9/4/2026, 12:00:00 AM ~ 9/7/2026, 12:00:00 AM (UTC)
Recent Announcements
AWS announces user-managed setup (3LO) for SharePoint, OneDrive, and Confluence data sources in Amazon Bedrock Managed Knowledge Base. Previously, configuring these data sources required generating 2LO credentials on the third-party side, which could be time-consuming and inaccessible for users who lack admin-level access to those systems. With user-managed setup, you simply sign in with your existing third-party credentials, and Amazon Bedrock Managed Knowledge Base handles authentication—allowing you to complete data source setup in just a few minutes.\n This streamlined experience lowers the barrier to getting started with managed knowledge bases. Teams that want to quickly prototype an AI assistant grounded in their SharePoint documentation, OneDrive files, or Confluence wikis no longer need to coordinate with IT administrators to obtain service account credentials. User-managed setup complements the existing service account authentication, giving you a faster path to connect your data while still preserving the programmatic, enterprise-grade option for production workloads. To get started, see the following resources:; SharePoint user-managed setup in the Amazon Bedrock User Guide, OneDrive user-managed setup in the Amazon Bedrock User Guide and, Confluence user-managed setup in the Amazon Bedrock User Guide
Amazon Bedrock Managed Knowledge Base now supports ServiceNow as a native data source connector
AWS announces the ServiceNow data source connector for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service. Customers can now connect their ServiceNow instance to crawl knowledge articles and service catalog items directly into their managed knowledge base. Previously, bringing ServiceNow content into Bedrock Knowledge Bases required building and maintaining custom ingestion pipelines—now, you simply provide your ServiceNow instance credentials, and the connector handles data crawling, metadata extraction, and incremental sync automatically.\n The ServiceNow connector crawls knowledge articles and service catalog items, including file attachments, giving your AI agents access to the institutional knowledge already maintained in ServiceNow. You can scope crawls to specific knowledge bases, article categories, or service catalogs using sys ID inclusion lists, ensuring only relevant content is ingested. This makes it straightforward to power employee-facing IT assistants, HR helpdesks, or customer support agents grounded in your organization’s up-to-date ServiceNow content. To learn more, see ServiceNow data source in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page.
AWS announces automatic sync scheduling for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service that handles data ingestion, storage optimization, and advanced retrieval without requiring you to manage vector databases or data pipelines. Previously, keeping your knowledge base current required manually triggering a sync each time your source data changed or building a custom solution. Now, you can configure daily, weekly, or monthly sync schedules for all native data source connectors, so your AI agents always retrieve the most up-to-date information.\n With automatic sync scheduling, you can match your sync frequency to how often your source content changes. For example, set a daily sync for a rapidly evolving customer support knowledge base in Confluence, a weekly sync for SharePoint policy documents that update periodically, or a monthly sync for reference materials stored in Amazon S3. This eliminates the need to build and maintain custom scheduling workarounds, reducing operational overhead while ensuring your retrieval-augmented generation applications stay grounded in current enterprise data. To learn more, see Sync scheduling for data sources in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page.
Amazon EC2 now supports specifying compatible instance types on AMIs
Amazon EC2 now enables AMI owners to define which instance types are compatible with their AMIs. Owners can specify supported instance types, unsupported instance types, or both — and any launch attempt on a non-permitted instance type is automatically blocked.\n AMI owners now have a built-in way to prevent launches on instances that are not compatible with their AMIs. This reduces the risk of failed launches due to incompatible instance-AMI pairings. By default, an AMI can be launched on any instance type, so existing workflows remain unaffected until restrictions are explicitly applied.
This feature is available in all AWS Regions at no additional cost. To learn more, please visit the documentation.
Amazon ECS introduces Early Success Criteria for service deployments
Amazon Elastic Container Service (Amazon ECS) now supports Early Success Criteria for rolling service deployments, giving you the flexibility to define when a deployment is considered successful based on your confidence level and the operational needs of your workload. This can help you complete deployments sooner and unblock subsequent deployments, CI/CD pipelines, and other dependent operations.\n With Early Success Criteria, you configure the healthy percent - the proportion of desired tasks that must be running and healthy on the target service revision before the deployment is marked successful. For example, with a desired count of 100 and a healthy percent of 90%, Amazon ECS marks the deployment successful after 90 tasks are healthy and continues launching the remaining tasks through regular service scaling, outside the deployment lifecycle. This can benefit workloads running on specialized or constrained capacity, such as GPU-accelerated inference workloads, where hardware availability can extend task launch times. Early Success Criteria also gives you more control over how long deployment rollback monitoring applies, allowing it to protect the deployment until your configured success criteria are met while subsequent scale-out continues through regular service scaling. You can also choose how Amazon ECS handles source service revision cleanup using BLOCKING or DEFERRED. With BLOCKING, Amazon ECS completes source revision cleanup before declaring success. With DEFERRED, Amazon ECS declares success when the criteria are met and drains source revision tasks asynchronously outside the deployment. This benefits services with active long-lived connections or task scale-in protection, where source revision tasks may need to remain running without holding the deployment open.
The feature is available with the rolling deployment strategy in all AWS Commercial and AWS GovCloud (US) Regions. You can configure Early Success Criteria for new and existing Amazon ECS services using the AWS Management Console, AWS CLI, AWS SDKs, and infrastructure as code (IaC) tools. To learn more, see our documentation.
AWS MCP Server adds a serverless capability for AWS Lambda functions
Today, AWS Model Context Protocol Server (AWS MCP Server) added a serverless capability so that coding agents such as Claude Code and Kiro can efficiently diagnose issues with your Lambda functions. The serverless capability helps you troubleshoot your running Lambda functions and their connected resources.\n The AWS MCP Server, available through the Agent Toolkit for AWS or as a standalone installation, is a managed service that gives AI coding agents secure access to AWS services. With the new serverless capability in the AWS MCP Server, your coding agent inspects your Lambda function and its connected resources across Amazon API Gateway, Amazon EventBridge, Amazon S3, Amazon DynamoDB, Amazon SNS, Amazon SQS, and AWS Step Functions. The agent can correlate error signals against a 7-day baseline to pinpoint what changed, surface recurring errors to identify trends, retrieve the deployed configuration of your function and connected resources, provide a timeline of recent changes to track what happened, and analyze service latency across connected resources. As the agent gets comprehensive data in a single call, it consumes fewer tokens compared to orchestrating multiple API calls.
To get started, configure the Agent toolkit for AWS by running ‘aws configure agent-toolkit’ from the AWS CLI, or enable the AWS MCP Server directly.
The AWS MCP Server can access services in all commercial AWS Regions, while the AWS MCP Server itself runs in the US East (N. Virginia) and Europe (Frankfurt) Regions. The serverless diagnostic capabilities in the AWS MCP Server are available at no additional cost. To learn more, see the user guide.
Amazon EC2 C8g instances now available in additional regions
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8g instances are available in AWS Asia Pacific (Taipei, New Zealand), and AWS GovCloud (US-East) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 C8g instances are built for compute-intensive workloads, such as high performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad serving. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.\n AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon C7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. C8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 C8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
AWS Transfer Family SFTP Connectors now support continuing file transfers during credential rotation
AWS Transfer Family SFTP Connectors now continue running file transfers while you rotate the credentials used to authenticate with remote SFTP servers. You no longer need to update the connector to point to a new secret version each time a credential rotates, removing a manual step and helping avoid failed transfers during the rotation window.\n Connectors can now retrieve credentials from an ordered list of AWS Secrets Manager version stages, such as the current and previous versions, during authentication. The connector automatically tries each version in the order you specify and proceeds with the first that succeeds, so transfers continue uninterrupted as credentials are rotated on either side. You configure this when you create or update a connector, and the connector’s credentials must be stored in AWS Secrets Manager.
Support for continuing transfers during credential rotation is available in all AWS Regions where AWS Transfer Family SFTP Connectors are supported. To learn more, visit the AWS Transfer Family User Guide. Get started with AWS Transfer Family in the AWS Transfer Family console.
Amazon SageMaker AI Batch Transform now supports G6e instances
Amazon SageMaker AI now supports Amazon EC2 G6e instances for batch transform. Batch Transform enables you to run predictions on datasets stored in Amazon S3 and is suited for large datasets that do not require a persistent inference endpoint.\nAmazon EC2 G6e instances are powered by up to eight NVIDIA L40S Tensor Core GPUs with 48 GB of memory per GPU and third-generation AMD EPYC processors. G6e instances deliver improved performance for GPU-intensive workloads. With this launch, you can use G6e instances for GPU-intensive offline inference workloads, including large language models and diffusion models that generate images, video, and audio. To get started, select a supported ml.g6e instance type when creating a Batch Transform job through AWS SDKs, AWS CLI, or the CreateTransformJob API. G6e support for Batch Transform is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), and Asia Pacific (Hyderabad). To learn more, visit the Amazon SageMaker AI product page and see the Batch Transform documentation. For pricing information on these instances, please visit our pricing page.
AWS Blogs
AWS Japan Blog (Japanese)
- Agent Engineering in the Cloud: Kiro Web and Cloud Sessions Now Available to the General Public
- Disaster recovery for VMware workloads on Amazon EVS
- NatWest Achieves Multi-Region Resilience with Amazon Connect Customer
- Combine structured business logic with agent-based AI to deliver a pleasant conversation experience
- AWS Modular Data Center Deployment: From Ordering to Delivery to Installation
- [Event Report] How AI Agents Will Change Drug Discovery Research: Claude Code on AWS Workshop for Pharmaceutical Companies
AWS Big Data Blog
AWS DevOps & Developer Productivity Blog
Artificial Intelligence
- Deploy a multimodal WhatsApp ordering assistant with Amazon Bedrock AgentCore
- Designing lifecycle policies for AgentCore memory
- Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod
- Run agent-driven Amazon SageMaker HyperPod operations with InstantStart
- Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
- How Intuit built an agentic disaster recovery assistant with Amazon Bedrock