AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously.

The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024.

AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.

 

​AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously.
The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024.
AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.  

You can now connect AI agents directly to the AWS MCP Server using AWS Sign-In. Agents connect using industry-standard OAuth without requiring additional authentication software. Existing AWS identities, sign-in methods, IAM permissions, and governance controls you have already set up continue to apply.

Developers can authorize agents interactively through a browser or programmatically using non-interactive (headless) authorization. Administrators can govern OAuth access using familiar IAM policies together with new OAuth capabilities, including global condition keys, token introspection and revocation APIs, dynamic client registration, and CloudTrail audit events.

To learn more, see the OAuth Support for the AWS MCP Server blogpost, Sign-In with OAuth 2.0 in the AWS Sign-In User Guide, and Setting up the AWS MCP Server in the Agent Toolkit for AWS User Guide.

 

​You can now connect AI agents directly to the AWS MCP Server using AWS Sign-In. Agents connect using industry-standard OAuth without requiring additional authentication software. Existing AWS identities, sign-in methods, IAM permissions, and governance controls you have already set up continue to apply.
Developers can authorize agents interactively through a browser or programmatically using non-interactive (headless) authorization. Administrators can govern OAuth access using familiar IAM policies together with new OAuth capabilities, including global condition keys, token introspection and revocation APIs, dynamic client registration, and CloudTrail audit events.
To learn more, see the OAuth Support for the AWS MCP Server blogpost, Sign-In with OAuth 2.0 in the AWS Sign-In User Guide, and Setting up the AWS MCP Server in the Agent Toolkit for AWS User Guide.  

Amazon SageMaker HyperPod now supports deep health checks for Slurm-orchestrated clusters created with continuous provisioning, enabling you to proactively verify GPU accelerator health on running instances at any time. Continuous provisioning lets you start training quickly and scale instance groups asynchronously without all-or-nothing failures, and you can now pair that flexibility with comprehensive hardware validation as instances come online. This capability addresses a critical challenge where even a single unhealthy node can waste hours of compute time and delay critical workloads.

With deep health checks, you can target entire instance groups or specific instances to run comprehensive hardware stress tests and connectivity tests before committing compute resources to a job. Because continuous provisioning adds worker nodes to your Slurm cluster asynchronously as capacity becomes available, you can run deep health checks on each new node as it comes online, validating hardware before scheduling jobs on it and without interrupting workloads already running on healthy nodes. Progress and results are visible at both the instance group and instance level through the SageMaker console and APIs, providing complete visibility into GPU health, network connectivity, and multi-node communication performance. Instances undergoing checks are automatically isolated from workload scheduling and returned to service upon passing. When paired with HyperPod’s automatic node recovery capability, instances that fail are automatically rebooted or replaced, ensuring cluster health.

This capability is available in all regions where Amazon SageMaker HyperPod is available. To learn more about on-demand deep health checks and continuous provisioning, see the Amazon SageMaker HyperPod User Guide.

 

​Amazon SageMaker HyperPod now supports deep health checks for Slurm-orchestrated clusters created with continuous provisioning, enabling you to proactively verify GPU accelerator health on running instances at any time. Continuous provisioning lets you start training quickly and scale instance groups asynchronously without all-or-nothing failures, and you can now pair that flexibility with comprehensive hardware validation as instances come online. This capability addresses a critical challenge where even a single unhealthy node can waste hours of compute time and delay critical workloads. With deep health checks, you can target entire instance groups or specific instances to run comprehensive hardware stress tests and connectivity tests before committing compute resources to a job. Because continuous provisioning adds worker nodes to your Slurm cluster asynchronously as capacity becomes available, you can run deep health checks on each new node as it comes online, validating hardware before scheduling jobs on it and without interrupting workloads already running on healthy nodes. Progress and results are visible at both the instance group and instance level through the SageMaker console and APIs, providing complete visibility into GPU health, network connectivity, and multi-node communication performance. Instances undergoing checks are automatically isolated from workload scheduling and returned to service upon passing. When paired with HyperPod’s automatic node recovery capability, instances that fail are automatically rebooted or replaced, ensuring cluster health. This capability is available in all regions where Amazon SageMaker HyperPod is available. To learn more about on-demand deep health checks and continuous provisioning, see the Amazon SageMaker HyperPod User Guide.  

Amazon Timestream for InfluxDB now publishes events to Amazon EventBridge when your database instances or clusters undergo state changes. Events are emitted for lifecycle operations including creation, deletion, compute and storage scaling, parameter group updates, maintenance windows, and reboot — covering both successful completions and failures.

With this capability, customers can use Amazon EventBridge rules to programmatically react to database operations without polling the API for status. DevOps teams can build automation workflows that trigger when a scaling operation completes, operations teams can route failure events for immediate alerting, and compliance teams can persist all events to Amazon CloudWatch Logs or Amazon S3 for audit trails. Events are published to the default Amazon EventBridge event bus in your account with source aws.timestream-influxdb, supporting content-based filtering and routing to any EventBridge target including AWS Lambda functions, AWS Step Functions, Amazon SQS queues, Amazon SNS topics, and cross-account event buses.

This capability is available in all AWS Regions where Amazon Timestream for InfluxDB is available. Standard Amazon EventBridge pricing applies for rule evaluation and target delivery. To get started, open the Amazon EventBridge console and create a rule with source aws.timestream-influxdb. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.

 

​Amazon Timestream for InfluxDB now publishes events to Amazon EventBridge when your database instances or clusters undergo state changes. Events are emitted for lifecycle operations including creation, deletion, compute and storage scaling, parameter group updates, maintenance windows, and reboot — covering both successful completions and failures. With this capability, customers can use Amazon EventBridge rules to programmatically react to database operations without polling the API for status. DevOps teams can build automation workflows that trigger when a scaling operation completes, operations teams can route failure events for immediate alerting, and compliance teams can persist all events to Amazon CloudWatch Logs or Amazon S3 for audit trails. Events are published to the default Amazon EventBridge event bus in your account with source aws.timestream-influxdb, supporting content-based filtering and routing to any EventBridge target including AWS Lambda functions, AWS Step Functions, Amazon SQS queues, Amazon SNS topics, and cross-account event buses. This capability is available in all AWS Regions where Amazon Timestream for InfluxDB is available. Standard Amazon EventBridge pricing applies for rule evaluation and target delivery. To get started, open the Amazon EventBridge console and create a rule with source aws.timestream-influxdb. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.  

Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports new capabilities for high-throughput feature ingestion, record discovery, and offline store cataloging. Data scientists can now write multiple records across multiple feature groups in a single request with BatchWriteRecord, list the records stored in a feature group without knowing each record identifier in advance with ListRecords, and create tables and databases with custom names in the offline store.

Data scientists can use BatchWriteRecord to ingest feature data at scale with fewer API calls and lower latency than writing one record at a time. BatchWriteRecord targets the online store, the offline store, or both, returns individual record failures without failing the entire request, and supports time-to-live settings at the record, request, and feature group level. With ListRecords, data scientists can retrieve the record identifiers in a feature group, one page at a time, to browse and audit feature group contents, recover record identifiers, and manage the record lifecycle. When configuring an offline store, data scientists can also create Glue and Iceberg tables with custom names. These capabilities enable data scientists to ingest features at scale and manage the records stored in SageMaker Feature Store without building custom tooling.

These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. For more information, see Amazon Feature Store Runtime and Offline Store Configuration documentation.

 

​Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports new capabilities for high-throughput feature ingestion, record discovery, and offline store cataloging. Data scientists can now write multiple records across multiple feature groups in a single request with BatchWriteRecord, list the records stored in a feature group without knowing each record identifier in advance with ListRecords, and create tables and databases with custom names in the offline store. Data scientists can use BatchWriteRecord to ingest feature data at scale with fewer API calls and lower latency than writing one record at a time. BatchWriteRecord targets the online store, the offline store, or both, returns individual record failures without failing the entire request, and supports time-to-live settings at the record, request, and feature group level. With ListRecords, data scientists can retrieve the record identifiers in a feature group, one page at a time, to browse and audit feature group contents, recover record identifiers, and manage the record lifecycle. When configuring an offline store, data scientists can also create Glue and Iceberg tables with custom names. These capabilities enable data scientists to ingest features at scale and manage the records stored in SageMaker Feature Store without building custom tooling.
These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. For more information, see Amazon Feature Store Runtime and Offline Store Configuration documentation.  

Amazon MSK Replicator now supports data replication from external Apache Kafka clusters – including on-premises, self-managed on AWS, or other cloud providers to Amazon MSK Standard brokers. This capability extends replication support to MSK Standard brokers, in addition to the existing support for MSK Express brokers. With this launch, you can migrate workloads to MSK Standard brokers, support disaster recovery by using MSK clusters as a failover or backup target, and enable data distribution across hybrid and multi-cloud environments.

MSK Replicator is a feature of Amazon MSK that automates data replication between Kafka clusters, eliminating the need to manage custom replication infrastructure or configure open-source tools. Previously, MSK Replicator supported replication from external Apache Kafka clusters to MSK Express brokers only. With this launch, you can now also replicate data from external Kafka clusters to MSK Standard brokers, using either SASL/SCRAM or mutual TLS (mTLS) authentication to connect to your external clusters. You can also use MSK Replicator to replicate data from Amazon MSK Standard to external Kafka clusters for reliable failback or multi-cloud data distribution. Unlike self-managed replication tools, MSK Replicator lets you retain your original Kafka topic names during replication while automatically avoiding infinite replication loops. It also synchronizes consumer group offsets bidirectionally, enabling you to move producers and consumers across clusters independently, in any order, without coordination constraints or the risk of data loss.

This new capability is supported in all AWS Regions where Amazon MSK Replicator is available. Visit the MSK Replicator documentation, product page, pricing page, and this AWS blog post to learn more.

 

​Amazon MSK Replicator now supports data replication from external Apache Kafka clusters – including on-premises, self-managed on AWS, or other cloud providers to Amazon MSK Standard brokers. This capability extends replication support to MSK Standard brokers, in addition to the existing support for MSK Express brokers. With this launch, you can migrate workloads to MSK Standard brokers, support disaster recovery by using MSK clusters as a failover or backup target, and enable data distribution across hybrid and multi-cloud environments. MSK Replicator is a feature of Amazon MSK that automates data replication between Kafka clusters, eliminating the need to manage custom replication infrastructure or configure open-source tools. Previously, MSK Replicator supported replication from external Apache Kafka clusters to MSK Express brokers only. With this launch, you can now also replicate data from external Kafka clusters to MSK Standard brokers, using either SASL/SCRAM or mutual TLS (mTLS) authentication to connect to your external clusters. You can also use MSK Replicator to replicate data from Amazon MSK Standard to external Kafka clusters for reliable failback or multi-cloud data distribution. Unlike self-managed replication tools, MSK Replicator lets you retain your original Kafka topic names during replication while automatically avoiding infinite replication loops. It also synchronizes consumer group offsets bidirectionally, enabling you to move producers and consumers across clusters independently, in any order, without coordination constraints or the risk of data loss. This new capability is supported in all AWS Regions where Amazon MSK Replicator is available. Visit the MSK Replicator documentation, product page, pricing page, and this AWS blog post to learn more.  

Amazon SageMaker Unified Studio now supports custom asset types for IAM-based domains. With custom asset types, domain administrators can catalog any format of asset within the SageMaker Unified Studio, such as medical imaging files in Amazon S3, revenue dashboards built in PowerBI, or PDF research reports generated by a third-party platform. Custom asset types bring all assets, regardless of their underlying format, into the SageMaker catalog so teams can search, discover, and subscribe to them without needing separate tools or processes.

To get started, an administrator can create a custom asset type with a name, description, and optional metadata forms that define the fields each asset should carry. Individual assets can then be created from that type, enriched with glossary terms and README documentation to add business context for humans and AI agents, and published for discovery. Once published, anyone in the domain can find the asset by name, type, or glossary term and request a subscription through the same governed workflow used for all other catalog assets.

Custom asset types for IAM-based domains are available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the SageMaker Unified Studio user guide. 

 

​Amazon SageMaker Unified Studio now supports custom asset types for IAM-based domains. With custom asset types, domain administrators can catalog any format of asset within the SageMaker Unified Studio, such as medical imaging files in Amazon S3, revenue dashboards built in PowerBI, or PDF research reports generated by a third-party platform. Custom asset types bring all assets, regardless of their underlying format, into the SageMaker catalog so teams can search, discover, and subscribe to them without needing separate tools or processes.
To get started, an administrator can create a custom asset type with a name, description, and optional metadata forms that define the fields each asset should carry. Individual assets can then be created from that type, enriched with glossary terms and README documentation to add business context for humans and AI agents, and published for discovery. Once published, anyone in the domain can find the asset by name, type, or glossary term and request a subscription through the same governed workflow used for all other catalog assets.
Custom asset types for IAM-based domains are available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the SageMaker Unified Studio user guide.   

AWS Client VPN is now available in four new regions: Canada West (Calgary), Mexico (Central) and two in Asia Pacific – New Zealand and Taipei. This fully managed service enables customers to securely connect their remote workforce to resources in AWS or on-premises networks.

AWS Client VPN eliminates the need for hardware VPN appliances and complex operational management through its pay-as-you-go model. Organizations can easily manage and monitor VPN connections through a single console.
To learn more about Client VPN:

• Visit the AWS Client VPN product page.
• Read the AWS Client VPN documentation.
• AWS Client VPN pricing page.

 

​AWS Client VPN is now available in four new regions: Canada West (Calgary), Mexico (Central) and two in Asia Pacific – New Zealand and Taipei. This fully managed service enables customers to securely connect their remote workforce to resources in AWS or on-premises networks.
AWS Client VPN eliminates the need for hardware VPN appliances and complex operational management through its pay-as-you-go model. Organizations can easily manage and monitor VPN connections through a single console. To learn more about Client VPN:

Visit the AWS Client VPN product page.
Read the AWS Client VPN documentation.
AWS Client VPN pricing page.  

Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code.
With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the AWS Region table.
To learn more, see Supported operators for Amazon MWAA Serverless workflows. To get started, see Serverless visual workflows in the Amazon SageMaker Unified Studio User Guide.

 

​Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code. With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the AWS Region table. To learn more, see Supported operators for Amazon MWAA Serverless workflows. To get started, see Serverless visual workflows in the Amazon SageMaker Unified Studio User Guide.  

AWS Builder Center now lets builders request free, time-limited sandbox environments directly from eligible workshops, eliminating the need for a personal AWS account, credit card, or concerns about unexpected charges. Previously, workshops could only be completed using a builder’s own AWS account. With sandbox environments, builders of all skill levels can safely deploy resources, write code, and experiment in a pre-provisioned AWS account.

Free sandbox environments are ideal for builders who want to gain practical AWS experience without setting up an account. Each sandbox provides 8 hours of access from activation, with automatic cleanup afterward. Builders can request one sandbox per week (resetting every Sunday), and most environments are ready within 15 minutes. Sandboxes are available for select workshops at launch, with more being enabled over time.

To get started, visit Workshops on AWS Builder Center.

 

​AWS Builder Center now lets builders request free, time-limited sandbox environments directly from eligible workshops, eliminating the need for a personal AWS account, credit card, or concerns about unexpected charges. Previously, workshops could only be completed using a builder’s own AWS account. With sandbox environments, builders of all skill levels can safely deploy resources, write code, and experiment in a pre-provisioned AWS account.
Free sandbox environments are ideal for builders who want to gain practical AWS experience without setting up an account. Each sandbox provides 8 hours of access from activation, with automatic cleanup afterward. Builders can request one sandbox per week (resetting every Sunday), and most environments are ready within 15 minutes. Sandboxes are available for select workshops at launch, with more being enabled over time.
To get started, visit Workshops on AWS Builder Center.