Amazon Elastic Container Registry (Amazon ECR) now supports replication backfill, enabling you to replicate existing images in your private registry to your configured replication registry destinations. Previously, replication only applied to images pushed after the replication configuration was enabled, requiring customers to manually retag images to trigger replication of preexisting images. With replication backfill, you can now replicate your full image catalog to new or existing destinations with a single API call.

Replication backfill is useful when expanding to a new AWS Region, validating disaster recovery readiness, updating replication rules, or ensuring complete image catalogs exist across Regions for compliance purposes. You can run up to ten backfill jobs concurrently, monitor progress and per-image results through new APIs, and stop a job at any time without deleting images that have already been replicated.

Replication backfill is available in all AWS Regions where Amazon ECR is available. To learn more, refer to the Amazon ECR user guide. 

 

​Amazon Elastic Container Registry (Amazon ECR) now supports replication backfill, enabling you to replicate existing images in your private registry to your configured replication registry destinations. Previously, replication only applied to images pushed after the replication configuration was enabled, requiring customers to manually retag images to trigger replication of preexisting images. With replication backfill, you can now replicate your full image catalog to new or existing destinations with a single API call.
Replication backfill is useful when expanding to a new AWS Region, validating disaster recovery readiness, updating replication rules, or ensuring complete image catalogs exist across Regions for compliance purposes. You can run up to ten backfill jobs concurrently, monitor progress and per-image results through new APIs, and stop a job at any time without deleting images that have already been replicated.
Replication backfill is available in all AWS Regions where Amazon ECR is available. To learn more, refer to the Amazon ECR user guide.   

Today, AWS announces light mode support in Amazon SageMaker Unified Studio for Identity Center (IDC) based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes. With this launch, customers can set this visual preference in both IAM-based and IDC-based domains. 

Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over the appearance of your development environment, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can choose ‘Customize appearance’ under your Profile settings to select between visual modes including dark mode, light mode, and device setting, which matches your operating system’s setting. The setting persists across browsers and devices.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, see the Amazon SageMaker Unified Studio User Guide.

 

​Today, AWS announces light mode support in Amazon SageMaker Unified Studio for Identity Center (IDC) based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes. With this launch, customers can set this visual preference in both IAM-based and IDC-based domains. 
Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over the appearance of your development environment, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can choose ‘Customize appearance’ under your Profile settings to select between visual modes including dark mode, light mode, and device setting, which matches your operating system’s setting. The setting persists across browsers and devices.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, see the Amazon SageMaker Unified Studio User Guide.  

Amazon Connect Customer now enables you to validate the outcomes of your customer experience flow actions during testing and simulation. In addition to confirming that an action started, you can now verify the data it returns when it completes, such as a Lambda function response, an Amazon Lex resolved intent, or the variables set by an Agentic CX block. When a test fails, you can pinpoint the exact action that returned an unexpected result instead of tracing it back from a downstream step, so you can resolve issues faster and launch flow changes with greater confidence.

To learn more about this feature, see testing and simulation in the Connect Customer administrator guide. For a complete list of testing and simulation capabilities available by AWS Region, refer to Availability of Connect Customer features by Region. To learn more about Amazon Connect Customer, an agentic AI solution that helps enterprises deliver exceptional customer experiences, visit the Amazon Connect Customer website.

 

​Amazon Connect Customer now enables you to validate the outcomes of your customer experience flow actions during testing and simulation. In addition to confirming that an action started, you can now verify the data it returns when it completes, such as a Lambda function response, an Amazon Lex resolved intent, or the variables set by an Agentic CX block. When a test fails, you can pinpoint the exact action that returned an unexpected result instead of tracing it back from a downstream step, so you can resolve issues faster and launch flow changes with greater confidence.
To learn more about this feature, see testing and simulation in the Connect Customer administrator guide. For a complete list of testing and simulation capabilities available by AWS Region, refer to Availability of Connect Customer features by Region. To learn more about Amazon Connect Customer, an agentic AI solution that helps enterprises deliver exceptional customer experiences, visit the Amazon Connect Customer website.  

Amazon WorkSpaces Core Managed Instances now supports the ModifyVolume API, which lets you change volume size, volume type, IOPS, or throughput in place, without detaching the volume or restarting the instance. Previously, changing storage capacity or performance on a deployed managed instance required rebuilding it.

With ModifyVolume, you can increase volume size up to 16,384 GiB, switch between supported Amazon EBS volume types, adjust IOPS for gp3, io1, and io2 volumes, and modify throughput for gp3 volumes. The API supports idempotency tokens to prevent duplicate changes, and you can track progress with the Amazon EC2 DescribeVolumesModifications operation.

ModifyVolume is available in all AWS Regions where Amazon WorkSpaces Core Managed Instances is available.

To get started, call ModifyVolume directly, or use it through your WorkSpaces Core partner solution once available. To learn more, see ModifyVolume in the WorkSpaces Instances API Reference and Limitations in the Amazon EBS User Guide, or visit WorkSpaces Core.

 

​Amazon WorkSpaces Core Managed Instances now supports the ModifyVolume API, which lets you change volume size, volume type, IOPS, or throughput in place, without detaching the volume or restarting the instance. Previously, changing storage capacity or performance on a deployed managed instance required rebuilding it.
With ModifyVolume, you can increase volume size up to 16,384 GiB, switch between supported Amazon EBS volume types, adjust IOPS for gp3, io1, and io2 volumes, and modify throughput for gp3 volumes. The API supports idempotency tokens to prevent duplicate changes, and you can track progress with the Amazon EC2 DescribeVolumesModifications operation.
ModifyVolume is available in all AWS Regions where Amazon WorkSpaces Core Managed Instances is available.
To get started, call ModifyVolume directly, or use it through your WorkSpaces Core partner solution once available. To learn more, see ModifyVolume in the WorkSpaces Instances API Reference and Limitations in the Amazon EBS User Guide, or visit WorkSpaces Core.  

Trusted identity propagation carries a user’s identity with their request, so AWS services can grant permissions based on who the user is and their underlying group associations, and log that user’s access for auditing. With cross-Region support, this identity context can now be propagated to AWS services running in different AWS Regions, giving you greater flexibility in how you architect and deploy across AWS Regions.

Customers often distribute workloads across AWS Regions for many reasons including separating the compute from the data it queries, to running workloads where capacity is available, or for disaster recovery and global scale. Previously, trusted identity propagation required all of the AWS services accessing a user’s identity to reside in the same AWS Region. To reach services in another AWS Region, customers had to either grant access through broad IAM roles that cannot distinguish individual users or consolidate all their services into a single AWS Region. With cross-Region trusted identity propagation, a user’s identity travels with the request across AWS Regions automatically, so you can grant access based on who the user is and their underlying group associations, and audit who accessed which resources, while preserving fine-grained access control for every individual user.

Cross-Region trusted identity propagation is available at no additional cost in all AWS Regions where IAM Identity Center is available. For the list of Regions where IAM Identity Center is available, see AWS Capabilities by Region.  For more information, see Trusted identity propagation in the IAM Identity Center User Guide.

 

​Trusted identity propagation carries a user’s identity with their request, so AWS services can grant permissions based on who the user is and their underlying group associations, and log that user’s access for auditing. With cross-Region support, this identity context can now be propagated to AWS services running in different AWS Regions, giving you greater flexibility in how you architect and deploy across AWS Regions.
Customers often distribute workloads across AWS Regions for many reasons including separating the compute from the data it queries, to running workloads where capacity is available, or for disaster recovery and global scale. Previously, trusted identity propagation required all of the AWS services accessing a user’s identity to reside in the same AWS Region. To reach services in another AWS Region, customers had to either grant access through broad IAM roles that cannot distinguish individual users or consolidate all their services into a single AWS Region. With cross-Region trusted identity propagation, a user’s identity travels with the request across AWS Regions automatically, so you can grant access based on who the user is and their underlying group associations, and audit who accessed which resources, while preserving fine-grained access control for every individual user.
Cross-Region trusted identity propagation is available at no additional cost in all AWS Regions where IAM Identity Center is available. For the list of Regions where IAM Identity Center is available, see AWS Capabilities by Region.  For more information, see Trusted identity propagation in the IAM Identity Center User Guide.  

Today, AWS announces the general availability of OpenAI GPT-6.1 Terra on Amazon Bedrock. GPT-6.1 Terra expands the GPT-6 family with a balance of capability, speed, and cost efficiency for everyday workloads. 

As the newest model in the Terra line, GPT-6.1 Terra combines detailed visual reasoning, accurate computer use, and improved factuality and natural writing over GPT-5.6 Terra, for complex workflows. In OpenAI’s Agents’ Last Exam evaluation, it scored 15 percentage points higher than GPT-5.6 Terra, performed on par with GPT-6 Astra, and completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. GPT-6.1 Terra offers balanced performance with input and output token prices at least 50% lower than those of GPT-6.1 Sol and GPT-5.6 Terra. It supports a context window of up to 1 million input tokens and accepts both text and image inputs. The Amazon Bedrock inference engine delivers the performance, security, and scale required for enterprise workloads. 

You can call GPT-6.1 Terra directly through Amazon Bedrock APIs or configure ChatGPT Work and Codex to use the model on Amazon Bedrock. Use it to draft proposals, interpret technical drawings, investigate codebases, and build agents that operate across business systems.  Established AWS controls help you secure workloads, govern access, and audit model invocation activity. 

You can get started in the Amazon Bedrock console or programmatically through supported Amazon Bedrock APIs. For information about supported AWS Regions, endpoints, APIs, features, inference profiles and pricing, see the Amazon Bedrock documentation.  

 

​Today, AWS announces the general availability of OpenAI GPT-6.1 Terra on Amazon Bedrock. GPT-6.1 Terra expands the GPT-6 family with a balance of capability, speed, and cost efficiency for everyday workloads. 
As the newest model in the Terra line, GPT-6.1 Terra combines detailed visual reasoning, accurate computer use, and improved factuality and natural writing over GPT-5.6 Terra, for complex workflows. In OpenAI’s Agents’ Last Exam evaluation, it scored 15 percentage points higher than GPT-5.6 Terra, performed on par with GPT-6 Astra, and completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. GPT-6.1 Terra offers balanced performance with input and output token prices at least 50% lower than those of GPT-6.1 Sol and GPT-5.6 Terra. It supports a context window of up to 1 million input tokens and accepts both text and image inputs. The Amazon Bedrock inference engine delivers the performance, security, and scale required for enterprise workloads. 
You can call GPT-6.1 Terra directly through Amazon Bedrock APIs or configure ChatGPT Work and Codex to use the model on Amazon Bedrock. Use it to draft proposals, interpret technical drawings, investigate codebases, and build agents that operate across business systems.  Established AWS controls help you secure workloads, govern access, and audit model invocation activity. 
You can get started in the Amazon Bedrock console or programmatically through supported Amazon Bedrock APIs. For information about supported AWS Regions, endpoints, APIs, features, inference profiles and pricing, see the Amazon Bedrock documentation.    

Now you can view your metrics across customer segments: customer lifetime value (LTV) and repeat callers. See how any metric differs for repeat callers versus the rest of your contacts, or how results hold up among your highest-LTV customers; so you can spot the groups trending the wrong way before it shows up in the aggregate. Where a segment is underperforming, you can act. Route high-LTV customers to your most effective people, adjust the knowledge bases and self-service flows that repeat callers are falling back from, or refine agent guidance for segments where satisfaction is slipping. The segment tells you where to look; the metrics tell you what to fix.

 

​Now you can view your metrics across customer segments: customer lifetime value (LTV) and repeat callers. See how any metric differs for repeat callers versus the rest of your contacts, or how results hold up among your highest-LTV customers; so you can spot the groups trending the wrong way before it shows up in the aggregate. Where a segment is underperforming, you can act. Route high-LTV customers to your most effective people, adjust the knowledge bases and self-service flows that repeat callers are falling back from, or refine agent guidance for segments where satisfaction is slipping. The segment tells you where to look; the metrics tell you what to fix.  

Amazon Connect Customer now scores the outcome of your customer interactions. It shows whether the customer left satisfied, is at risk of churning, made a purchase, or resolved their need in self-service. You can now see whether an interaction worked, not just how efficiently it ran. Each metric comes with a default definition you can adjust to match how your business defines success and are available both through the analytics dashboards and APIs.
 

 

​Amazon Connect Customer now scores the outcome of your customer interactions. It shows whether the customer left satisfied, is at risk of churning, made a purchase, or resolved their need in self-service. You can now see whether an interaction worked, not just how efficiently it ran. Each metric comes with a default definition you can adjust to match how your business defines success and are available both through the analytics dashboards and APIs.    

Web Search on AWS Bedrock now supports two new controls that give you more precision over the results that ground a model’s response: domain filtering and user location. Both are configured directly on the web_search tool in your existing OpenAI Responses API request.

With domain filtering, you can restrict Web Search results to a set of domains you choose. Using the filters parameter, you specify up to 100 allowed_domains to limit results to trusted sources, or up to 100 blocked_domains to exclude sources you don’t want. This is useful when you want responses grounded only in specific sites, or when you need to keep certain domains out of results.

With user location, you can optionally specify location parameters so that Web Search can return results relevant to that geography. This helps with location-dependent questions, such as regional availability or local services.

Domain filtering and user location are available in all AWS Regions where Web Search is supported: US East (N. Virginia), US East (Ohio), US West (Oregon), and AWS GovCloud (US-West). To get started, see the Web Search section in the AWS Bedrock User Guide. For pricing, see the AWS Bedrock pricing page.

 

​Web Search on AWS Bedrock now supports two new controls that give you more precision over the results that ground a model’s response: domain filtering and user location. Both are configured directly on the web_search tool in your existing OpenAI Responses API request.
With domain filtering, you can restrict Web Search results to a set of domains you choose. Using the filters parameter, you specify up to 100 allowed_domains to limit results to trusted sources, or up to 100 blocked_domains to exclude sources you don’t want. This is useful when you want responses grounded only in specific sites, or when you need to keep certain domains out of results.
With user location, you can optionally specify location parameters so that Web Search can return results relevant to that geography. This helps with location-dependent questions, such as regional availability or local services.
Domain filtering and user location are available in all AWS Regions where Web Search is supported: US East (N. Virginia), US East (Ohio), US West (Oregon), and AWS GovCloud (US-West). To get started, see the Web Search section in the AWS Bedrock User Guide. For pricing, see the AWS Bedrock pricing page.  

Amazon Connect Customer Global Resiliency now provides customers a mechanism to deliver long, persistent chat experiences across linked AWS Regions. With this capability, end users can resume previous conversations with full context, metadata, and transcripts carried over across Regions, eliminating the need for end users to repeat information and allowing agents to provide personalized service with access to the entire conversation history across AWS Regions.

For example, an end user can start a chat in US-East (N. Virginia) and later come back to it in the linked US-West (Oregon) Region, continuing the same conversation with an agent with full conversation history and chat attachment. To adopt this capability, onboard to Global routing.

This feature is available in all AWS Regions where Amazon Connect Customer Global Resiliency is supported. To learn more, visit Amazon Connect Customer Admin Guide.

 

​Amazon Connect Customer Global Resiliency now provides customers a mechanism to deliver long, persistent chat experiences across linked AWS Regions. With this capability, end users can resume previous conversations with full context, metadata, and transcripts carried over across Regions, eliminating the need for end users to repeat information and allowing agents to provide personalized service with access to the entire conversation history across AWS Regions.
For example, an end user can start a chat in US-East (N. Virginia) and later come back to it in the linked US-West (Oregon) Region, continuing the same conversation with an agent with full conversation history and chat attachment. To adopt this capability, onboard to Global routing.
This feature is available in all AWS Regions where Amazon Connect Customer Global Resiliency is supported. To learn more, visit Amazon Connect Customer Admin Guide.