Kategorie: Amazon AWS

Amazon Quick agentic AI capabilities are now available in AWS GovCloud (US-West)

Today, AWS announces that Amazon Quick’s agentic AI capabilities are now available in AWS GovCloud (US-West), bringing an agentic AI teammate to government and regulated-industry teams within an isolated, FedRAMP Class D (formerly High) authorized environment. Building on the analytics and business intelligence capabilities already available to customers in AWS GovCloud (US), Quick now turns questions into actions, helping teams drive mission-critical decisions faster without switching applications.
With this launch, teams can build custom chat agents tailored to mission-specific workflows — including procurement, ATO compliance, and grants management — while keeping data hosted and processed entirely within the AWS GovCloud (US-West) Region. Spaces enforce least-privilege access by scoping information to the appropriate program office or mission area, ensuring analysts only access mission-relevant data. Quick also integrates with tools teams already rely on, including Microsoft 365, SharePoint, and OneDrive via GCC High connectors, as well as browser extensions.
AWS GovCloud (US) Regions are isolated AWS Regions operated by U.S. citizens on U.S. soil, purpose-built to host sensitive data and regulated workloads. Customers can address the most stringent U.S. government security and compliance requirements, including the FedRAMP Class D (formerly High) baseline, Department of Defense Cloud Computing Security Requirements Guide (DoD SRG) Impact Levels 4 and 5, International Traffic in Arms Regulations (ITAR), Criminal Justice Information Services (CJIS), and Federal Information Processing Standard (FIPS) 140-3. Inference on authorized foundation models is processed within the AWS GovCloud (US-West) Region, and enterprise governance features are available at launch.
With this launch, Amazon Quick’s agentic AI capabilities are available in 8 AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Sydney, Tokyo), and AWS GovCloud (US-West). 
To learn more, visit the Amazon Quick product page and AWS GovCloud (US) documentation
Quelle: aws.amazon.com

Amazon Connect Customer supports manual assignment of queued agent-first callbacks

Amazon Connect Customer now lets agents view and self-assign queued agent-first callbacks alongside emails, tasks, and chats. This gives agents the option to prioritize work that needs immediate follow-up or for which they already have relevant context. For example, an agent familiar with a customer’s issue can assign the callback to themselves, avoiding another handoff and helping resolve the issue faster.
This feature is available in all AWS Regions where Amazon Connect Customer is available. To learn more about how to use queued callbacks, refer to our documentation Access the Worklist app and Set up queued callbacks. For more information about Amazon Connect Customer, visit our product page.
Quelle: aws.amazon.com

Amazon EKS now supports advanced Kubernetes control plane configuration parameters

Amazon Elastic Kubernetes Service (Amazon EKS) now supports configuring parameters for Kubernetes control plane components including the scheduler, controller manager, and API server. You can tune pod placement strategies to improve resource utilization, adjust how quickly horizontal pod autoscaling responds to changes in demand, set resource lifecycle parameters such as event retention duration, and more. Cluster administrators now have more control over Kubernetes control plane parameters beyond the defaults. For example, you can set the scheduler’s node resource fit strategy parameter to MostAllocated, which packs pods onto nodes that are already well utilized and helps you run the same workloads on fewer nodes. The default LeastAllocated strategy spreads pods across nodes, and you can keep it where headroom matters more than density. You can configure Kubernetes control plane parameters in any AWS Region where Amazon EKS is available. For the full list of configurable parameters and to learn more, see Control plane configuration in the Amazon EKS User Guide.
Quelle: aws.amazon.com

Amazon Bedrock expands IAM principal cost allocation to the bedrock-mantle endpoint

Amazon Bedrock is a fully managed service that provides secure, enterprise-grade access to high-performing foundation models from leading AI companies, enabling you to build and scale generative AI applications. Amazon Bedrock now supports cost allocation by AWS Identity and Access Management (IAM) principal, including IAM users and roles, for model inference requests made through the bedrock-mantle endpoint. This extends the capability previously available for the bedrock-runtime endpoint, helping customers attribute inference costs across users, teams, projects, and applications.
Customers can tag IAM users and roles with attributes such as team, project, or cost center, activate them as cost allocation tags, and analyze bedrock-mantle inference costs by those tags in AWS Cost Explorer or at the line-item level in AWS Cost and Usage Report 2.0 (CUR 2.0). To get started, activate your IAM principal tags in the AWS Billing and Cost Management console. Then filter or group costs by those tags in Cost Explorer, or create a CUR 2.0 data export and select Include caller identity (IAM principal) allocation data.
This feature is available in all AWS Regions where the bedrock-mantle endpoint is available. To learn more, see Using IAM principal for cost allocation and IAM principal attribution in Amazon Bedrock.
Quelle: aws.amazon.com

Amazon EC2 R8a instances are now available in Canada (Central) region

Starting today, Amazon EC2 R8a instances are now available in Canada (Central) Region. These instances, feature 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to R7a instances.
R8a instances deliver 45% more memory bandwidth compared to R7a instances, making these instances ideal for latency sensitive workloads. Compared to Amazon EC2 R7a instances, R8a instances provide up to 60% faster performance for GroovyJVM, allowing higher request throughput and better response times for business-critical applications.
Built on the AWS Nitro System using sixth generation Nitro Cards, R8a instances are ideal for high performance, memory-intensive workloads, such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and Electronic Design Automation (EDA) applications. R8a instances offer 12 sizes including 2 bare metal sizes. Amazon EC2 R8a instances are SAP-certified, and providing 38% more SAPS compared to R7a instances.
To get started, sign in to the AWS Management Console. For more information about the new instances, visit the Amazon EC2 R8a instance page. 
Quelle: aws.amazon.com

Amazon Connect Customer launches performance dashboard for Cases

Amazon Connect Customer now provides a performance dashboard for cases that helps managers monitor case volume, resolution trends, and performance against service level agreement (SLA) targets. Managers can compare current and prior-period performance across metrics such as cases created, average resolution time, first-contact resolution percentage, and SLA achievement rate. They can also analyze trends across dimensions such as case template, assigned user, or assigned queue. For example, a manager can identify that the billing team missed more SLA targets for refund cases than in the prior period, investigate the causes, and prioritize process improvements.
Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Cases webpage and documentation.
Quelle: aws.amazon.com

AWS Glue adds one-click access to SageMaker Unified Studio from the AWS console

AWS Glue now provides direct access to Amazon SageMaker Unified Studio, helping data engineers and analysts move from viewing the catalog in the Glue console to querying their data, running data quality checks, and building data pipelines in SageMaker Unified Studio with a single click. This new integration helps customers who already work in the Glue console transition and access a broader set of data and AI capabilities in SageMaker Unified Studio seamlessly. With this launch, SageMaker Unified Studio can now be accessed by a single click from S3 Tables, Athena, EMR, Redshift and Glue consoles.
When working in the AWS Glue console to browse catalog tables or build ETL jobs, you now have one-click access to open SageMaker Unified Studio, and can immediately begin working with your data in the catalog or query your data using SageMaker Notebooks using the same IAM role. For Glue console customers who have not yet set up SageMaker Unified Studio, a new inline permissions panel helps you create and configure the required IAM policies directly within the setup workflow, without navigating to the IAM console and switching browser tabs. You can use your existing IAM role and customize the permissions in-context, reducing the steps required to get started.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is supported. To get started, navigate to the AWS Glue console.
Quelle: aws.amazon.com

AWS Secrets Manager adds managed external secrets support for Jenkins and SonarQube

AWS Secrets Manager now extends its managed external secrets capability to include Jenkins API Tokens and SonarQube Tokens, enabling you to automatically rotate these third-party credentials directly from the AWS console without writing any custom rotation code.
For Jenkins, Secrets Manager mints a new token and revokes the old one only after the replacement is verified active, so your continuous integration and continuous delivery (CI/CD) jobs transition without interruption. Rotation supports both self-rotation, where the token being rotated authenticates its own replacement, and admin-assisted rotation, where a separate admin token performs the generate and revoke operations. For SonarQube, you can rotate three types of tokens — User Tokens, Global Analysis Tokens, and Project Analysis Tokens — via SonarQube’s Web API. User Tokens support self-rotation, while analysis tokens are rotated using an admin token.
These integrations join existing managed external secrets support for BigID, Confluent Cloud, Datadog, GitLab, MongoDB Atlas, Okta, Paddle, Salesforce, and Snowflake.
Jenkins and SonarQube managed external secrets are available in all AWS Regions where AWS Secrets Manager managed external secrets is supported. To learn more, visit the  AWS Secrets Manager managed external secrets documentation .
Quelle: aws.amazon.com

NVIDIA Nemotron 3.5 Lightning model is now available on Amazon SageMaker JumpStart

NVIDIA’s Nemotron 3.5 Lightning is now available on Amazon SageMaker JumpStart, giving AWS customers access to the fastest open model in its class for persistent agent workloads and rapid task execution.
Nemotron 3.5 Lightning is engineered for persistent agents and high-throughput enterprise automation across domains including personal assistants, financial document processing, cybersecurity triage, and telecom operations. Built on a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters and just 3B active per forward pass, it achieves up to 4x the throughput (~410 tokens/sec) and 30% faster task completion over comparable models. Distilled from Nemotron 3 Ultra, it handles up to 1M tokens of context via DFlash speculative decoding and integrates directly with popular agent harnesses. The model is fully open-trained on open datasets thereby allowing enterprises to post-train for their own tools, workflows, and policies, and deploy with complete ownership across edge, on-premises, or cloud infrastructure.
With SageMaker JumpStart, customers can deploy this model in a few clicks to power their specific AI workloads.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B models now available on Amazon SageMaker JumpStart

NVIDIA’s LocateAnything-3B, Qwen’s Qwen-AgentWorld-35B-A3B, and Qwen’s Qwen3.5-122B-A10B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning visual grounding, agent environment simulation, and large-scale multimodal reasoning, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
LocateAnything-3B is optimized for fast, high-quality visual grounding and object localization from natural language instructions. It uses a Parallel Box Decoding (PBD) framework that decodes bounding boxes and points as atomic units in a single step, preserving geometric coherence and unlocking substantial parallelism. It enables precise object localization, dense detection, and point-based localization across diverse domains in both Enterprise Intelligence and Physical AI applications.
Qwen-AgentWorld-35B-A3B excels in simulating agent environments across seven interaction domains: tool calling, search, terminal, software engineering, Android, web, and OS interaction. It is the first language world model to cover all seven domains within a single model, predicting next environment states given an agent’s action and interaction history via long chain-of-thought reasoning—trained on over 10 million real-world interaction trajectories.
Qwen3.5-122B-A10B provides high-performance multimodal reasoning with production-friendly efficiency. It features 122B total parameters with only 10B activated per token through a hybrid architecture integrating Gated Delta Networks with sparse Mixture-of-Experts (256 experts), delivering strong reasoning, coding, agents, and visual understanding performance with a native 262K context window and minimal latency overhead.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
 
Quelle: aws.amazon.com