You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems so that you can access your file data as if it were in S3. With this new capability, your file data in FSx for NetApp ONTAP is effortlessly accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3 while your file data continues to reside in your FSx for NetApp ONTAP file system.

Amazon FSx for NetApp ONTAP is the first and only complete, fully managed NetApp ONTAP file system in the cloud, allowing you to migrate on-premises applications that rely on NetApp ONTAP or other NAS appliances to AWS without having to change how you manage your data. An S3 Access Point is an endpoint that helps control and simplify how different applications or users can access data. Now, with S3 Access Points for FSx for NetApp ONTAP, you can discover new insights, innovate faster, and make even better data-driven decisions with the data you migrate to AWS. For example, you can use your data to augment generative AI applications with Amazon Bedrock, train machine learning models with Amazon SageMaker, run analysis using Amazon Glue or a wide range of AWS Data and Analytics Competency Partner solutions, and run workflows using S3-based cloud-native applications.

Get started with this capability by creating and attaching S3 Access Points to new FSx for NetApp ONTAP file systems using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). Support for existing FSx for NetApp ONTAP file systems will come in an upcoming weekly maintenance window. This new capability is available in the select AWS Regions.

To get started, see the following list of resources:

• Amazon FSx for NetApp ONTAP
• Amazon S3 Access Points
• AWS News Blog

 

​You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems so that you can access your file data as if it were in S3. With this new capability, your file data in FSx for NetApp ONTAP is effortlessly accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3 while your file data continues to reside in your FSx for NetApp ONTAP file system. Amazon FSx for NetApp ONTAP is the first and only complete, fully managed NetApp ONTAP file system in the cloud, allowing you to migrate on-premises applications that rely on NetApp ONTAP or other NAS appliances to AWS without having to change how you manage your data. An S3 Access Point is an endpoint that helps control and simplify how different applications or users can access data. Now, with S3 Access Points for FSx for NetApp ONTAP, you can discover new insights, innovate faster, and make even better data-driven decisions with the data you migrate to AWS. For example, you can use your data to augment generative AI applications with Amazon Bedrock, train machine learning models with Amazon SageMaker, run analysis using Amazon Glue or a wide range of AWS Data and Analytics Competency Partner solutions, and run workflows using S3-based cloud-native applications. Get started with this capability by creating and attaching S3 Access Points to new FSx for NetApp ONTAP file systems using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). Support for existing FSx for NetApp ONTAP file systems will come in an upcoming weekly maintenance window. This new capability is available in the select AWS Regions. To get started, see the following list of resources:

Amazon FSx for NetApp ONTAP
Amazon S3 Access Points
AWS News Blog  

Amazon RDS for SQL Server launches optimize CPU with support for M7i and R7i instance families, which reduce prices by up to 55% compared to equivalent previous generation instances. Optimize CPU optimizes Simultaneous Multi-threading (SMT) configuration to reduce commercial software charges. Customers can lower cost by upgrading to M7i and R7i instances from similar 6th generation instances. Furthermore, for memory or IO intensive database workloads, customers can get additional cost reduction by fine tuning optimize CPU configuration.

RDS for SQL Server price for database instance hours consumed is inclusive of Microsoft Windows and Microsoft SQL Server software charges. Optimize CPU disables SMT for instances with 2 or more physical CPU cores. This reduces the number of vCPUs, and the corresponding commercial software charges by 50% while providing the same number of physical CPU cores, and near equivalent performance. The most significant savings are available on 2Xlarge and higher instances, and instances that use Multi-AZ deployment, where RDS optimizes to reduce SQL Server software charges for only a single active node for most usage. For workloads that are memory or IO intensive, customers can fine tune the number of active physical CPU cores for further savings.

RDS for SQL Server supports M7i and R7i instances in all AWS Regions. With unbundled instance pricing, database costs are calculated with separate charges for third party licensing fees per vCPU hour, and third party licensing fees are not eligible towards your organization’s discounts with AWS. You can view Microsoft Windows and SQL Server charges associated with your usage on AWS Billing and Cost Management, and in monthly bills. For more details, visit RDS for SQL Server pricing, Amazon RDS User Guide and AWS News Blog.

 

​Amazon RDS for SQL Server launches optimize CPU with support for M7i and R7i instance families, which reduce prices by up to 55% compared to equivalent previous generation instances. Optimize CPU optimizes Simultaneous Multi-threading (SMT) configuration to reduce commercial software charges. Customers can lower cost by upgrading to M7i and R7i instances from similar 6th generation instances. Furthermore, for memory or IO intensive database workloads, customers can get additional cost reduction by fine tuning optimize CPU configuration. RDS for SQL Server price for database instance hours consumed is inclusive of Microsoft Windows and Microsoft SQL Server software charges. Optimize CPU disables SMT for instances with 2 or more physical CPU cores. This reduces the number of vCPUs, and the corresponding commercial software charges by 50% while providing the same number of physical CPU cores, and near equivalent performance. The most significant savings are available on 2Xlarge and higher instances, and instances that use Multi-AZ deployment, where RDS optimizes to reduce SQL Server software charges for only a single active node for most usage. For workloads that are memory or IO intensive, customers can fine tune the number of active physical CPU cores for further savings. RDS for SQL Server supports M7i and R7i instances in all AWS Regions. With unbundled instance pricing, database costs are calculated with separate charges for third party licensing fees per vCPU hour, and third party licensing fees are not eligible towards your organization’s discounts with AWS. You can view Microsoft Windows and SQL Server charges associated with your usage on AWS Billing and Cost Management, and in monthly bills. For more details, visit RDS for SQL Server pricing, Amazon RDS User Guide and AWS News Blog.  

AWS Transform adds powerful new agentic AI capabilities to automate VMware migrations to AWS. The migration agent collaborates with migration teams to understand business priorities and intelligently plan and migrate hundreds of applications spanning thousands of servers, significantly reducing manual effort, time, and complexity.

The agent can now discover your on-premises environment and prioritize applications for migration using the AWS Transform discovery tool, inventory data from various third-party discovery tools, and unstructured data such as documents, notes, and business rules. It analyzes infrastructure, database, and application details, maps dependencies, and generates migration plans grouped by business and technical priorities such as ownership, department, function, subnet, and operating systems. It generates networks with hub-and-spoke and isolated network configurations, provides flexible IP address management options, deploys to multiple accounts, generates network configurations for your AWS landing zones, and migrates from source environments like NSX, Palo Alto, Fortigate, and Cisco ACI. The agent migrates servers to AWS securely and iteratively in waves and provides clear progress updates throughout the deployment. It also migrates Windows and Linux x86 servers, hypervisors such as VMware, HyperV, Nutanix, and KVM, and bare-metal physical environments to multiple target accounts. Throughout your migration, you can ask the agent questions as it guides your decisions, whether that’s repeating or skipping steps, or adjusting plans. To simplify internal approvals, the agent also generates a detailed report with the migration plan and mapping of networks, servers, and applications.

With AWS Transform, you can accelerate time to value, lower risk, and reduce the complexity of VMware migrations. These new capabilities are available in all AWS Regions where AWS Transform is offered, with support for migrating servers and networks to 16 AWS Regions.

Learn more on the product page and user guide, and get started with AWS Transform.

 

​AWS Transform adds powerful new agentic AI capabilities to automate VMware migrations to AWS. The migration agent collaborates with migration teams to understand business priorities and intelligently plan and migrate hundreds of applications spanning thousands of servers, significantly reducing manual effort, time, and complexity. The agent can now discover your on-premises environment and prioritize applications for migration using the AWS Transform discovery tool, inventory data from various third-party discovery tools, and unstructured data such as documents, notes, and business rules. It analyzes infrastructure, database, and application details, maps dependencies, and generates migration plans grouped by business and technical priorities such as ownership, department, function, subnet, and operating systems. It generates networks with hub-and-spoke and isolated network configurations, provides flexible IP address management options, deploys to multiple accounts, generates network configurations for your AWS landing zones, and migrates from source environments like NSX, Palo Alto, Fortigate, and Cisco ACI. The agent migrates servers to AWS securely and iteratively in waves and provides clear progress updates throughout the deployment. It also migrates Windows and Linux x86 servers, hypervisors such as VMware, HyperV, Nutanix, and KVM, and bare-metal physical environments to multiple target accounts. Throughout your migration, you can ask the agent questions as it guides your decisions, whether that’s repeating or skipping steps, or adjusting plans. To simplify internal approvals, the agent also generates a detailed report with the migration plan and mapping of networks, servers, and applications. With AWS Transform, you can accelerate time to value, lower risk, and reduce the complexity of VMware migrations. These new capabilities are available in all AWS Regions where AWS Transform is offered, with support for migrating servers and networks to 16 AWS Regions. Learn more on the product page and user guide, and get started with AWS Transform.  

AWS Transform for mainframe delivers new data and activity analysis capabilities to extract comprehensive insights to drive the reimagining of mainframe applications. These insights can be combined with business logic extraction to inform decomposition of legacy applications into logical business domains. Together, these form the basis of a comprehensive specification for coding agents like Kiro to reimagine applications into cloud-native architectures.

The new capabilities empower organizations to reimagine legacy workloads, providing a comprehensive reverse engineering workflow that includes automated code and data structure analysis, activity analysis, technical documentation generation, business logic extraction, and intelligent code decomposition. Through in-depth data and activity analysis, AWS Transform helps identify application components with high utilization or business value, allowing teams to optimize their modernization efforts and make data-informed architectural decisions.

In the AI-powered chat interface, users can customize their modernization approach through flexible job plans that allow them to select predefined comprehensive workflows—full modernization, analysis focus, or business logic focus—or create their own combination of capabilities based on specific objectives.

The reimagine capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions.

To learn more about reimagining mainframe applications with AWS Transform for mainframe, read the AWS News Blog post or visit the AWS Transform product page. 

 

​AWS Transform for mainframe delivers new data and activity analysis capabilities to extract comprehensive insights to drive the reimagining of mainframe applications. These insights can be combined with business logic extraction to inform decomposition of legacy applications into logical business domains. Together, these form the basis of a comprehensive specification for coding agents like Kiro to reimagine applications into cloud-native architectures. The new capabilities empower organizations to reimagine legacy workloads, providing a comprehensive reverse engineering workflow that includes automated code and data structure analysis, activity analysis, technical documentation generation, business logic extraction, and intelligent code decomposition. Through in-depth data and activity analysis, AWS Transform helps identify application components with high utilization or business value, allowing teams to optimize their modernization efforts and make data-informed architectural decisions. In the AI-powered chat interface, users can customize their modernization approach through flexible job plans that allow them to select predefined comprehensive workflows—full modernization, analysis focus, or business logic focus—or create their own combination of capabilities based on specific objectives. The reimagine capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions. To learn more about reimagining mainframe applications with AWS Transform for mainframe, read the AWS News Blog post or visit the AWS Transform product page.   

Amazon Connect now supports creation of custom metrics, enabling contact center supervisors to analyze tailored performance measurements without requiring technical skills. This feature provides a simple, no-code interface for performing mathematical operations (e.g., addition, subtraction, sum, average) on existing Connect data to build metrics that align with your organization’s specific business requirements. Custom metrics are available to use in the dashboards and APIs.

With custom metrics, you can track performance in ways that matter most to your business. For example, create average handle time metrics for premium versus standard customer segments, calculate total agent time on outbound calls by product line, or measure queue performance filtered by contact type such as callbacks versus incoming calls.
This new feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect custom metrics, see the Administrator Guide. To learn more about Amazon Connect, see the Amazon Connect website.

 

​Amazon Connect now supports creation of custom metrics, enabling contact center supervisors to analyze tailored performance measurements without requiring technical skills. This feature provides a simple, no-code interface for performing mathematical operations (e.g., addition, subtraction, sum, average) on existing Connect data to build metrics that align with your organization’s specific business requirements. Custom metrics are available to use in the dashboards and APIs. With custom metrics, you can track performance in ways that matter most to your business. For example, create average handle time metrics for premium versus standard customer segments, calculate total agent time on outbound calls by product line, or measure queue performance filtered by contact type such as callbacks versus incoming calls. This new feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect custom metrics, see the Administrator Guide. To learn more about Amazon Connect, see the Amazon Connect website.  

AWS Transform is expanding its capability from the .NET modernization agent to now include the full-stack Windows modernization agent that handles both .NET applications and their associated databases. The new agent automates the transformation of .NET applications and Microsoft SQL Server databases to Amazon Aurora PostgreSQL and deploys them to containers on Amazon ECS or Amazon EC2 Linux. AWS Transform accelerates full-stack Windows modernization by 5x across application and database layers, while reducing operating costs by up to 70%.

With AWS Transform, customers can accelerate their full-stack modernization journey through automated discovery, transformation, and deployment. The full-stack Windows modernization agent scans Microsoft SQL Server databases in Amazon EC2 or Amazon RDS instances, and it scans .NET application code from source repositories (GitHub, GitLab, Bitbucket, or Azure Repos) to create customized, editable modernization plans. It automatically transforms SQL Server schemas to Aurora PostgreSQL and migrates databases to new or existing Aurora PostgreSQL target clusters. For .NET application transformation, the agent updates database connections in the source code and modifies database access code written in Entity Framework and ADO.NET to be compatible with Aurora PostgreSQL—all in a unified workflow with human supervision. All the transformed code is committed to a new repository branch. Finally, the transformed application along with the databases can be deployed into a new or existing environment to validate the transformed applications and databases. Customers can monitor transformation progress through worklog updates and interactive chat, and they can use the detailed transformation summaries for next steps recommendations and for easy handoff to AI code companions.

AWS Transform for full-stack Windows modernization is available in the US East (N. Virginia) AWS Region.

To learn more, visit the overview page and AWS Transform documentation.

 

​AWS Transform is expanding its capability from the .NET modernization agent to now include the full-stack Windows modernization agent that handles both .NET applications and their associated databases. The new agent automates the transformation of .NET applications and Microsoft SQL Server databases to Amazon Aurora PostgreSQL and deploys them to containers on Amazon ECS or Amazon EC2 Linux. AWS Transform accelerates full-stack Windows modernization by 5x across application and database layers, while reducing operating costs by up to 70%. With AWS Transform, customers can accelerate their full-stack modernization journey through automated discovery, transformation, and deployment. The full-stack Windows modernization agent scans Microsoft SQL Server databases in Amazon EC2 or Amazon RDS instances, and it scans .NET application code from source repositories (GitHub, GitLab, Bitbucket, or Azure Repos) to create customized, editable modernization plans. It automatically transforms SQL Server schemas to Aurora PostgreSQL and migrates databases to new or existing Aurora PostgreSQL target clusters. For .NET application transformation, the agent updates database connections in the source code and modifies database access code written in Entity Framework and ADO.NET to be compatible with Aurora PostgreSQL—all in a unified workflow with human supervision. All the transformed code is committed to a new repository branch. Finally, the transformed application along with the databases can be deployed into a new or existing environment to validate the transformed applications and databases. Customers can monitor transformation progress through worklog updates and interactive chat, and they can use the detailed transformation summaries for next steps recommendations and for easy handoff to AI code companions. AWS Transform for full-stack Windows modernization is available in the US East (N. Virginia) AWS Region. To learn more, visit the overview page and AWS Transform documentation.  

AWS Transform custom is now generally available, accelerating organization-specific code and application modernization at scale using agentic AI. AWS Transform is the first agentic AI service to accelerate the transformation of Windows, mainframe, VMware, and more—reducing technical debt and making your tech stack AI-ready. Technical debt accumulates when organizations maintain legacy systems and outdated code, requiring them to allocate 20-30% of their software development resources to repeatable, cross-codebase transformation tasks that must be performed manually. AWS Transform can automate repeatable transformations of version upgrades, runtime migrations, framework transitions, and language translations at scale, reducing execution time by over 80% in many cases while eliminating the need for specialized automation expertise.

The custom transformation agent in AWS Transform provides both pre-built and custom solutions. It includes out-of-the-box transformations for common scenarios, such as Python and Node.js runtime upgrades, Lambda function modernization, AWS SDK updates across multiple languages, and Java 8 to 17 upgrades (supporting any build system including Gradle and Maven). For organization-specific needs, teams can define custom transformations using natural language, reference documents, and code samples. Users can trigger autonomous transformations with a simple one-line CLI command, which can be scripted or embedded into any existing pipeline or workflow. Within your organization, the agent continually learns from developer feedback and execution results, improving transformation accuracy and tightly aligning the agent’s performance with your organization’s preferences. This approach enables organizations to systematically address technical debt at scale, with the agent continually improving while developers can focus on innovation and high-impact tasks.

AWS Transform custom is now available in the US East (N. Virginia) AWS Region.

To learn more, visit the user guide, overview page, and pricing page.

 

​AWS Transform custom is now generally available, accelerating organization-specific code and application modernization at scale using agentic AI. AWS Transform is the first agentic AI service to accelerate the transformation of Windows, mainframe, VMware, and more—reducing technical debt and making your tech stack AI-ready. Technical debt accumulates when organizations maintain legacy systems and outdated code, requiring them to allocate 20-30% of their software development resources to repeatable, cross-codebase transformation tasks that must be performed manually. AWS Transform can automate repeatable transformations of version upgrades, runtime migrations, framework transitions, and language translations at scale, reducing execution time by over 80% in many cases while eliminating the need for specialized automation expertise.
The custom transformation agent in AWS Transform provides both pre-built and custom solutions. It includes out-of-the-box transformations for common scenarios, such as Python and Node.js runtime upgrades, Lambda function modernization, AWS SDK updates across multiple languages, and Java 8 to 17 upgrades (supporting any build system including Gradle and Maven). For organization-specific needs, teams can define custom transformations using natural language, reference documents, and code samples. Users can trigger autonomous transformations with a simple one-line CLI command, which can be scripted or embedded into any existing pipeline or workflow. Within your organization, the agent continually learns from developer feedback and execution results, improving transformation accuracy and tightly aligning the agent’s performance with your organization’s preferences. This approach enables organizations to systematically address technical debt at scale, with the agent continually improving while developers can focus on innovation and high-impact tasks.
AWS Transform custom is now available in the US East (N. Virginia) AWS Region.
To learn more, visit the user guide, overview page, and pricing page.  

Today, AWS announces the general availability of expanded .NET transformation capabilities and an enhanced developer experience in AWS Transform. Customers can now modernize .NET Framework and .NET code to .NET 10 or .NET Standard. New transformation capabilities include UI porting of ASP.NET Web Forms to Blazor on ASP.NET Core and porting Entity Framework ORM code. The new developer experience, available with the AWS Toolkit for Visual Studio 2026 or 2022, is customizable, interactive, and iterative. It includes an editable transformation plan, estimated transformation time, real-time updates during transformation, the ability to repeat transformations with a revised plan, and next steps markdown for easy handoff to AI code companions. With these enhancements, AWS Transform provides a path to modern .NET for more project types, supports the latest releases of .NET and Visual Studio, and gives developers oversight and control of transformations.

Developers can now streamline their .NET modernization through an enhanced IDE experience. The process begins with automated code analysis that produces a customizable transformation plan. Developers can customize the transformation plan, such as fine-tuning package updates. Throughout the transformation, they benefit from transparent progress tracking and detailed activity logs. Upon completion, developers receive a Next Steps document that outlines remaining tasks, including Linux readiness requirements, which they can address through additional AWS Transform iterations or by leveraging AI code companion tools such as Kiro.

AWS Transform is available in the following AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London).

To get started with AWS Transform, refer to the AWS Transform documentation.

 

​Today, AWS announces the general availability of expanded .NET transformation capabilities and an enhanced developer experience in AWS Transform. Customers can now modernize .NET Framework and .NET code to .NET 10 or .NET Standard. New transformation capabilities include UI porting of ASP.NET Web Forms to Blazor on ASP.NET Core and porting Entity Framework ORM code. The new developer experience, available with the AWS Toolkit for Visual Studio 2026 or 2022, is customizable, interactive, and iterative. It includes an editable transformation plan, estimated transformation time, real-time updates during transformation, the ability to repeat transformations with a revised plan, and next steps markdown for easy handoff to AI code companions. With these enhancements, AWS Transform provides a path to modern .NET for more project types, supports the latest releases of .NET and Visual Studio, and gives developers oversight and control of transformations.
Developers can now streamline their .NET modernization through an enhanced IDE experience. The process begins with automated code analysis that produces a customizable transformation plan. Developers can customize the transformation plan, such as fine-tuning package updates. Throughout the transformation, they benefit from transparent progress tracking and detailed activity logs. Upon completion, developers receive a Next Steps document that outlines remaining tasks, including Linux readiness requirements, which they can address through additional AWS Transform iterations or by leveraging AI code companion tools such as Kiro.
AWS Transform is available in the following AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London).
To get started with AWS Transform, refer to the AWS Transform documentation.  

AWS Transform for mainframe now offers test planning and automation features to accelerate mainframe modernization projects. New capabilities include automated test plan generation, test data collection scripts, and test case automation scripts, alongside functional test environment tools for continuous delivery and regression testing, helping accelerate and de-risk testing and validation during mainframe modernization projects.

The new capabilities address key testing challenges across the modernization lifecycle, reducing the time and effort required for mainframe modernization testing, which typically consumes over 50% of project duration. Automated test plan generation helps teams reduce upfront planning efforts and align on critical functional tests needed to mitigate risk and ensure modernization success, while test data collection scripts accelerate the error-prone, complex process of capturing mainframe data. Test automation scripts then enable scalable execution of test cases by automating test environment staging, test case execution, and results validation against expected outcomes.

By automating complex testing tasks and reducing dependency on scarce mainframe expertise, organizations can now modernize their applications with greater confidence while improving accuracy through consistent, automated processes.

The new testing capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions.

To learn more about automated testing in AWS Transform for mainframe, and how it can help your organization accelerate modernization, read the AWS News Blog, visit the AWS Transform for mainframe product page, or explore the AWS Transform User Guide.

 

​AWS Transform for mainframe now offers test planning and automation features to accelerate mainframe modernization projects. New capabilities include automated test plan generation, test data collection scripts, and test case automation scripts, alongside functional test environment tools for continuous delivery and regression testing, helping accelerate and de-risk testing and validation during mainframe modernization projects. The new capabilities address key testing challenges across the modernization lifecycle, reducing the time and effort required for mainframe modernization testing, which typically consumes over 50% of project duration. Automated test plan generation helps teams reduce upfront planning efforts and align on critical functional tests needed to mitigate risk and ensure modernization success, while test data collection scripts accelerate the error-prone, complex process of capturing mainframe data. Test automation scripts then enable scalable execution of test cases by automating test environment staging, test case execution, and results validation against expected outcomes. By automating complex testing tasks and reducing dependency on scarce mainframe expertise, organizations can now modernize their applications with greater confidence while improving accuracy through consistent, automated processes. The new testing capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions. To learn more about automated testing in AWS Transform for mainframe, and how it can help your organization accelerate modernization, read the AWS News Blog, visit the AWS Transform for mainframe product page, or explore the AWS Transform User Guide.  

Amazon Connect now enables businesses to restrict access to specific performance evaluation forms, preventing unauthorized access to evaluation form templates and completed evaluations. Businesses can provide managers access to modify or use only the evaluation form templates that are relevant to their business line or function, improving security and making it easier for managers to select the right form while completing evaluations. Additionally, both managers and agents can be restricted from viewing certain completed evaluations. For example, you can restrict agents from viewing test evaluations filled with a form template that is yet to be finalized.

This feature is available in all regions where Amazon Connect is offered. To learn more, please visit our documentation and our webpage. 

 

​Amazon Connect now enables businesses to restrict access to specific performance evaluation forms, preventing unauthorized access to evaluation form templates and completed evaluations. Businesses can provide managers access to modify or use only the evaluation form templates that are relevant to their business line or function, improving security and making it easier for managers to select the right form while completing evaluations. Additionally, both managers and agents can be restricted from viewing certain completed evaluations. For example, you can restrict agents from viewing test evaluations filled with a form template that is yet to be finalized. This feature is available in all regions where Amazon Connect is offered. To learn more, please visit our documentation and our webpage.