which metrics to check first, what “normal” looks like for this service, and where to find the deployment history. That knowledge often lives in runbooks, internal wikis, and the heads of senior engineers who built the [...]

Amazon S3 Tables now offer complete support for the Apache Iceberg V3 spec. Upgrade existing V2 tables or create new V3 tables with built-in compaction, maintenance, and engine compatibility.

AWS today announced the general availability of AMD-based R8a and M8a database instances for Amazon Aurora and Amazon RDS, powered by 5th generation AMD EPYC processors. R8a is a memory-optimized instance family available on both Aurora and RDS. M8a is a general-purpose instance family available on RDS.

In the post Automate AWS GovCloud (US) account creation using AWS Organizations APIs, we showed how to programmatically create GovCloud (US) accounts using AWS Organizations APIs. That post ends at account creation—the new accounts exist but the standard account remains in the organization root while the GovCloud (US) account remains a standalone account. You must manually complete the steps to join a GovCloud (US) organization and move accounts to the correct organizational units (OUs).

Thousands of biomedical research papers are published every day, and no single researcher can keep pace without automated analysis. For researchers in genomics and biotechnology, staying current means manually reading through dozens of dense PDFs, extracting relevant findings about genetic variations, biomarkers, and disease associations one paper at a time. It’s slow, error-prone, and ultimately limits how much literature a researcher can realistically incorporate into their work. When the goal is discovering life-saving treatments, that bottleneck matters.

In August, we released Dogwood, an open source governance language for agents and their actions. Dogwood policies specify which actions an agent may take and under what conditions. A key feature of the language is support for temporal conditions, which let a policy refer to an agent’s past actions and their outcomes. A Dogwood policy [...]

Amazon S3 Vectors now supports metadata pre filtering, delivering up to 5x higher recall on filtered searches. Filters evaluate before similarity search so scoped queries return more relevant results. Ideal for RAG, agentic apps, and document search.

A redesigned checkout button is meant to lift sales. A longer cache time to live (TTL) is meant to cut backend load. But until you test a change against real production traffic, decisions come down to intuition and whoever argues hardest, not evidence. With AWS AppConfig experimentation, you can make data-driven calls instead: using A/B [...]

Prove your multi-AZ architecture can sustain a real impairment. This post shows how to run a multi-day (48-72 hour) Availability Zone evacuation drill with ARC Zonal Shift across Amazon ECS, Amazon EKS, Amazon RDS for PostgreSQL, and Amazon Aurora PostgreSQL, with step-by-step CLI commands, prerequisites, observability metrics, and restore procedures.

MHK built the SmartProminence AI Orchestrator, a HIPAA-eligible agentic workflow framework on AWS using Amazon Bedrock, Amazon ECS, and event-driven patterns. It reduced manual medical review effort by 90% and cut new AI feature deployment from months to weeks. Learn how MHK architected this reusable, multi-tenant orchestrator for healthcare AI.

To build such a self-evolving loop for Autonomous Network Operations, agentic systems must react to network events, drive an investigation loop, remember context, learn how its operator works, and coordinate across unique tools and workflows, so the system comes back with progress instead of another workflow to restart. This blog will focus on the persistent and self-learning aspects of the Agent system.

In this post, we show you how to use Amazon Elastic Compute Cloud (Amazon EC2) resource tags and next-hop metadata, two new capabilities in Version 11 of Amazon Virtual Private Cloud (Amazon VPC) Flow Logs. These fields let you simplify network traffic analysis without building custom enrichment pipelines. Since the introduction of VPC Flow Logs [...]

Amazon Aurora PostgreSQL now lets you directly query Apache Iceberg and Parquet data stored in your data lake alongside live operational data—no ETL pipelines required. Powered by DuckDB embedded within Aurora, this capability enables single queries that join transactional and historical data using familiar PostgreSQL syntax. It supports AWS Glue Data Catalog, S3, and S3 Tables, with optimizations like predicate pushdown and caching for efficient performance.

You write specs that guide AI coding agents, deploy agents on managed runtimes, and orchestrate multi-agent systems across production workloads. Now you can build those skills in a live AWS environment. Today, we’re launching three new AWS Jam experiences focused on agentic AI development: AWS Jam – Spec Driven Development with Kiro, AWS Jam – [...]

Today, we’re excited to introduce the newest members of the AWS Heroes program. AWS Heroes are a vibrant, worldwide group of AWS experts who go above and beyond to share knowledge, mentor others, and build thriving communities. These individuals make a real difference in helping developers and organizations succeed with AWS. This month, we welcome [...]

Replication lag during a change data capture (CDC) migration can be counterintuitive: the source is busy, yet replication falls behind and WAL piles up. This post shows how to resolve PostgreSQL replication lag with heartbeat tables using AWS DMS or Debezium, and how to monitor replication slot health with Amazon CloudWatch and SQL diagnostics.

Learn how to use AWS DMS and a Db2 gateway server on Amazon EC2 to migrate and replicate data from an on-premises IBM Db2 database on z/OS to Amazon Aurora PostgreSQL. This post covers configuring the gateway, creating DMS resources, running a full load with periodic full-load refresh, and validating the migration.

AS/400 business rule extraction no longer requires months of manual effort. Using Kiro, an agentic AI-powered development environment, this guide shows how to extract business rules from AS/400 RPG and COBOL programs, generate technical specifications, and produce modernization-ready documentation in days instead of months.

This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations. It covers ingesting claim documents from Amazon S3, querying with the AgenticRetrieveStream API, multi-turn follow-ups, metadata filters, and contextual grounding guardrails.

Learn how to build an AI-powered migration assessment agent using Amazon Bedrock AgentCore and the Strands Agents SDK. The agent reads application source code and container artifacts, scores Amazon EKS migration readiness, identifies blockers by severity, and generates an actionable migration plan with target architecture recommendations.

Amazon Bedrock AgentCore Runtime Instances gives multi-agent workflows AWS managed EC2 infrastructure with GPUs, persistent volumes, and multi-day sessions. In this post, we deploy a three-agent music production pipeline where the agents colocate on one GPU instance, share a filesystem, and hand work to each other to produce a finished track.

Learn how athenahealth used Amazon EKS Hybrid Nodes to modernize latency-sensitive healthcare workloads on premises, cutting response times in half, reducing hardware and operational costs by 50%, and maintaining a single Kubernetes operating model across its data center and the cloud while meeting HITRUST and data residency requirements.

Introduction Contact center teams running Amazon Connect Customer consistently face a core data engineering challenge: reducing the end-to-end latency from call completion to transcript availability in the data warehouse. The answer is not a single number. It depends on understanding the interplay of 14 distinct latency factors across the pipeline. The factors include the irreducible [...]

Introduction Contact center teams running Amazon Connect Customer consistently face a core data engineering challenge: reducing the end-to-end latency from call completion to transcript availability in the data warehouse. The answer is not a single number. It depends on understanding the interplay of 14 distinct latency factors across the pipeline. The factors include the irreducible [...]

Email is one of the highest-volume support channels in most contact centers, and one of the most manual. Agents read long threads, search internal knowledge, and draft replies from scratch, which makes for slow first responses and inconsistent messaging. Amazon Connect Customer now provides native AI capabilities for the email channel. With email AI agents, [...]

A voice-activated AI agent can make contact center management significantly faster when the AI agents handling your calls need urgent changes. Supervisors running these AI agents on Amazon Connect Customer face a persistent operational challenge. When critical systems go down or AI agent behavior needs immediate adjustment, the path from decision to action involves multiple [...]

In this blog, we will dive-deeper into each use-case mentioned in our launch blog. We will share some of the results partners and customers have achieved using the Kiro based sample agents during their SAP modernization projects. You can download these sample agents here. Here is a recap of the primary use cases covered in [...]

Kiro is AWS’s AI-powered, engineering agent, designed for enterprises to build production-ready software. With Kiro, customers can build across large codebases with parallel agents, which is ideal for complex, resource intensive projects, like SAP modernizations. Today, we are publishing Kiro based sample agents that customers and partners can leverage to accelerate the modernization and deployment [...]

Anthropic’s Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 are now available in India through Amazon Bedrock geographic cross-Region inference. You can access these models while processing data within the India Regions, and get started from the Amazon Bedrock console or with the Messages, InvokeModel, and Converse APIs.

Amazon Bedrock now supports Anthropic’s Claude Opus 5 and Claude Sonnet 5 with in-region inference in Seoul, and Claude Sonnet 5 in Singapore. If you have local data processing requirements in South Korea or Singapore, you can now use these Anthropic models at scale, with inference processed entirely within the Region you call.

This post was contributed by Charlie Chuang, Madhu Vellakal, and Charunethran Panchalam Govindarajan. Since launching Research and Engineering Studio on AWS (RES), we have helped customers stand up self-service portals for research, engineering, and technical computing on AWS. As this space has matured, with a strong ecosystem of AWS Partners and complementary AWS services, we [...]

In this post, we share what we’ve learned at Amazon Web Services (AWS) working with public sector and higher-education organizations through cloud migrations, contact center modernizations, and AI adoption initiatives, and why we think getting executive sponsorship right is your first priority, not your last.

This post walks through the architecture and implementation of an event-driven content refresh pipeline for media workflows.

TESSERA, a global geospatial AI foundation model, is openly available through the Amazon Sustainability Data Initiative (ASDI) and the Amazon Web Services (AWS) Open Data Sponsorship Program. With compute and hosting support from AWS, dClimate is completing TESSERA’s global historical coverage and publishing the resulting analysis-ready data on Amazon Simple Storage Service (Amazon S3) for anyone to use at no additional cost. dClimate delivers climate data solutions that help businesses and governments assess risk, monitor natural capital, and make confident decisions. In this post, we share what TESSERA is, how it works, and how researchers, builders, and institutions can start using it.

The Seattle Public Library and AWS are bringing free AI and cloud skills training directly into neighborhood branches to meet learners where they are. Since 2025, the collaboration has delivered over 50 classes to more than 550 community members, removing barriers like cost, transportation, and access that often keep people from pursuing tech education. Classes are led by AWS technical trainers who adapt to each audience, and the Library offers free certification exam vouchers to qualifying participants.

If you manage production incidents, you know the drill. A ServiceNow ticket fires at 2 AM. The on-call engineer wakes up, logs in to Dynatrace, pulls traces and metrics across multiple dashboards, cross-references change records, forms a hypothesis, updates the ticket, and posts findings to Slack. The investigation takes one to three hours, and that’s [...]

GPT-6.1 Sol is now generally available on Amazon Bedrock, bringing stronger reasoning to coding, computer use, and professional workloads that run frequently.

In this post, we describe how the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School built mindLAMP, an open source digital psychiatry platform running on Amazon Web Services (AWS). Now deployed at 65 sites across 17 countries, mindLAMP enables continuous patient monitoring, digital phenotyping, and AI-augmented clinical workflows—all powered by serverless AWS infrastructure that a small academic group can operate without a dedicated site reliability engineering team.

MIXI, Inc. operates FamilyAlbum, a service that approximately 30 million registered users rely on to share photos and videos with the people closest to them. As of January 2026, families have shared more than 20 billion photos and videos through the service. They can rediscover a specific precious moment — a baby’s first swim, an [...]

An AI agent is only as trustworthy as the identity controls and data layer behind it. If you’re the one building those, it’s time to validate — and showcase — exactly what you can do. Today, two new microcredentials focused on agentic AI are now available: AWS Securing Agent Identities Demonstrated and AWS Databases for [...]

Lambda durable functions checkpoint execution state to durable storage, and for regulated payment workloads that data is sensitive. This post shows how to configure a customer managed key in AWS KMS to encrypt durable execution data, define a least-privilege key policy, and verify encryption through AWS CloudTrail, all deployed with Terraform.

Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (specificity, context-setting, few-shot examples, and the CRISPE framework) for consistent, high-quality results across Amazon Quick.

Part 2 of our Amazon Quick prompt engineering series goes component by component. Learn the prompt patterns that get the best results from Amazon Quick Research, Quick Flows, Quick Sight, chat agents, and action integrations, plus the common pitfalls to avoid.

Debugging Apache Airflow DAG failures across services like AWS Glue, Amazon EMR, and Amazon Athena is slow and manual. In this post, we show you how to build a custom Airflow plugin that integrates with Amazon Bedrock to automatically analyze DAG task failures and deliver on-demand root cause analysis on Amazon MWAA.

Manually extracting data from hundreds of vendor contracts doesn’t scale, and RAG chat tools fall short on portfolio-wide questions. This post shares a contract intelligence platform on AWS that uses AI agents to extract and verify contract fields, then answers aggregate and single-contract questions through Amazon Quick analytics.

Managing Amazon EC2 AMIs at scale means constantly mapping AMI IDs to their AWS Systems Manager parameter paths by hand. A recent enhancement to the DescribeImages API returns the associated SSM parameter directly. This post shows how to use it across the AWS CLI, AWS CloudFormation, Terraform, and Auto Scaling launch templates.

Announcing Spark Connect on Amazon EMR on EKS: build, test, and debug Spark applications from VS Code, PyCharm, Jupyter notebooks, Amazon SageMaker Unified Studio, or dbt, while running full-scale Spark operations on your existing Amazon EKS clusters.

Security incident triage usually starts cold. An Amazon CloudWatch alarm fires at 2 a.m. because an AWS Identity and Access Management (IAM) access key just created another access key. The responder spends the first 30 minutes assembling evidence from AWS CloudTrail, Amazon CloudWatch Logs, and Amazon Virtual Private Cloud (Amazon VPC) Flow Logs before deciding [...]

Amazon Aurora PostgreSQL zero-ETL integration with Amazon SageMaker replicates your operational data to a lakehouse in near real time, without building custom ETL pipelines. Learn the architecture and change data capture mechanics, then set up the integration and query your data in Amazon SageMaker.

Every code review discussion, incident response thread, and standup happens in Slack. But when an engineer needs to analyze a service or debug a failing test, they leave Slack, open a terminal, navigate to the repository, run commands, and paste the output back. That round trip takes 30 seconds for someone who knows exactly where [...]