9/2/2026, 12:00:00 AM ~ 9/3/2026, 12:00:00 AM (UTC)

Recent Announcements

Web Search on Amazon Bedrock is now available in AWS GovCloud (US-West)

The Web Search built-in server-side tool on Amazon Bedrock is now available in AWS GovCloud (US-West), helping bring grounded web results to compliance-sensitive government and public-sector workloads. Web Search helps supported OpenAI GPT models ground responses with information from the web. Responses include citations to the sources the model used so users can trace each claim back to its web origin. This can be especially valuable whenever an answer depends on information that changes over time or is more recent than a model’s training data, such as current events, recent releases or live pricing. Because the tool runs inside Amazon Bedrock, you don’t host a search index, manage crawlers, or write the tool-call loop yourself.\n Web Search is designed to support the governance and data-handling standards AWS GovCloud (US) customers require. By default, it keeps your request data within the AWS boundary, serving results from a web index and cache maintained by Amazon. As an AWS-native capability governed by AWS Identity and Access Management (IAM), administrators can allow or deny it at the account or organization level and restrict it by Region, giving teams centralized control while keeping request data within the AWS boundary by default. To get started, add a tool of type web_search to the tools array in your OpenAI Responses API request using your existing OpenAI client library with an Amazon Bedrock API key. The model uses the tool only when it determines a request needs current information. At launch, Web Search in AWS GovCloud (US-West) supports GPT-5.4 , GPT-5.6 Terra and Luna models.

Web Search is available in AWS GovCloud (US-West), in addition to US East (N. Virginia), US East (Ohio), and US West (Oregon). To get started, see the Web Search technical blog. For implementation guidance, see the Web Search documentation. For pricing, see the Amazon Bedrock pricing page.

Amazon Connect Customer expands automated performance evaluations to Malay

Amazon Connect Customer now automates evaluations of human and AI agents in Malay using generative AI. Managers define custom evaluation criteria in natural language and receive AI-generated evaluations with justifications in their preferred language. Performance evaluations also supports cross-language evaluation and can complete assessments in English, even when the conversation is in Malay. This enables multilingual contact centers to use a standardized evaluation framework across languages.\n This feature is supported in 8 AWS regions including US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (London), Canada (Central), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Asia Pacific (Singapore). For information about Amazon Connect pricing, please visit our pricing page. To learn more, please visit our documentation and our webpage.

Amazon Quick adds new tool settings and Model Context Protocol (MCP) sync support for connectors

Amazon Quick connectors let users leverage tools and services such as Outlook, Slack, Salesforce, Jira, and homegrown MCP servers directly into their workflows across chat, agents, apps, flows, and deep research. Today, Amazon Quick introduces new tool settings and MCP sync support that give admins and connector owners more control over how connectors are deployed and kept up to date.\n Connector owners and admins can now selectively enable or disable individual tools within a connector to ensure only approved tools are available to end users. Additionally, new tool permission settings let connector owners decide which tools require consent before proceeding or give end users the flexibility to decide for themselves. Lastly, MCP sync keeps connectors current as external MCP servers add new tools, update descriptions, and evolve their capabilities, ensuring users always have the latest information to get their work done.

These features are available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick User Guide.

Amazon Connect Customer announces general availability of agentic CX designer

Amazon Connect Customer announces the general availability of agentic CX designer, a no-code canvas for designing and deploying AI-powered self-service experiences. You can now build and launch voice and digital experiences that bring agentic and deterministic AI together to transform how you serve customers with the control and reliability enterprises demand. Your business teams users can go from designing conversations and integrating with the systems that run your business, to testing, to launching production-ready experiences in weeks, not months.\n Agentic CX designer gives you the clarity of a flowchart with the power of a large language model. On a visual canvas you define the logic, guardrails, and integrations, and the model handles the natural conversation. For outcomes that have to be exact, such as eligibility, approvals, routing, or compliance, you define the workflow and the conversation follows it. You build, test, and deploy in the same place, so the team that designs an experience can validate it and put it into production without writing code or handing the work to engineering.

Second-generation AWS Outposts racks now in the AWS GovCloud (US) Regions

Second-generation AWS Outposts racks are now supported in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Outposts racks extend AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience.\n Organizations from startups to enterprises and the public sector can now order their Outposts racks connected to the new supported regions, optimizing for their latency and data residency needs. Outposts allows customers to run workloads that need low latency access to on-premises systems locally while connecting back to their home Region for application management. Customers can also use Outposts and AWS services to manage and process data that needs to remain on-premises to meet data residency requirements. This regional expansion provides additional flexibility in the AWS Regions that customers’ Outposts can connect to. To learn more about second-generation Outposts racks, read this blog post and user guide. For the most updated list of countries and territories and the AWS Regions where second-generation Outposts racks are supported, check out the Outposts rack FAQs page.

AWS Config now supports 60 new resource types

AWS Config now supports 60 additional AWS resource types across key services including Amazon Bedrock,  Amazon EC2, Amazon SageMaker, and AWS Organizations. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.\n With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators.

You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the resources are available:

Resource Types:

AWS::AppSync::ChannelNamespace AWS::EC2::RouteServer AWS::Organizations::Policy

AWS::AppSync::SourceApiAssociation AWS::EC2::RouteServerEndpoint AWS::Organizations::ResourcePolicy

AWS::Bedrock::EnforcedGuardrailConfiguration AWS::EC2::RouteServerPeer AWS::QuickSight::RefreshSchedule

AWS::Bedrock::Flow AWS::EKS::PodIdentityAssociation AWS::RDS::DBProxy

AWS::Bedrock::FlowVersion AWS::ElasticLoadBalancingV2::ListenerRule AWS::S3Vectors::Index

AWS::Bedrock::PromptVersion AWS::GameLiftStreams::Application AWS::SageMaker::Action

AWS::BedrockAgentCore::OAuth2CredentialProvider AWS::GameLiftStreams::StreamGroup AWS::SageMaker::Algorithm

AWS::BedrockAgentCore::PaymentManager AWS::IdentityStore::Group AWS::SageMaker::App

AWS::BedrockAgentCore::Policy AWS::IoT::TopicRuleDestination AWS::SageMaker::Context

AWS::BedrockAgentCore::PolicyEngine AWS::Lightsail::Container AWS::SageMaker::Hub

AWS::BedrockAgentCore::TokenVault AWS::Lightsail::Database AWS::SageMaker::MlflowApp

AWS::Chime::AppInstance AWS::Lightsail::Distribution AWS::SageMaker::ModelCard

AWS::CloudTrail::ResourcePolicy AWS::Lightsail::Domain AWS::SageMaker::ModelPackage

AWS::CodePipeline::Webhook AWS::Lightsail::Instance AWS::SES::MailManagerArchive

AWS::Config::OrganizationConformancePack AWS::Lightsail::LoadBalancer AWS::Transfer::WebApp

AWS::Connect::AgentStatus AWS::Logs::ResourcePolicy AWS::WorkSpacesWeb::TrustStore

AWS::Connect::EvaluationForm AWS::MediaConnect::Bridge AWS::WorkSpacesWeb::UserAccessLoggingSettings

AWS::Connect::View AWS::NetworkManager::CoreNetwork AWS::XRay::Group

AWS::Connect::ViewVersion AWS::Organizations::Account AWS::XRay::ResourcePolicy

AWS::EC2::NetworkPerformanceMetricSubscription AWS::Organizations::Organization AWS::XRay::SamplingRule

AWS User Experience Customization (UXC) is now available in all commercial AWS Regions

AWS User Experience Customization (UXC) is now available in all commercial AWS Regions. UXC enables account administrators to set a custom account color and control which services and Regions appear in the AWS Management Console.\n Previously, UXC was available only in US East (N. Virginia), requiring customers who manage these settings programmatically to direct API calls to that single Region. With today’s expansion, customers can manage account customizations from any commercial AWS Region using the AWS CLI, AWS SDKs, or AWS CloudFormation — making it easier to configure the console experience alongside existing infrastructure automation.

UXC is available at no additional charge. To get started, see the AWS User Experience Customization documentation.

AWS Lambda now supports SnapStart for container image functions

Starting today, AWS Lambda supports SnapStart for functions packaged as container images, reducing startup times from several seconds to as low as sub-second. Lambda SnapStart is an opt-in capability that makes it easier for you to build highly responsive and scalable applications without provisioning resources or implementing complex performance optimizations.\n Customers deploy Lambda functions with container images to align with their organization’s container-based deployment standards, or to package larger dependencies up to 10 GB. However, larger container images can experience startup times of several seconds as Lambda downloads image layers and initializes the runtime and application code. SnapStart addresses this by taking a snapshot of the initialized execution environment during function deployment, caching it, and resuming from it on invocation, instead of initializing from scratch. Previously, SnapStart was only supported for managed runtimes (Python, .NET, and Java). Starting today, customers can use SnapStart for container images to improve startup times for latency-sensitive workloads such as ML inference and interactive APIs.

Lambda SnapStart for container images is available in all commercial AWS Regions, except Asia Pacific (New Zealand) and Asia Pacific (Taipei). 

You can activate SnapStart for new or existing container image functions using AWS Lambda API, AWS Console, AWS Command Line Interface (AWS CLI), AWS CloudFormation, AWS Serverless Application Model (AWS SAM), AWS SDK, and AWS Cloud Development Kit (AWS CDK). If you use an AWS base image for Lambda with Java (version 11+), Python (version 3.12+), or .NET (version 8+), the experience remains the same as with functions deployed as .zip file archives. For all other AWS base images for Lambda (for example, Node.js or Ruby) or custom base images, refer to the developer guide. For more information about SnapStart, see Lambda documentation. To learn more about pricing for SnapStart for container image functions, visit AWS Lambda Pricing.

Amazon SageMaker Unified Studio CI/CD adds notebook promotion and AI-assisted manifest generation

Amazon SageMaker Unified Studio CI/CD expands its open-source deployment toolkit with two new capabilities: (1) an AI agent skill that automates manifest authoring, and (2) native notebook promotion across environments. Together, they help data teams go from project to production faster while maintaining best-practice defaults across stages.\n AI-assisted manifest generation. The new generate-bundle-manifest agent skill inspects a project’s connections, storage, and workflows and produces a ready-to-use deployment manifest. It applies least-privilege IAM guidance, substitutes environment variables in place of hardcoded resource identifiers, and sets safe defaults such as opt-in catalog handling. Teams can import the skill into their own agents to standardize how they package and promote SageMaker Unified Studio projects across development, test, and production accounts.

Native notebook promotion. The CI/CD toolkit now supports promoting native SMUS Notebooks alongside code, workflows, and catalog assets. Notebook promotion uses an in-place synchronization model that creates a notebook on first deployment and updates it on subsequent deployments, preserving run history across releases. Teams can promote every notebook in a project or select specific notebooks by ID, and a dry-run mode validates S3 connectivity, IAM permissions, and notebook counts before deployment. Notebook promotion integrates with the existing bundle, deploy, destroy, and dry-run commands and requires no changes to current pipeline structure.

Both capabilities are open source and available in all AWS Regions where Amazon SageMaker Unified Studio is offered.

To get started, visit the CICD-for-SageMakerUnifiedStudio repository on GitHub. For more information, see the CI/CD for Amazon SageMaker Unified Studio documentation.

Amazon RDS for SQL Server supports additional SQL trace flags

Amazon RDS for SQL Server supports 18 additional SQL trace flags that you can enable through database parameter groups. Trace flags are configuration switches that modify SQL Server engine behavior — such as query optimizer cardinality estimation, lock escalation, statistics management, and memory handling — to allow database administrators to fine-tune performance and address workload-specific challenges. With this expansion, you have greater flexibility to optimize and stabilize your SQL Server workloads directly within your managed RDS environment.\n The newly supported trace flags include: 647, 652, 1448, 3654, 4138, 4139, 7745, 8285, 8780, 9432, 9481, 9492, 9592, 11024, 11042, 12502, 12618, and 12656. These trace flags address scenarios such as query plan optimization, DDL performance improvements, availability group replication, Query Store behavior, automatic plan correction, and known engine bug mitigations. Because trace flags modify core SQL Server engine behavior, you should test them in a non-production environment before applying them to production instances. Some trace flags can impact system performance, increase memory usage, or change query execution plans in unexpected ways.

Trace flags are available in all AWS Regions where Amazon RDS for SQL Server is supported. To get started, update your RDS parameter group to enable the desired trace flags and apply it to your DB instance. To learn more, see Amazon RDS for SQL Server User Guide.

AWS Blogs

AWS Japan Blog (Japanese)

AWS Big Data Blog

AWS Contact Center

AWS Database Blog

AWS Developer Tools Blog

AWS DevOps & Developer Productivity Blog

AWS for Industries

Artificial Intelligence

Networking & Content Delivery

AWS Security Blog

Open Source Project

AWS CLI

AWS CDK

Amplify for iOS

Amplify for Android