11/25/2025, 12:00:00 AM ~ 11/26/2025, 12:00:00 AM (UTC)

Recent Announcements

Manage Amazon SageMaker HyperPod clusters with the new Amazon SageMaker AI MCP Server

The Amazon SageMaker AI MCP Server now supports tools that help you setup and manage HyperPod clusters. Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building generative AI models by quickly scaling model development tasks such as training, fine-tuning, or deployment across a cluster of AI accelerators. The SageMaker AI MCP Server now empowers AI coding assistants to provision and operate AI/ML clusters for model training and deployment.\n MCP servers in AWS provide a standard interface to enhance AI-assisted application development by equipping AI code assistants with real-time, contextual understanding of various AWS services. The SageMaker AI MCP server comes with tools that streamline end-to-end AI/ML cluster operations using the AI assistant of your choice—from initial setup through ongoing management. It enables AI agents to reliably setup HyperPod clusters orchestrated by Amazon EKS or Slurm complete with pre-requisites, powered by CloudFormation templates that optimize networking, storage, and compute resources. Clusters created via this MCP server are fully optimized for high-performance distributed training and inference workloads, leveraging best practice architectures to maximize throughput and minimize latency at scale. Additionally, it provides comprehensive tools for cluster and node management—including scaling operations, applying software patches, and performing various maintenance tasks. When used in conjunction with AWS API MCP Server, AWS Knowledge MCP Server, and Amazon EKS MCP Server you gain complete coverage for all SageMaker HyperPod APIs and you can effectively troubleshoot common issues, such as diagnosing why a cluster node became inaccessible. For cluster administrators, these tools streamline daily operations. For data scientists, they enable you to set up AI/ML clusters at scale without requiring infrastructure expertise, allowing you to focus on what matters most—training and deploying models. You can manage your AI/ML clusters through the SageMaker AI MCP server in all regions where SageMaker HyperPod is available. To get started, visit the AWS MCP Servers documentation.

AWS Lambda adds support for Node.js 24

AWS Lambda now supports creating serverless applications using Node.js 24. Developers can use Node.js 24 as both a managed runtime and a container base image, and AWS will automatically apply updates to the managed runtime and base image as they become available.\n Node.js 24 is the latest long-term support release of Node.js and is expected to be supported for security and bug fixes until April 2028. With this release, Lambda has simplified the developer experience, focusing on the modern async/await programming pattern and no longer supports callback-based function handlers. You can use Node.js 24 with Lambda@Edge (in supported Regions), allowing you to customize low-latency content delivered through Amazon CloudFront. Powertools for AWS Lambda (TypeScript), a developer toolkit to implement serverless best practices and increase developer velocity, also supports Node.js 24. You can use the full range of AWS deployment tools, including the Lambda console, AWS CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and AWS CloudFormation to deploy and manage serverless applications written in Node.js 24. The Node.js 24 runtime is available in all Regions, including the AWS GovCloud (US) Regions and China Regions. For more information, including guidance on upgrading existing Lambda functions, see our blog post. For more information about AWS Lambda, visit our product page.

Amazon OpenSearch Service introduces Agentic Search

Amazon OpenSearch Service launches Agentic Search, transforming how users interact with their data through intelligent, agent-driven search. Agentic Search introduces an intelligent agent-driven system that understands user intent, orchestrates the right set of tools, generates OpenSearch DSL (domain-specific language) queries, and provides transparent summaries of its decision-making process through a simple ‘agentic’ query clause and natural language search terms.\n Agentic Search automates OpenSearch query planning and execution, eliminating the need for complex search syntax. Users can ask questions in natural language like “Find red cars under $30,000” or “Show last quarter’s sales trends.” The agent interprets intent, applies optimal search strategies, and delivers results while explaining its reasoning process. The feature provides two agent types: conversational agents, which handle complex interactions with the ability to store conversations in memory, and flow agents for efficient query processing. The built-in QueryPlanningTool uses large language models (LLMs) to create DSL queries, making search accessible regardless of technical expertise. Users can manage Agentic Search through APIs or OpenSearch Dashboards to configure and modify agents. Agentic Search’s advanced settings allow you to connect with external MCP servers and use custom search templates. Support for agentic search is available for OpenSearch Service version 3.3 and later in all AWS Commercial and AWS GovCloud (US) Regions where OpenSearch Service is available. See here for a full listing of our Regions. Build agents and run agentic searches using the new Agentic Search use case available in the AI Search Flows plugin. To learn more about Agentic Search, visit the OpenSearch technical documentation.

AWS Service Quotas adds now support for automatic quota management

Today, we’re excited to announce the general availability of new capability of automatic quota management feature in AWS Service Quotas. Today, automatic quota management supports customers to receive notifications when their quota usage approaches their allocated quotas and configure their preferred notifications channel, such as email, SMS, or Slack, through Service Quotas console or API. Now, this feature adjusts values of AWS services’ quotas automatically and safely based on customer’s usage, which reduces operational burden from customers to constantly monitor their quota usage, and request quota increases across multiple AWS services in different AWS accounts and Regions. Customers can now confidently scale their applications on AWS to meet their growing customer demand without the risk of unexpected service interruptions due to quota exhaustion.\n This new capability is now available at no additional cost in all AWS commercial regions. To explore this feature and for details, please visit Service Quotas console and AWS Service Quotas documentation.

Amazon SageMaker AI Inference now supports bidirectional streaming

Amazon SageMaker AI Inference now supports bidirectional streaming for real-time speech-to-text transcription, enabling continuous speech processing instead of batch input. Models can now receive audio streams and return partial transcripts simultaneously as users speak, enabling you to build voice agents that process speech with minimal latency.\n As customers build AI voice agents, they need real-time speech transcription to minimize delays between user speech and agent responses. Data scientists and ML engineers lack managed infrastructure for bidirectional streaming, making it necessary to build custom WebSocket implementations and manage streaming protocols. Teams spend weeks developing and maintaining this infrastructure rather than focusing on model accuracy and agent capabilities. With bidirectional streaming on Amazon SageMaker AI Inference, you can deploy speech-to-text models by invoking your endpoint with the new Bidirectional Stream API. The client opens an HTTP2 connection to the SageMaker AI runtime, and SageMaker AI automatically creates a WebSocket connection to your container. This can process streaming audio frames and return partial transcripts as they are produced. Any container implementing a WebSocket handler following the SageMaker AI contract works automatically, with real-time speech models such as Deepgram running without modifications. This eliminates months of infrastructure development, enabling you to deploy voice agents with continuous transcription while focusing your time on improving model performance. Bidirectional streaming is available in following AWS Regions - Canada (Central), South America (São Paulo), Africa (Cape Town), Europe (Paris), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Israel (Tel Aviv), Europe (Zurich), Asia Pacific (Tokyo), AWS GovCloud US (West), AWS GovCloud US (East), Asia Pacific (Mumbai), Middle East (Bahrain), US West (Oregon), China (Ningxia), US West (Northern California), Asia Pacific (Sydney), Europe (London), Asia Pacific (Seoul), US East (N. Virginia), Asia Pacific (Hong Kong), US East (Ohio), China (Beijing), Europe (Stockholm), Europe (Ireland), Middle East (UAE), Asia Pacific (Osaka), Asia Pacific (Melbourne), Europe (Spain), Europe (Frankfurt), Europe (Milan), Asia Pacific (Singapore). To learn more, visit AWS News Blog here and SageMaker AI documentation here.

AWS Glue Data Quality now supports rule labeling for enhanced reporting

Today, AWS announces the general availability of rule label, a feature of AWS Glue Data Quality, enabling you to apply custom key-value pair labels to your data quality rules for improved organization, filtering, and targeted reporting. This enhancement allows you to categorize data quality rules by business context, team ownership, compliance requirements, or any custom taxonomy that fits your data quality and governance needs.\n Rule labels provide effective way to organize analyze data quality results. You can query results by specific labels to identify failing rules within particular categories, count rule outcomes by team or domain, and create focused reports for different stakeholders. For example, you can apply all rules that pertain to finance team with a label “team=finance” and generate a customized report to showcase quality metrics specific to finance team. You can label high priority rules with “criticality=high” to prioritize remediation efforts. Labels can be authored as part of the DQDL. You can query the labels as part of rule outcomes, row-level results, and API responses, making it easy to integrate with your existing monitoring and reporting workflows. AWS Glue Data Quality rule labeling is available in all commercial AWS Regions where AWS Glue Data Quality is available. See the AWS Region Table for more details. To learn more about rule labeling, see the AWS Glue Data Quality documentation.

AWS Glue Data Quality now supports pre-processing queries

Today, AWS announces the general availability of preprocessing queries for AWS Glue Data Quality, enabling you to transform your data before running data quality checks through AWS Glue Data Catalog APIs. This feature allows you to create derived columns, filter data based on specific conditions, perform calculations, and validate relationships between\n columns directly within your data quality evaluation process.

Preprocessing queries provide enhanced flexibility for complex data quality scenarios that require data transformation before validation. You can create derived metrics like calculating total fees from tax and shipping columns, limiting number of columns that are considered for data quality recommendations or filter datasets to focus quality checks on specific data subsets. This capability eliminates the need for separate data pre-processing steps, streamlining your data quality workflows.

AWS Glue Data Quality preprocessing queries are available through AWS Glue Data Catalog APIs - start-data-quality-rule-recommendation-run and start-data-quality-ruleset-evaluation-run, in all commercial AWS Regions where AWS Glue Data Quality is available. To learn more about preprocessing queries, see the Glue Data Quality documentation.

Amazon Quick Suite introduces scheduling for Quick Flows

Amazon Quick Flows now supports scheduling, enabling you to automate repetitive workflows without requiring manual intervention. You can now configure Quick Flows to run automatically at specified times or intervals, improving operational efficiency and ensuring critical tasks execute consistently.\n You can schedule Quick Flows to run daily, weekly, monthly, or on custom intervals. This capability is great for automating routine and administrative tasks such as generating recurring reports from dashboards, summarizing open items assigned to you in external services, or generating daily meeting briefings before you head out to work. You can schedule any flow you have access to—whether you created it or it was shared with you. To schedule a flow, click the scheduling icon and configure your desired date, time, and frequency. Scheduling in Quick Flows is available now in IAD, PDX, and DUB. There are no additional charges for using scheduled execution beyond standard Quick Flows usage. To learn more about configuring scheduled Quick Flows, please visit our documentation.

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