Amazon Inspector now offers expanded agentless EC2 scanning with enhanced detection coverage, including new support for Windows operating system vulnerability scanning without requiring an agent. Security teams and IT administrators can now detect vulnerabilities across a broader range of software and applications on their EC2 instances — including WordPress, Apache HTTP Server, Python packages, and Ruby gems — as well as Windows OS vulnerabilities, all through agentless scanning. Customers automatically receive findings for newly supported software and applications with no configuration changes required.

Amazon Inspector is also introducing Windows Knowledge Base (KB)-based findings for Windows OS vulnerabilities. Rather than receiving a separate finding for each CVE addressed by a single Microsoft patch, customers now receive a single consolidated KB finding that groups all related CVEs together. Each KB finding surfaces the highest CVSS score, EPSS score, and exploit availability from its constituent CVEs, and includes a direct link to the relevant Microsoft KB article — making it straightforward to understand exactly which patch to apply and why. All existing CVE-based Windows OS findings will automatically transition to KB-based findings. All existing CVE-based Windows OS findings will automatically transition to KB-based findings, and customers do not need to take any additional action.

Both capabilities are available in all AWS Regions where Amazon Inspector is available. To learn more, visit the Amazon Inspector product page and the Amazon Inspector documentation. 

 

​Amazon Inspector now offers expanded agentless EC2 scanning with enhanced detection coverage, including new support for Windows operating system vulnerability scanning without requiring an agent. Security teams and IT administrators can now detect vulnerabilities across a broader range of software and applications on their EC2 instances — including WordPress, Apache HTTP Server, Python packages, and Ruby gems — as well as Windows OS vulnerabilities, all through agentless scanning. Customers automatically receive findings for newly supported software and applications with no configuration changes required.
Amazon Inspector is also introducing Windows Knowledge Base (KB)-based findings for Windows OS vulnerabilities. Rather than receiving a separate finding for each CVE addressed by a single Microsoft patch, customers now receive a single consolidated KB finding that groups all related CVEs together. Each KB finding surfaces the highest CVSS score, EPSS score, and exploit availability from its constituent CVEs, and includes a direct link to the relevant Microsoft KB article — making it straightforward to understand exactly which patch to apply and why. All existing CVE-based Windows OS findings will automatically transition to KB-based findings. All existing CVE-based Windows OS findings will automatically transition to KB-based findings, and customers do not need to take any additional action.
Both capabilities are available in all AWS Regions where Amazon Inspector is available. To learn more, visit the Amazon Inspector product page and the Amazon Inspector documentation.   

Amazon Connect now offers agentic speech-to-speech voice experiences in an additional AWS Region: Europe (London). Amazon Connect also adds three new speech-to-speech voices across US Spanish and UK English: Pedro (es-US), Amy (en-GB), and Brian (en-GB).

Amazon Connect’s agentic self-service capabilities enable AI agents to understand, reason, and take action across voice and messaging channels to automate routine and complex customer service tasks. Connect’s agentic speech-to-speech voice AI agents understand not only what customers say but how they say it, adapting voice responses to match customer tone and sentiment while maintaining natural conversational pace. With these updates, you can deliver agentic speech-to-speech voice experiences to customers across a new region with a wider selection of voices.

To learn more about this feature, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale, visit the Amazon Connect website.

 

​Amazon Connect now offers agentic speech-to-speech voice experiences in an additional AWS Region: Europe (London). Amazon Connect also adds three new speech-to-speech voices across US Spanish and UK English: Pedro (es-US), Amy (en-GB), and Brian (en-GB).
Amazon Connect’s agentic self-service capabilities enable AI agents to understand, reason, and take action across voice and messaging channels to automate routine and complex customer service tasks. Connect’s agentic speech-to-speech voice AI agents understand not only what customers say but how they say it, adapting voice responses to match customer tone and sentiment while maintaining natural conversational pace. With these updates, you can deliver agentic speech-to-speech voice experiences to customers across a new region with a wider selection of voices.
To learn more about this feature, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale, visit the Amazon Connect website.  

Amazon Connect now supports 13 new languages for voice AI agents, bringing the total to 40 language locales.  New languages include Arabic (Saudi Arabia), Czech, Danish, Dutch (Belgium), English (Ireland), English (New Zealand), English (Wales), German (Switzerland), Icelandic, Romanian, Spanish (Mexico), Turkish, and Welsh.

Amazon Connect’s agentic self-service capabilities enable AI agents to understand, reason, and take action across voice and digital channels to automate routine and complex customer service tasks across multiple languages.  

To learn more about this feature, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale, visit the Amazon Connect website.

 

​Amazon Connect now supports 13 new languages for voice AI agents, bringing the total to 40 language locales.  New languages include Arabic (Saudi Arabia), Czech, Danish, Dutch (Belgium), English (Ireland), English (New Zealand), English (Wales), German (Switzerland), Icelandic, Romanian, Spanish (Mexico), Turkish, and Welsh.
Amazon Connect’s agentic self-service capabilities enable AI agents to understand, reason, and take action across voice and digital channels to automate routine and complex customer service tasks across multiple languages.  
To learn more about this feature, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale, visit the Amazon Connect website.  

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy.

This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation.

This feature is now available in select AWS Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation.

 

​AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in select AWS Regions. To learn more, visit the S3 Tables documentation and the AWS Glue Data Catalog documentation.  

Amazon SageMaker Unified Studio now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete picture of data transformations and dependencies across your entire lineage graph, helping you quickly identify all upstream sources and downstream consumers of your datasets.

Previously, SageMaker Unified Studio showed the lineage graph as it existed at a specific point in time, which is useful for troubleshooting and investigating specific data processing events. The aggregated view now provides a complete picture of data transformations and dependencies across multiple levels of the lineage graph. You can use this view to understand the full scope of jobs impacting your datasets and to identify all upstream sources and downstream consumers.

The aggregated view is available as the default lineage view in Amazon SageMaker Unified Studio for IdC-based domains. You can switch to the previous view by toggling the «display in event timestamp order» option. You can also query the lineage graph using the new QueryGraph API, which provides lineage node graphs with metadata and augmented business context.

Aggregated view of lineage is available in all existing Amazon SageMaker Unified Studio regions. For detailed information on how to get started with lineage using these new features, refer to the documentation and API.

 

​Amazon SageMaker Unified Studio now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete picture of data transformations and dependencies across your entire lineage graph, helping you quickly identify all upstream sources and downstream consumers of your datasets. Previously, SageMaker Unified Studio showed the lineage graph as it existed at a specific point in time, which is useful for troubleshooting and investigating specific data processing events. The aggregated view now provides a complete picture of data transformations and dependencies across multiple levels of the lineage graph. You can use this view to understand the full scope of jobs impacting your datasets and to identify all upstream sources and downstream consumers. The aggregated view is available as the default lineage view in Amazon SageMaker Unified Studio for IdC-based domains. You can switch to the previous view by toggling the «display in event timestamp order» option. You can also query the lineage graph using the new QueryGraph API, which provides lineage node graphs with metadata and augmented business context. Aggregated view of lineage is available in all existing Amazon SageMaker Unified Studio regions. For detailed information on how to get started with lineage using these new features, refer to the documentation and API.  

AWS Blu Insights capabilities are now available as part of AWS Transform, enabling customers to launch mainframe refactoring projects from the AWS Transform console. This launch unifies all three mainframe modernization patterns — refactor, replatform, and reimagine — within AWS Transform for mainframe. Code transformation is now offered at no cost, replacing the previous lines-of-code based pricing model.

With this launch, you can access AWS Transform for mainframe refactor directly from the AWS Transform console using your existing AWS credentials. The mandatory three-level certification requirement to access the Transformation Center has been removed, lowering the friction to exploring refactor projects. Self-paced training content remains available within the application for those who want to build deeper knowledge.

AWS Transform for mainframe refactor is available in 18 AWS Regions. In regions where AWS Transform for mainframe is not yet available, you can continue to access the service through the AWS Mainframe Modernization console.

To get started, visit the AWS Transform for mainframe refactor user guide.

 

​AWS Blu Insights capabilities are now available as part of AWS Transform, enabling customers to launch mainframe refactoring projects from the AWS Transform console. This launch unifies all three mainframe modernization patterns — refactor, replatform, and reimagine — within AWS Transform for mainframe. Code transformation is now offered at no cost, replacing the previous lines-of-code based pricing model. With this launch, you can access AWS Transform for mainframe refactor directly from the AWS Transform console using your existing AWS credentials. The mandatory three-level certification requirement to access the Transformation Center has been removed, lowering the friction to exploring refactor projects. Self-paced training content remains available within the application for those who want to build deeper knowledge. AWS Transform for mainframe refactor is available in 18 AWS Regions. In regions where AWS Transform for mainframe is not yet available, you can continue to access the service through the AWS Mainframe Modernization console. To get started, visit the AWS Transform for mainframe refactor user guide.  

Amazon Bedrock AgentCore Runtime now supports InvokeAgentRuntimeCommand, a new API that lets you execute shell commands directly inside a running AgentCore Runtime session. Developers can send a command, stream the output in real time over HTTP/2, and receive the exit code — without building custom command execution logic in their containers.

AI agents often operate in workflows where deterministic operations such as running tests, installing dependencies, or executing git commands need to run alongside LLM-powered reasoning. Previously, developers had to build custom logic inside their containers to distinguish agent invocations from shell commands, spawn child processes, capture stdout and stderr, and handle timeouts. InvokeAgentRuntimeCommand eliminates this undifferentiated work by providing a platform-level API for command execution. Commands run inside the same container, filesystem, and environment as the agent session, and can execute concurrently with agent invocations without blocking.

Executing shell commands in AgentCore Runtime is supported across fourteen AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Canada (Central), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm).

To learn more, see Execute shell commands in AgentCore Runtime.

 

​Amazon Bedrock AgentCore Runtime now supports InvokeAgentRuntimeCommand, a new API that lets you execute shell commands directly inside a running AgentCore Runtime session. Developers can send a command, stream the output in real time over HTTP/2, and receive the exit code — without building custom command execution logic in their containers.
AI agents often operate in workflows where deterministic operations such as running tests, installing dependencies, or executing git commands need to run alongside LLM-powered reasoning. Previously, developers had to build custom logic inside their containers to distinguish agent invocations from shell commands, spawn child processes, capture stdout and stderr, and handle timeouts. InvokeAgentRuntimeCommand eliminates this undifferentiated work by providing a platform-level API for command execution. Commands run inside the same container, filesystem, and environment as the agent session, and can execute concurrently with agent invocations without blocking.
Executing shell commands in AgentCore Runtime is supported across fourteen AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Canada (Central), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm).
To learn more, see Execute shell commands in AgentCore Runtime.  

Amazon Corretto 26, a Feature Release (FR) version, is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. You can download Corretto 26 for Linux, Windows, and macOS from our downloads page. Corretto 26 will be supported through October 2026.

• HTTP/3 Support – Java applications can now use the latest HTTP/3 protocol, which is faster and more efficient than older HTTP versions (JEP 517)
• Ahead-of-Time Object Caching – Applications can start up faster by pre-caching commonly used objects, working with any garbage collector (JEP 516)
• Enhanced Pattern Matching – Developers can write cleaner code when checking types and values, now including support for primitive types like int and boolean (JEP 530)
• Making Final Mean Final – Starts warning when code uses reflection to modify final fields, preparing for a future release where final fields will truly be immutable. This change improves both program safety and enables better optimizations like constant folding (JEP 500)
• Structured Concurrency (continued preview) – Introduces API for structured concurrency, treating groups of related tasks running in different threads as single units of work, streamlining error handling and cancellation, improving reliability, and enhancing observability (JEP 525)
• Vector API (continued incubator) – Improved support for high-performance mathematical operations that can take advantage of modern CPU capabilities (JEP 529)
• Lazy Constants (continued preview) – Provides API to defer initialization of immutable data until it is actually needed, combining the performance benefits of final fields with the flexibility of lazy initialization (JEP 526)

A detailed description of these features can be found on the OpenJDK 26 Project page. Amazon Corretto 26 is distributed by Amazon under an open source license.

 

​Amazon Corretto 26, a Feature Release (FR) version, is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. You can download Corretto 26 for Linux, Windows, and macOS from our downloads page. Corretto 26 will be supported through October 2026.

HTTP/3 Support – Java applications can now use the latest HTTP/3 protocol, which is faster and more efficient than older HTTP versions (JEP 517)
Ahead-of-Time Object Caching – Applications can start up faster by pre-caching commonly used objects, working with any garbage collector (JEP 516)
Enhanced Pattern Matching – Developers can write cleaner code when checking types and values, now including support for primitive types like int and boolean (JEP 530)
Making Final Mean Final – Starts warning when code uses reflection to modify final fields, preparing for a future release where final fields will truly be immutable. This change improves both program safety and enables better optimizations like constant folding (JEP 500)
Structured Concurrency (continued preview) – Introduces API for structured concurrency, treating groups of related tasks running in different threads as single units of work, streamlining error handling and cancellation, improving reliability, and enhancing observability (JEP 525)
Vector API (continued incubator) – Improved support for high-performance mathematical operations that can take advantage of modern CPU capabilities (JEP 529)
Lazy Constants (continued preview) – Provides API to defer initialization of immutable data until it is actually needed, combining the performance benefits of final fields with the flexibility of lazy initialization (JEP 526)

A detailed description of these features can be found on the OpenJDK 26 Project page. Amazon Corretto 26 is distributed by Amazon under an open source license.  

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Additional Storage Volumes, Resource Governor, and SQL Server 2019 with SQL Server Developer Edition. SQL Server Developer Edition is an ideal choice to build and test applications because it includes all the functionality of Enterprise edition, and is free of license charges for use as a development and test system, not as production server.

You can use Additional Storage Volumes to your Amazon RDS for SQL Server Developer Edition instances, which provide you up to 256 TiB, 4X more storage. You can also use SQL Server Resource Governor, which lets you manage workload and resource consumption by defining resource pools and workload groups to control CPU and memory usage, enabling more realistic performance testing. Amazon RDS for SQL Server Developer Edition now also supports SQL Server 2019 (CU32 GDR – 15.0.4455.2), so you can match the SQL Server version used in your development and testing environments with what you use for your production environment.

For more information about these features and region availability, see Working with SQL Server Developer Edition on RDS for SQL Server. For pricing details, see Amazon RDS for SQL Server Pricing.

 

​Amazon Relational Database Service (Amazon RDS) for SQL Server now supports Additional Storage Volumes, Resource Governor, and SQL Server 2019 with SQL Server Developer Edition. SQL Server Developer Edition is an ideal choice to build and test applications because it includes all the functionality of Enterprise edition, and is free of license charges for use as a development and test system, not as production server. You can use Additional Storage Volumes to your Amazon RDS for SQL Server Developer Edition instances, which provide you up to 256 TiB, 4X more storage. You can also use SQL Server Resource Governor, which lets you manage workload and resource consumption by defining resource pools and workload groups to control CPU and memory usage, enabling more realistic performance testing. Amazon RDS for SQL Server Developer Edition now also supports SQL Server 2019 (CU32 GDR – 15.0.4455.2), so you can match the SQL Server version used in your development and testing environments with what you use for your production environment. For more information about these features and region availability, see Working with SQL Server Developer Edition on RDS for SQL Server. For pricing details, see Amazon RDS for SQL Server Pricing.  

SageMaker Training Plans allows you to reserve GPU capacity within specified time frames in cluster sizes of up to 64 instances. Today, Amazon SageMaker AI announces that Training Plans can now be extended when your AI workloads take longer than anticipated, ensuring uninterrupted access to capacity. You can extend plans by 1-day increments up to 14 days, or 7-day increments up to 182 days (26 weeks). Extensions can be initiated via API or the SageMaker console. Once the extension is purchased the workload continues to run un-interrupted without you needing to reconfgure the workload.

SageMaker AI helps you create the most cost-efficient training plans that fits within your timeline and AI budget. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the AI workloads on these compute resources without requiring any manual intervention. See the SageMaker AI pricing page for a detailed breakdown of instance availability by AWS Region.

To learn more about training plan extensions, see the Amazon SageMaker Training Plans User Guide

 

​SageMaker Training Plans allows you to reserve GPU capacity within specified time frames in cluster sizes of up to 64 instances. Today, Amazon SageMaker AI announces that Training Plans can now be extended when your AI workloads take longer than anticipated, ensuring uninterrupted access to capacity. You can extend plans by 1-day increments up to 14 days, or 7-day increments up to 182 days (26 weeks). Extensions can be initiated via API or the SageMaker console. Once the extension is purchased the workload continues to run un-interrupted without you needing to reconfgure the workload. SageMaker AI helps you create the most cost-efficient training plans that fits within your timeline and AI budget. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the AI workloads on these compute resources without requiring any manual intervention. See the SageMaker AI pricing page for a detailed breakdown of instance availability by AWS Region. To learn more about training plan extensions, see the Amazon SageMaker Training Plans User Guide