Today, we’re announcing the public preview of AWS Well-Architected Agent, an AI-powered service that analyzes your AWS environment to deliver targeted, contextual recommendations for improving your applications’ cost, security, performance, and resilience. The AWS Well-Architected Agent analyzes your infrastructure, understands unique business goals, and delivers contextual recommendations with ready-to-implement fixes. It delivers context-aware optimization without relying on manual audits or generic checklists.

The agent evaluates your environment as an experienced cloud architect would. It automatically correlates utilization metrics, resource configurations, and application topology, and analyzes against Well-Architected best practices across 65+ AWS services. It generates recommendations aligned to your declared business goals, delivers implementation packages with every finding, and surfaces cross-pillar trade-offs making it simpler to remediate the findings.

Here are the three main features of this service:

• Goal-aligned intelligence: AWS Well-Architected Agent replaces flat, undifferentiated findings with context-aware, prioritized recommendations. You declare your business objectives and share your application context. The agent automatically generates and prioritizes recommendations by impact and effort against those goals.
• Three-level recommendations: AWS Well-Architected Agent provides individual resource findings with specific dollar impact (where applicable) and step-by-step remediation, consolidated findings across multiple resources scoped to your application, and broad architectural patterns and designs with Infrastructure as Code (IaC) code changes needed to align with Well-Architected best practices.
• Optionality in remediation: You can choose your path on how you want to remediate with a complete implementation steps tailored to your environment: the console walk-throughs, updated IaC changes for architecture-level recommendations, and AWS Command Line Interface (AWS CLI) commands.

AWS Well-Architected Agent in action
To get started, create an agent profile to define the scope of what Well-Architected Agent can access and provide recommendations on, complete the IAM role setup to access resources, conduct architecture review, and remediate recommendations.

Create an agent profile
In the AWS Well-Architected console, choose Get started with Well-Architected Agent. You can define an agent profile that specifies which AWS accounts and applications to monitor, which optimization pillars to focus on, and the permissions required.

You can choose AWS accounts or AWS Regions to monitor and optimization pillars that matter most to your business. You can also set goals for each pillar: cost optimization, performance, resilience, and security.

To give access to the agent for your AWS environment, provision customer-managed IAM roles the agent uses to read resource configurations, utilization metrics, and application topology. To learn more, visit the IAM prerequisite for AWS Well-Architected Agent.

When you choose Get Started, the agent creates your agent profile. Resource and application recommendations will be generated within 24 hours after profile creation.

You can conduct an architecture review on pre-deployment workloads by uploading an IaC project in Terraform, AWS CloudFormation, or AWS Cloud Development Kit (CDK) to be analyzed. Choose Conduct architecture review in the dashboard, upload a.zip file containing IaC project or repository file, and select which Well-Architected lens to use for reviewing your infrastructure.

You can define your applications to add context which will enhance the relevancy and further contextualize recommendations. Choose Add application context in the dashboard, add your applications with AWS accounts, AWS Regions, AWS services, tags if you want to narrow the scope to specific resources, and the details of applications.

Review prioritized recommendations and start remediating
Now you can see generated prioritized recommendations generated by the agent across your resources and applications, selected pillars, ranked against your declared goals, with automation-ready remediation included.

When you choose the specific recommendation, you can see the details, insights into why the agent are suggesting the recommendation, impacts and trade-off, and recommended fixes across affected AWS resources.

Choose Start remediation to address recommended fixes. You can choose the console, updated IaC template, CLI commands to remediate by the resolution type. It provides detailed step-by-step instructions and you can roll out this instruction and verify the result.

When you choose Using updated IaC template, the agent provides the code changes needed to update your existing IaC templates such as the CDK function shown above which you can copy directly into your codebase.

You can also configure API access to integrate recommendations directly into your existing development and operations workflows. To interact with the agent programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the AWS Well-Architected Agent documentation.

Things to know
Here are some things that you should know about the Well-Architected Agent.

• Automation: You can receive recommendations with the exact IaC code changes needed to remediate, with risks identified by pillar, catching issues before they reach production. Recommendations are delivered through the console and API so you can act without context-switching. Recommendations are also updated periodically, so new recommendations are available for your team to track regularly.
• Evaluation: Generative AI capabilities produce this recommendation, which may contain errors or incomplete information. You are responsible for evaluating the recommendation in your specific context and implementing appropriate oversight and safeguards. Learn more about AWS Responsible AI practices.

You can still use existing AWS Well-Architected Tool to manually evaluate your cloud architecture with user-defined lenses that measure your workload using your own best practices.

Join the preview
Access to the AWS Well-Architected Agent and its recommendations is available in US East (N. Virginia), US East (Ohio), and US West (Oregon). You can onboard workloads from any AWS commercial Region. AWS Well-Architected Agent is delivered by AWS Support and available to AWS customers with an AWS Support plan.

Give it a try today in the AWS Well-Architected console and send feedback through your usual AWS Support contacts.

— Channy

The post Announcing AWS Well-Architected Agent, an AI-powered intelligence to optimize your cloud environment (preview) appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of vector search in Amazon DynamoDB. You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store.

DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors. There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance.

Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search.

If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services. This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model.

Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings. You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time. The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity.

Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline. DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering.

Getting started with vector search in DynamoDB
This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console. The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId, category, description, marketplace, name, and price. The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.

1. Prepare DynamoDB table
To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content. Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible.

I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or other infrastructure-as-code (IaC) tools.

For an existing table like ProductCatalog, I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector. This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes.

2. Create vector index
In the DynamoDB console, open the ProductCatalog table and choose the Indexes tab. I choose Create vector index. On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute.

I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function. Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.

• Euclidean: Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count.
• Dot product: Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.

I enter marketplace as the Partition key. The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index. The partition key is optional, but recommended for large datasets with high query throughput.

I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time. Filter conditions support exact-match values only; range conditions such as BETWEEN or BEGINS_WITH are not supported. I leave Attribute projections set to All so that all table attributes are returned with my search results. Choose Create vector index and wait for the index status to change to Active.

3. Run vector search
I generate a query vector from a natural language search term such as “lightweight running shoes for summer” using the same embedding model I used for the product descriptions. In the DynamoDB console, I choose Explore items in the left navigation pane and select the ProductCatalog table.

Choose Search to switch to vector search mode. I select ProductDescriptionIndex from the Select a vector index dropdown, paste the query vector into the Search vector field, and set Number of results (Top K) to 5. I enter US as the Partition key value to scope the search to the US marketplace. I expand Inline filter attributes and set category equal to footwear to narrow the search to footwear products only. Now, choose Run.

DynamoDB returns the five most semantically similar products in the footwear category, ranked by similarity score, alongside the standard operational attributes such as name and price in the same response. The similarity score’s meaning depends on the distance function selected for the index. For Cosine and Euclidean distance functions, lower similarity score values indicate higher similarity, with a score of 0 indicating identical vectors. For the dot product distance function, higher similarity score values indicate higher similarity.

To interact with vector search programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon DynamoDB Developer Guide.

Get started today
Vector search in Amazon DynamoDB is generally available in all commercial AWS Regions, including the AWS GovCloud (US) Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For pricing details, visit the Amazon DynamoDB pricing page.

Start exploring vector search in DynamoDB today and send feedback to AWS re:Post for Amazon DynamoDB or through your usual AWS Support contacts.

— Esra

The post Amazon DynamoDB now supports real-time vector search at any scale appeared first on The NOKIA Blog.

A couple of editions ago I wrote about what I find so energizing about working with startups. Last week I got a fresh dose of it: I spent a few days with the AWS Startups team, listening to stories of founders talking about the problems they’re actually solving. One story that stayed with me came from Marco Negreiros, founder of EyeCare Health, a Brazilian healthtech expanding access to eye care. He shared a striking fact: more than 70% of Brazilian municipalities don’t have a single ophthalmologist. His answer was to put a vision test on the one device almost everyone already carries, the smartphone, so a basic eye screening no longer depends on living near a clinic. Watching a founder turn a gap that big into something that concrete is exactly why I love this space.

This week, I’ll take a closer look at some key launches, and then cover the quarterly AWS Service Availability updates.

Last week’s launches

Here are some of the launches covered from this past week in the AWS News Blog:

Here are some launches and updates that caught my attention:

• Claude Sonnet 5 is now available on AWS – Anthropic’s most capable Sonnet model brings top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional work at scale. It navigates large codebases, calls tools precisely, and holds state across long agentic tasks. To learn more, visit the AI Blog post.
• Amazon WorkSpaces for AI agents is now generally available: AI agents can now securely access and operate desktop applications through managed WorkSpaces environments, without requiring application modernization or custom integrations. To learn more. visit the Desktop and Application Streaming Blog post.
• Amazon OpenSearch Service is now optimized for log analytics: This release introduces a new engine purpose-built for log analytics workloads that delivers up to 4x better price-performance on internal benchmarks, while keeping the full-text search capabilities OpenSearch is known for. Teams can now get aggregations and precise text search in one place. To learn more, visit the Big Data Blog post.
• Amazon SageMaker AI cuts generative AI inference scale-out time by up to half: SageMaker Inference now supports container image caching, enabling up to 2x faster end-to-end scaling for generative AI models during scale-out events. To learn more, visit the AI Blog post.
• Amazon CloudWatch supports creating alarms from log queries : You can now create alarms directly on log query results and set thresholds in a single workflow, eliminating the need to first create metric filters or custom metrics as intermediate steps.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

AWS Service Availability Updates

When the availability of an AWS service or feature changes, we provide customers guidance in AWS Product Lifecycle Changes on available alternatives and support for migration so that disruptions to your operations are minimized. The following lifecycle changes were updated on June 30, 2026.

Services moving to Maintenance (no longer accessible to new customers starting July 30, 2026):

Services entering Sunset:

Services reaching End of Support (as of June 30, 2026):

• Amazon Chime SDK – Carrier Voice Focus
• Amazon SageMaker AI – Ground Truth Plus

We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22. Registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Claude Sonnet 5 on AWS, Amazon WorkSpaces for AI agents, AWS service availability updates, and more (July 6, 2026) appeared first on The NOKIA Blog.

Today, we’re announcing AWS CloudFormation Express mode, a new deployment mode that accelerates deployments for developers and AI tools iterating on infrastructure. Express mode accelerates deployments by completing when CloudFormation confirms resource configuration is applied, rather than waiting for extended stabilization checks. This reduces deployment time by up to 4 times for iterative development workflows and production scenarios.

How it works

Every CloudFormation deployment performs stabilization checks after resource configuration is applied. These checks serve an important purpose when you need to confirm resources can serve traffic before shifting load.

However, many workflows do not require full stabilization to proceed. Express mode benefits two primary use cases: iterative development workflows and production scenarios where you are comfortable with eventual stabilization. These use cases include iterating on infrastructure configurations during development, testing individual components of your application, and AI-assisted infrastructure development that benefits from sub-minute feedback loops.

With Express mode, CloudFormation completes deployments when resource configuration is applied, without waiting for stabilization checks. Resources continue becoming operational in the background. CloudFormation automatically retries dependent resources that encounter transient failures during provisioning within the same stack, without requiring any customer intervention. This built-in resilience handles timing issues between resources as they stabilize. Express mode changes when the deployment completes, not how resources are provisioned.

For example, when I create an Amazon Simple Queue Service (SQS) queue with a dead letter queue (DLQ), Standard mode takes 64 seconds, but Express mode completes in up to 10 seconds. In the case of deleting an AWS Lambda function with network interface attachment, Standard mode takes 20–30 minutes, but Express mode completes in up to 10 seconds based on my benchmarking test.

Get started with CloudFormation Express mode

When you create a CloudFormation stack in the AWS Management Console, choose Enable in the Express mode under Stack deployment options.

You can also use AWS Command Line Interface (AWS CLI), AWS SDKs, or IaC tools like AWS Cloud Development Kit (CDK), and AI tools such as Kiro.

Activate Express mode by setting the --deployment-config parameter to EXPRESS when creating, updating, or deleting stacks. No template changes are required. Express mode disables rollback by default for the fastest iteration experience. To re-enable rollback, set disableRollback to false in the deployment-config for production environments, or implement monitoring/cleanup mechanisms for failed deployments.

aws cloudformation create-stack  
   --stack-name my-app  
   --template-body file://template.yaml  
   --deployment-config '{"mode": "EXPRESS", "disableRollback": true}' 

For example, use the Express mode when you build infrastructure incrementally, adding resources one at a time. Ensure your IAM role templates follow the principle of least privilege.

# Iteration 1: Deploy IAM role
aws cloudformation create-stack 
--stack-name my-microservice 
--template-body file://iteration1-iam.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 2: Add Lambda function
aws cloudformation update-stack 
--stack-name my-microservice 
--template-body file://iteration2-lambda.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 3: Add SQS queue and event source mapping
aws cloudformation update-stack 
--stack-name my-microservice 
--template-body file://iteration3-sqs.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

For AWS CDK, activate Express mode with the cdk deploy --express command when you deploy your CDK stack. This command retrieves your generated CloudFormation template and deploys it through the CloudFormation Express mode, which provisions your resources as part of a CloudFormation stack.

Express mode works with all existing CloudFormation templates and supports all CloudFormation features including change sets and nested stacks. When you enable Express mode on a parent stack, all nested stacks also use Express mode. If you need resources to be fully operational before proceeding with traffic or testing, continue using the default deployment behavior, which performs stabilization checks before completing.

Now available

AWS CloudFormation Express mode is available today in all AWS commercial Regions at no additional cost. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool. To learn more, visit the CloudFormation documentation.

Start accelerating your deployments today, and send feedback to AWS re:Post for AWS CloudFormation or through your usual AWS Support contacts.

— Channy

The post Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode appeared first on The NOKIA Blog.

Amazon Elastic Container Service (Amazon ECS) service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics.

You can choose proactive scaling by using predictive scaling (automatic) and scheduled scaling (customer-defined), or reactive scaling by using target tracking with just a target to scale on. Amazon ECS service auto scaling adjusts the number of tasks in an ECS service based on Amazon CloudWatch metrics, such as average CPU/Memory usage, request count per target, a custom metric such as queue depth, or demand surges by using advanced machine learning (ML) algorithms.

With today’s launch, Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x)

This launch delivers three key benefits for your applications:

• Improved performance and reliability: Faster scaling means, your application responds faster to demand surges, reducing latencies or failures for end users during demand surges.
• Right-size without compromise: Depending on the workload, you can reduce baseline task counts because scale-out now happens fast enough to handle traffic spikes without preemptive capacity padding. This directly reduces compute costs while maintaining application performance and availability.
• Simpler scaling configuration: Target tracking with high-resolution metrics delivers the aggressive scaling behavior that previously required custom scaling configurations, such as usage of step-scaling policies. One configuration change replaces custom engineering work.

How it works

To use ECS faster service auto scaling, first enable high-resolution metrics for your ECS service, and then configure a target tracking scaling policy which uses high-resolution metrics. ECS faster service autoscaling works across all compute options on ECS: AWS Fargate, ECS Managed Instances, and Amazon Elastic Compute Cloud (Amazon EC2). You can enable these metrics when you create or update your ECS service in the Amazon ECS console, or using AWS SDKs and tools, and AWS CloudFormation.

When you create a service in the console, add 20-seconds resolution metrics in the Monitoring configuration section. These metrics incur additional CloudWatch costs while the standard resolution (60-seconds) is free.

In the Service auto scaling section, check Use service auto scaling and choose Target Tracking for the scaling policy type to use real-time data to scale the number of tasks that your service runs based on demand.

Then, choose a Scaling policy type for the target tracking. You can select ECSServiceAverageCPUUtilizationHighResolution or ECSServiceAverageMemoryUtilizationHighResolution as new metrics.

That’s it – your ECS service will use high resolution metrics for auto scaling.

To update an existing ECS service to use faster auto scaling, you first need to configure high resolution metrics via Update Service. Once deployment completes, your service will generate high-resolution metrics. You can then go to the Service and auto scaling tab from your service details to update scaling policy to use higher resolution metrics.

That’s all you need. Your ECS service now evaluates scaling decisions at 20-second intervals.

You can also use the AWS Command Line Interface (AWS CLI) to enable new metrics in your ECS service through Application Auto Scaling. To learn more, visit the faster auto scaling documentation.

Now available

Faster service autoscaling with high-resolution metrics for Amazon ECS is available today. The feature itself has no additional cost, but high-resolution CloudWatch metrics introduce a new pricing dimension. For details, see the CloudWatch pricing page.

Give it a try today and send feedback to AWS re:Post for ECS or through your usual AWS Support contacts.

— Channy

The post Amazon ECS introduces new high-resolution metrics for faster service auto scaling appeared first on The NOKIA Blog.

Today, we’re announcing the next generation of AWS Resilience Hub with a significantly expanded experience that brings together a new application model, dependency discovery assessment, generative AI-powered failure mode analysis, modular resilience policies, and organization-wide reporting.

Organizations running hundreds of applications share a common challenge: availability is a top concern, yet there is no consistent way to set resilience goals, measure progress, or prove compliance across a portfolio. Teams set different standards, use different tools, and struggle to exchange information about whether applications actually meet expectations.

The next generation of AWS Resilience Hub changes this by giving Site Reliability Engineers (SREs) and development teams a structured way to align on resilience policy expectations, help application teams achieve them, and demonstrate compliance through testing. With integration into AWS Organizations, teams can now evaluate resilience at scale, identify failure modes, discover hidden dependencies, and report on progress across the enterprise.

The next generation of Resilience Hub walks you through your resilience journey and to help you there are the following concepts built into it.

• Resilience policy: You can define your resilience expectations through modular, composable requirements. Rather than choosing a single rigid policy type, you construct policies by selecting the requirements that matter to your application, such as service level objective (SLO), multi-AZ and multi-Region disaster recovery, and data recovery requirements.
• Business-level understanding: You can use new application modeling through critical end-user paths that map directly to business outcomes. Systems represent a business application, user journeys describe critical business paths, and services are the deployable units comprising AWS resources, code, and observability. Resilience Hub automatically discovers and maps them into a topology showing how resources connect.
• AI failure mode assessments: You can run generative AI-powered assessments that analyze your services against your defined resilience policies, AWS Well-Architected best practices, and the AWS Resilience Analysis Framework. These assessments identify potential failure modes and provide actionable recommendations.
• Dependency discovery assessment: You can automatically discover AWS services, internal endpoints, and third-party endpoints that your services depend on. This dependency assessment uses DNS query log analysis to identify dependencies you may not know about—including unexpected cross-region calls or critical third-party dependencies.

The next generation of AWS Resilience Hub in action

To get started, you configure a resilience policy, set up your first system and service, run a failure mode assessment, review the results, and implement the findings.

Before you begin, you should set up the invoker IAM role, which grants Resilience Hub read-only access to your AWS resources, cross-account roles (if not using AWS Organizations), or service-linked roles (SLRs) with AWS Organizations. Resilience Hub also integrates with AWS Organizations to enable organization-wide resilience management from a single delegated administrator account. This eliminates the need to log in to individual accounts to assess resilience posture across your enterprise. To learn more, visit For prerequisite details in the AWS Resilience Hub User Guide.

To configure a resilience policy, choose Create policy in the Policies menu through the AWS Resilience Hub console. Enter a policy name, description, and choose resilience requirements. For example, you can create a reusable policy for multi-Region disaster recovery used in financial applications—including 99.95% availability SLO, 15-minutes RTO, 5-minutes RPO for multi-Region disaster recovery, and disaster recovery approach that aligns with your RTO and RPO requirements.

If you choose data recovery requirements, you can define the data recovery time objective for restoring from backups for each service associated with this policy.

To create your first system representing your business application, choose Create a system in the Systems menu. Optionally, you can enable AWS Organizations account access for this system.

Now you can create a service that represents a deployable unit, like one of your microservices, and associate it with your system, and tell Resilience Hub where to find your resources. Enter a service name, for example, stock-exchange-service, choose your resilience policy and invoker AWS IAM role name. You can choose service Regions, service resources such as your resource tags, AWS CloudFormation stack, Terraform state file location, or Amazon EKS cluster and namespace.

When you enable dependency discovery for this service, AWS examines your VPC query logs for the VPCs associated with the resources in your service. You can disable this feature anytime from the dependency discovery settings in the service details page.

Now, you can run your first assessment with the service creation complete and a policy applied. Choose Run failure mode assessment in your service page and wait for the assessment to complete.

During the assessment, Resilience Hub assumes your invoker role, reads resources from your configured input sources, identifies parent-child relationships, queries the application topology service to map connections between resources, and builds a topology showing data flow, containment, and permissions.

By choosing Service topology, you can see service resources grouped by service functions in the graph, table, or JSON format.

By choosing Failure mode guidance, you can add assertions used to guide the agents while performing the failure mode assessment. Assertions are either generated by the agent or added by users. You can update them to improve assessment accuracy.

Once the assessment is complete, you can review findings and recommendations in the Assessment tab of your service page. Each finding tells you what the failure mode is, why it matters for your architecture, how to fix it, and which policy requirement it relates to.

You can choose Mark as resolved to implement the recommendation or Mark as irrelevant if the finding doesn’t apply to your use case.

If you’re an existing Resilience Hub customer, Resilience Hub provides migration APIs to simplify the transition of your previous applications. These APIs convert your previous assessment policies to new resilience policies, map your previous applications to the new model, such as multiple related applications to one system with multiple services.

For more information about new features, visit the AWS Resilience Hub User Guide.

Now available

The next generation of AWS Resilience Hub is now generally available in AWS commercial Regions where Resilience Hub is available. For Regional availability and the future roadmap, visit the AWS Capabilities by Region.

Resilience Hub uses a new service-based pricing model. Pricing includes two failure mode assessments per month for services, and optionally automated dependency assessment. You can try AWS Resilience Hub free. For pricing details, visit the AWS Resilience Hub pricing page.

Give the new AWS Resilience Hub a try in the Resilience Hub console and send feedback to AWS re:Post for Resilience Hub or through your usual AWS Support contacts.

— Channy

The post Introducing the next generation of AWS Resilience Hub for generative AI-based SRE resilience journey appeared first on The NOKIA Blog.

There’s something genuinely energizing about working with startups — something I’ve been doing intensely for more than two years now. Startups operate at a different frequency: the urgency is real, the constraints are tight, and the stakes are personal. Helping them navigate the challenge of proving their business model requires not just technical depth but a willingness to move fast, challenge assumptions, and make bets on the right architecture before the perfect data exists.

What I love most is that the work is never abstract: every decision I help a startup make has a direct impact on whether they ship on time, stay within budget, and earn the next round of confidence from their investors.

Let’s dive into this week’s AWS news.

Headlines

Now Open — AWS Local Zones in Istanbul, Türkiye — AWS has opened a new Local Zone in Istanbul, Türkiye, bringing AWS compute, storage, and networking services to one of Europe’s largest metropolitan areas. AWS Local Zones place AWS infrastructure much closer to large population and industry, enabling organizations to store and process data within specific jurisdictions to meet data residency requirements, while delivering single-digit millisecond latency for applications that need to run closer to end users. This supports compliance needs across financial services, government, telecoms, and healthcare.

A Local Zone is a significant infrastructure investment: it requires the same level of operational excellence as a Region, with consistent hardware, power, and networking infrastructure, and reflects AWS’s continued expansion into underserved markets.

For builders in Türkiye, this opens up a new set of architectural possibilities. You can now store and back up data within Turkish borders to help meet data residency requirements, and run latency-sensitive workloads in the Istanbul Local Zone while connecting seamlessly to the AWS Region—giving you the flexibility to architect hybrid applications without managing your own data center infrastructure.

To learn more about our decade-long commitment, available services, customers and partners in Türkiye, visit the launch blog post.

Last week’s launches

Here are some launches and updates that caught my attention:

• Security Hub Extended expands to 21 curated partner solutions across 9 categories — AWS Security Hub Extended now integrates with 21 curated partner security solutions spanning 9 categories, including endpoint protection, cloud security posture management, threat intelligence, and more. You can now get consolidated, prioritized security findings from a broader ecosystem of tools directly within Security Hub, without requiring custom integrations. This is particularly valuable for enterprise security teams that want a unified view of their security posture across AWS and third-party tooling.
• Amazon SageMaker AI now supports OpenAI-compatible APIs for inference endpoints — You can now call Amazon SageMaker AI inference endpoints using OpenAI-compatible APIs, making it significantly easier to migrate AI workloads from OpenAI to SageMaker — or to build applications that work across multiple providers — with no SDK changes required. This lowers the migration barrier for teams that started prototyping with OpenAI and are now looking to move to a more scalable, cost-controlled infrastructure on AWS. Your existing application code works as-is; you simply point it at your SageMaker endpoint.
• Introducing pre-fetching and IAM role assumption for AWS Secrets Manager Agent — The AWS Secrets Manager Agent can now pre-fetch secrets at startup and assume IAM roles to retrieve them, eliminating the cold-start latency associated with on-demand secret retrieval in latency-sensitive applications. You can configure the agent to preload the secrets your application needs before it starts serving traffic, reducing the risk of secrets-related latency spikes in production. IAM role assumption support also makes it easier to share the agent across workloads with different permission boundaries.
• AWS announces ExtendDB, an open-source DynamoDB-compatible adapter — AWS has open-sourced ExtendDB, a DynamoDB-compatible adapter that allows you to use the DynamoDB API and data model on top of alternative backend storage systems. This is particularly useful for local development and testing workflows — you can write against the DynamoDB API without requiring a live AWS connection. It’s also valuable for scenarios where you need DynamoDB-compatible semantics with more control over the underlying storage layer. It’s a practical tool for teams that want to build portability into their data access layer.
• AWS SAM CLI adds AWS CloudFormation Language Extensions support to accelerate local serverless development — The AWS SAM CLI now supports AWS CloudFormation Language Extensions locally, meaning you can use transforms, dynamic references, and other CloudFormation language features directly in your local development and testing workflows. This closes a long-standing gap between what you can test locally and what runs in production, making local serverless development faster and more reliable. If you build serverless applications with SAM and encounter edge cases in local testing, this update will meaningfully improve your experience.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news

Here are some additional posts and resources that you might find interesting:

• Amazon Bedrock introduces new advanced prompt optimization and migration tool — This post covers the newly launched Advanced Prompt Optimization and Migration Tool in Amazon Bedrock, which helps you automatically tune your prompts for better model performance and assists you in migrating prompts across different foundation models. It’s a must-read if you’re iterating on prompt quality for production AI workloads.
• Introducing Kiro Web — Kiro, AWS’s AI-powered development environment, now has a web-based interface. Kiro Web lets you access Kiro’s spec-driven development, AI chat, and agent capabilities directly from your browser, without needing to install the desktop IDE. This is a great step toward making AI-assisted development more accessible — whether you’re doing a quick review, prototyping from a new machine, or introducing your team to the Kiro workflow.
• Announcing updated retry behavior for AWS SDKs and Tools — AWS has updated the default retry behavior across its SDKs and CLI tools, improving resilience for transient errors without requiring configuration changes from developers. The updated behavior includes smarter backoff strategies and better handling of throttling responses. If you’re running production workloads that occasionally hit API rate limits or transient failures, this update improves reliability out of the box. It’s worth reading to understand what changed and how it affects your applications.
• Bitnami image removal from ECR Public — AWS has announced that Bitnami container images will be removed from Amazon ECR Public. If your workloads pull Bitnami images from ECR Public, you should review this post to understand the timeline and migration path. The Bitnami images remain available directly from Bitnami’s own registry, and this post explains how to update your image references to continue pulling them without interruption.

Upcoming AWS events

Check your calendar and sign up for these events:

• AWS Summit Amsterdam — Join us in Amsterdam on May 27 for a full day of cloud and AI sessions, hands-on labs, and networking with builders and AWS experts from across Europe. Registration is free.
• AWS Summit Bangkok — AWS Summit Bangkok takes place on May 28. It’s a fantastic opportunity for builders and customers across Southeast Asia to connect and explore the latest in cloud innovation.
• AWS Summit Milan — Also on May 28, AWS Summit Milan brings the AWS community together in Italy. If you’re in Southern Europe, this is your event.
• AWS Summit Mumbai — Also on May 28, AWS Summit Mumbai brings cloud and AI content to builders across India. Check the link for the full agenda and registration.
• AWS Summit Los Angeles — Mark your calendar for June 10 in Los Angeles. The AWS Summit LA is coming up and it’s a great opportunity to connect with the West Coast builder community.
• AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22 — registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: AWS Local Zones in Istanbul, open-source ExtendDB, Kiro Web, and more (May 25, 2026) appeared first on The NOKIA Blog.

I have been building with AI agents and MCP tools for a while now, and one question kept coming up: how do you give an agent real, authenticated access to AWS without handing it the keys to the kingdom? Today, there is an answer.

I’m happy to announce the general availability of the AWS MCP Server, a managed remote Model Context Protocol (MCP) server that gives AI agents and coding assistants secure, authenticated access to all AWS services through a small, fixed set of tools.

The AWS MCP Server is part of the Agent Toolkit for AWS, a suite of tooling that includes the MCP Server, skills, and plugins that help coding agents build more effectively and efficiently on AWS.

AI coding agents are already useful for many tasks, but they run into real trouble when working with AWS at any meaningful depth. Without access to current AWS documentation, agents rely on training data that may be months out of date and may not know about services like Amazon S3 Vectors, Amazon Aurora DSQL, or Amazon Bedrock AgentCore. When asked to build infrastructure, they tend to reach for the AWS Command Line Interface (AWS CLI) rather than AWS Cloud Development Kit (AWS CDK) or AWS CloudFormation, and they produce AWS Identity and Access Management (IAM) policies that are far broader than necessary. The result is infrastructure that works in a demo but is not production-ready.

The AWS MCP Server addresses this through a compact set of tools that do not consume your model’s context window. The call_aws tool executes any of the 15,000+ AWS API operations using your existing IAM credentials. When we will launch new APIs, they will be supported within days. The search_documentation and read_documentation tools retrieve current AWS documentation and best practices at query time, so the agent always works from up-to-date information.

With general availability, we are introducing several new capabilities. The AWS MCP Server now supports IAM context keys, so you no longer need a separate IAM permission to use the server and can express fine-grained access in a standard IAM policy. Documentation retrieval no longer requires authentication. We have also reduced the number of tokens required per interaction, which matters for complex, multi-step workflows.

Also new, the run_script tool lets the agent write a short Python script that runs server-side in a sandboxed environment. The sandbox inherits your IAM permissions but has no network access, so you can give an agent the ability to process data without giving it access to your local file system or a shell. When an agent needs to call multiple APIs and combine the results, making them one at a time is slow and burns context. With run_script, the agent chains API calls, filters responses, and computes results in a single round-trip, which is both faster and more context-efficient.

The most significant addition is the transition from Agent SOPs to Skills. Skills provide curated guidance and best practices for the tasks where agents most commonly make mistakes. This helps agents complete work faster, using validated best practices, with fewer errors and fewer tokens — all of which saves you time and money. Skills are contributed and maintained by AWS service teams. This keeps the tool list short and predictable, which reduces hallucination and keeps the agent focused.

For enterprise customers, the AWS MCP Server provides a clear separation between human and agent permissions. You can use IAM policies or Service Control Policies to specify that a given user can perform mutating operations while the MCP server is restricted to read-only actions. Amazon CloudWatch metrics published under the AWS-MCP namespace let you observe MCP server calls separately from direct human calls, giving you the audit trail that compliance teams require. Amazon CloudTrail captures all API calls for a complete record.

Let’s see it in action

For this demo, I chose to use Claude Code, but I can use the AWS MCP Server with any AI agent that supports MCP, which is basically all tools available today: Kiro CLI, Kiro, Cursor, Codex, and more. I configure Claude Code to use the Anthropic Opus 4.6 model.

Opus 4.6 has a knowledge cutoff date in May 2025. It means it doesn’t know anything that happened after May last year. I ask a question about an AWS service that was introduced recently: Amazon S3 Vectors, launched in preview in July 2025 and that went GA in December 2025.

The question is “how to store embedding on S3″. (embedding is a kind of vector)

It gives me five solutions, all correct, but none using S3 Vectors as I asked. Note that this answer comes from the Opus 4.6 model, not from Claude Code. Any AI tool using the same model will return similar answers because S3 Vectors wasn’t announced at the time the model was trained.

Let’s now try with the AWS MCP Server.

The AWS MCP Server uses AWS Identity and Access Management (IAM) and IAM SigV4 authentication. To use my local AWS credentials configuration over MCP, which only supports OAuth 2.1, I configure my AI coding agent to call the AWS MCP Server through a proxy. The MCP Proxy for AWS is an open source proxy that runs on my machine and bridges the world of IAM authentication to OAuth.

I add the MCP configuration with this command:

claude mcp add-json aws-mcp --scope user 
   '{"command":"uvx","args":["mcp-proxy-for-aws@latest","https://aws-mcp.us-east-1.api.aws/mcp","--metadata","AWS_REGION=us-west-2"]}'

You’ll have to have uv installed before you can use the AWS MCP server. On Linux or Mac, you can run: curl -LsSf https://astral.sh/uv/install.sh | sh

Let’s analyze the JSON configuration:

• I use the user scope to make the server available to all my projects on my laptop.
• uvx mcp-proxy-for-aws is the command to launch the proxy; the rest of the arguments are parameters passed to the proxy.
• https://aws-mcp.us-east-1.api.aws/mcp is one of the two regional endpoints for the AWS MCP Server. The proxy will forward Claude Code’s requests to that endpoint.
• --metadata are passed to the proxy target. Here, it tells the AWS MCP Server to use the US West (Oregon) Region.

I start Claude Code and I type /mcp to verify the AWS MCP Server is correctly installed and can use my credentials.

I ask the same question: “how can I store embedding on S3”.

This time, Claude Code knows it has a tool it can use to answer the question. It asks me permission to invoke the aws___search_documentation tool. After a few seconds, I receive a correct answer: “AWS now has a dedicated service for this: Amazon S3 Vectors …”

Pricing and availability

The AWS MCP Server is available today in the US East (N. Virginia) and Europe (Frankfurt) AWS Regions and can make API calls to any Region. There is no additional charge for the AWS MCP server itself. You pay only for the AWS resources you create and any applicable data transfer costs.

The AWS MCP Server works with Claude Code, Kiro, Cursor, and any MCP-compatible client. To get started, see the AWS MCP Server User Guide.

I have been waiting for something like this since I started using MCP tools in my AI agents early last year. The combination of current documentation, authenticated API access, and sandboxed script execution in a single server changes what an agent can actually do on AWS. I am curious what you build with it. Let me know in the comments.

— seb

Updated on May 6th – Added uv installation script.

The post The AWS MCP Server is now generally available appeared first on The NOKIA Blog.

In August 2025, we introduced AWS User Experience Customization (UXC) capability to tailor user interfaces (UIs) to meet your specific needs and complete your tasks efficiently. With this capability, your account administrator can customize some UI component of AWS Management Console, such as assigning a color to an AWS account for easier identification.

Today, we’re announcing additional customization capability in UXC that enables selective display of relevant AWS Regions and services for your team members. By hiding unused Regions and services, you can reduce cognitive load and eliminate unnecessary clicks and scrolling, helping you focus better and work faster. With this launch, we offer the ability to customize account color, Region, and service visibility together.

Categorize account by color

You can set a color for your accounts to visually distinguish between them. To get started, sign in to the AWS Management Console and choose your account name on the navigation bar. Your account color isn’t set yet. To set the color, choose Account.

In the Account display settings, select your preferred account color and choose Update. You can see the chosen color in the navigation bar.

By changing the account color, you can clearly distinguish the account’s purpose. For example, you can use orange for development accounts, light blue for test accounts, and red for production accounts.

Customize Regions and services visibility

You can control which AWS Regions appear in the Region selector or which AWS services appear in the console navigation. In other words, you can set to show only the Regions and services that are relevant to your account.

To get started, choose the gear icon on the navigation bar and choose See all user settings. If you are in an administrator role, you can see a new Account settings tab in the unified settings. If you have not configured a setting, all Regions and services are visible.

To set visible Regions, choose Edit in the Visible Regions section. Select your visible Regions to All available Regions or Select Regions and configure your list. Choose Save changes.

After configuring visible Region setting, you will find only selected Regions in the Regions selector on the navigation bar in the console.

You can also set visible services in the same way. Search or select services from the category. I used the Popular services category to select my favorites. When you finish selection, choose Save changes.

After configuring visible services setting, you will find only selected services in the All services menu on the navigation bar.

When you search the service name in the search bar, you can only choose selected services.

The Regions and services visibility settings control only the appearance of services and Regions in the console. They don’t restrict access through the AWS Command Line Interface (AWS CLI), AWS SDKs, AWS APIs, or Amazon Q Developer.

You can also manage these account customization settings programmatically with new visibleServices and visibleRegions parameters. For example, you can use AWS CloudFormation sample template:

AWSTemplateFormatVersion: "2010-09-09"
Description: Customize AWS Console appearance for this account

Resources:
  AccountCustomization:
    Type: AWS::UXC::AccountCustomization
    Properties:
      AccountColor: red
      VisibleServices:
        - s3
        - ec2
        - lambda
      VisibleRegions:
        - us-east-1
        - us-west-2

And you can deploy your Cloudformation template.

$ aws cloudformation deploy 
  --template-file account-customization.yaml 
  --stack-name my-account-customization

To learn more, visit the AWS User Experience Customization API Reference and AWS CloudFormation template reference.

Give it a try in the AWS Management Console today and provide feedback by selecting the Feedback link at the bottom of the console, posting to the AWS re:Post forum for the AWS Management Console, or reaching out to your AWS Support contacts.

— Channy

The post Customize your AWS Management Console experience with visual settings including account color, region and service visibility appeared first on The NOKIA Blog.

Today, we’re announcing a new feature of Amazon Simple Storage Service (Amazon S3) you can use to create general purpose buckets in your own account regional namespace simplifying bucket creation and management as your data storage needs grow in size and scope. You can create general purpose bucket names across multiple AWS Regions with assurance that your desired bucket names will always be available for you to use.

With this feature, you can predictably name and create general purpose buckets in your own account regional namespace by appending your account’s unique suffix in your requested bucket name. For example, I can create the bucket mybucket-123456789012-us-east-1-an in my account regional namespace. mybucket is the bucket name prefix that I specified, then I add my account regional suffix to the requested bucket name: -123456789012-us-east-1-an. If another account tries to create buckets using my account’s suffix, their requests will be automatically rejected.

Your security teams can use AWS Identity and Access Management (AWS IAM) policies and AWS Organizations service control policies to enforce that your employees only create buckets in their account regional namespace using the new s3:x-amz-bucket-namespace condition key, helping teams adopt the account regional namespace across your organization.

Create your S3 bucket with account regional namespace in action

To get started, choose Create bucket in the Amazon S3 console. To create your bucket in your account regional namespace, choose Account regional namespace. If you choose this option, you can create your bucket with any name that is unique to your account and region.

This configuration supports all of the same features as general purpose buckets in the global namespace. The only difference is that only your account can use bucket names with your account’s suffix. The bucket name prefix and the account regional suffix combined must be between 3 and 63 characters long.

Using the AWS Command Line Interface (AWS CLI), you can create a bucket with account regional namespace by specifying the x-amz-bucket-namespace:account-regional request header and providing a compatible bucket name.

$ aws s3api create-bucket --bucket mybucket-123456789012-us-east-1-an 
   --bucket-namespace account-regional 
   --region us-east-1

You can use the AWS SDK for Python (Boto3) to create a bucket with account regional namespace using CreateBucket API request.

import boto3

class AccountRegionalBucketCreator:
    """Creates S3 buckets using account-regional namespace feature."""
    
    ACCOUNT_REGIONAL_SUFFIX = "-an"
    
    def __init__(self, s3_client, sts_client):
        self.s3_client = s3_client
        self.sts_client = sts_client
    
    def create_account_regional_bucket(self, prefix):
        """
        Creates an account-regional S3 bucket with the specified prefix.
        Resolves caller AWS account ID using the STS GetCallerIdentity API.
        Format: ---an
        """
        account_id = self.sts_client.get_caller_identity()['Account']
        region = self.s3_client.meta.region_name
        bucket_name = self._generate_account_regional_bucket_name(
            prefix, account_id, region
        )
        
        params = {
            "Bucket": bucket_name,
            "BucketNamespace": "account-regional"
        }
        if region != "us-east-1":
            params["CreateBucketConfiguration"] = {
                "LocationConstraint": region
            }
        
        return self.s3_client.create_bucket(**params)
    
    def _generate_account_regional_bucket_name(self, prefix, account_id, region):
        return f"{prefix}-{account_id}-{region}{self.ACCOUNT_REGIONAL_SUFFIX}"


if __name__ == '__main__':
    s3_client = boto3.client('s3')
    sts_client = boto3.client('sts')
    
    creator = AccountRegionalBucketCreator(s3_client, sts_client)
    response = creator.create_account_regional_bucket('test-python-sdk')
    
    print(f"Bucket created: {response}")

You can update your infrastructure as code (IaC) tools, such as AWS CloudFormation, to simplify creating buckets in your account regional namespace. AWS CloudFormation offers the pseudo parameters, AWS::AccountId and AWS::Region, making it easy to build CloudFormation templates that create account regional namespace buckets.

The following example demonstrates how you can update your existing CloudFormation templates to start creating buckets in your account regional namespace:

BucketName: !Sub "amzn-s3-demo-bucket-${AWS::AccountId}-${AWS::Region}-an"
BucketNamespace: "account-regional"

Alternatively, you can also use the BucketNamePrefix property to update your CloudFormation template. By using the BucketNamePrefix, you can provide only the customer defined portion of the bucket name and then it automatically adds the account regional namespace suffix based on the requesting AWS account and Region specified.

BucketNamePrefix: 'amzn-s3-demo-bucket'
BucketNamespace: "account-regional"

Using these options, you can build a custom CloudFormation template to easily create general purpose buckets in your account regional namespace.

Things to know

You can’t rename your existing global buckets to bucket names with account regional namespace, but you can create new general purpose buckets in your account regional namespace. Also, the account regional namespace is only supported for general purpose buckets. S3 table buckets and vector buckets already exist in an account-level namespace and S3 directory buckets exist in a zonal namespace.

To learn more, visit Namespaces for general purpose buckets in the Amazon S3 User Guide.

Now available

Creating general purpose buckets in your account regional namespace in Amazon S3 is now available in 37 AWS Regions including the AWS China and AWS GovCloud (US) Regions. You can create general purpose buckets in your account regional namespace at no additional cost.

Give it a try in the Amazon S3 console today and send feedback to AWS re:Post for Amazon S3 or through your usual AWS Support contacts.

— Channy

The post Introducing account regional namespaces for Amazon S3 general purpose buckets appeared first on The NOKIA Blog.

Over the past week, we passed Laba festival, a traditional marker in the Chinese calendar that signals the final stretch leading up to the Lunar New Year. For many in China, it’s a moment associated with reflection and preparation, wrapping up what the year has carried, and turning attention toward what lies ahead.

Looking forward, next week also brings Lichun, the beginning of spring and the first of the 24 solar terms. In Chinese tradition, spring is often seen as the season when growth begins and new cycles take shape. There’s a common saying that “a year’s plans begin in spring,” capturing the idea that this is a time to set one’s direction and start fresh.

Last week’s launches

Here are the launches that got my attention this week:

• Amazon Bedrock enhances support for agent workflows with server-side tools and extended prompt caching – Amazon Bedrock introduced two updates that improve how developers build and operate AI agents. The Responses API now supports server-side tool use, so agents can perform actions such as web search, code execution, and database updates within AWS security boundaries. Bedrock also adds a 1-hour time-to-live (TTL) option for prompt caching, which helps improve performance and reduce the cost for long-running, multi-turn agent workflows. Server-side tools are available with OpenAI GPT OSS 20B and 120B models, and the 1-hour prompt caching TTL is generally available for select Claude models by Anthropic in Amazon Bedrock.
• Amazon SageMaker Unified Studio adds private VPC connectivity with AWS PrivateLink – Amazon SageMaker Unified Studio now supports AWS PrivateLink, providing private connectivity between your VPC and SageMaker Unified Studio without routing customer data over the public internet. With SageMaker service endpoints onboarded into a VPC, data traffic remains within the AWS network and is governed by IAM policies, supporting stricter security and compliance requirements.
• Amazon S3 adds support for changing object encryption without data movement – Amazon S3 now supports changing the server-side encryption type of existing encrypted objects without moving or re-uploading data. Using the UpdateObjectEncryption API, you can switch from SSE-S3 to SSE-KMS, rotate customer -managed AWS Key Management Service (AWS KMS) keys, or standardize encryption across buckets at scale with S3 Batch Operations while preserving object properties and lifecycle eligibility.
• Amazon Keyspaces introduces table pre-warming for predictable high-throughput workloads – Amazon Keyspaces (for Apache Cassandra) now supports table pre-warming, which helps you proactively set warm throughput levels so tables can handle high read and write traffic instantly without cold-start delays. Pre-warming helps reduce throttling during sudden traffic spikes, such as product launches or sales events, and works with both on-demand and provisioned capacity modes, including multi-Region tables. The feature supports consistent, low-latency performance while giving you more control over throughput readiness.
• Amazon DynamoDB MRSC global tables integrate with AWS Fault Injection Service – Amazon DynamoDB multi-Region strong consistency (MRSC) global tables now integrate with AWS Fault Injection Service. With this integration, you can simulate Regional failures, test replication behavior, and validate application resiliency for strongly consistent, multi-Region workloads.

Additional updates

Here are some additional projects, blog posts, and news items that I found interesting:

• Building zero-trust access across multi-account AWS environments with AWS Verified Access – This post walks through how to implement AWS Verified Access in a centralized, shared-services architecture. It shows how to integrate with AWS IAM Identity Center and AWS Resource Access Manager (AWS RAM) to apply zero trust access controls at the application layer and reduce operational overhead across multi-account AWS environments.
• Amazon EventBridge increases event payload size to 1 MB – Amazon EventBridge now supports event payloads up to 1 MB, an increase from the previous 256 KB limit. This update helps event-driven architectures carry richer context in a single event, including complex JSON structures, telemetry data, and machine learning (ML) or generative AI outputs, without splitting payloads or relying on external storage.
• AWS MCP Server adds deployment agent SOPs (preview) – AWS introduced deployment standard operating procedures (SOPs) that AI agents can deploy web applications to AWS from a single natural language prompt in MCP -compatible integrated development environments (IDEs) and command line interfaces (CLIs) such as Kiro, Cursor, and Claude Code. The agent generates AWS Cloud Development Kit (AWS CDK) infrastructure, deploys AWS CloudFormation stacks, and sets up continuous integration and continuous delivery (CI/CD) workflows following AWS best practices. The preview supports frameworks including React, Vue.js, Angular, and Next.js.
• AWS Network Firewall adds generation AI traffic visibility with web category filtering – AWS Network Firewall now provides visibility into generative AI application traffic through predefined web categories. You can use these categories directly in firewall rules to govern access to generative AI tools and other web services. When combined with TLS inspection, category-based filtering can be applied at the full URL level.
• AWS Lambda adds enhanced observability for Kafka event source mappings – AWS Lambda introduced enhanced observability for Kafka event source mappings, providing Amazon CloudWatch Logs and metrics to monitor event polling configuration, scaling behavior, and event processing state. The update improves visibility into Kafka-based Lambda workloads, helping teams diagnose configuration issues, permission errors, and function failures more efficiently. The capability supports both Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka event sources.
• AWS CloudFormation 2025 year in review – This year-in-review post highlights CloudFormation updates delivered throughout 2025, with a focus on early validation, safer deployments, and improved developer workflows. It covers enhancements such as improved troubleshooting, drift-aware change sets, stack refactoring, StackSets updates, and new -IDE and AI -assisted tooling, including the CloudFormation language server and the Infrastructure as Code (IaC) MCP server.

Upcoming AWS events

Check your calendars so that you can sign up for this upcoming event:

AWS Community Day Romania (April 23–24, 2026) – This community-led AWS event brings together developers, architects, entrepreneurs, and students for more than 10 professional sessions delivered by AWS Heroes, Solutions Architects, and industry experts. Attendees can expect expert-led technical talks, insights from speakers with global conference experience, and opportunities to connect during dedicated networking breaks, all hosted at a premium venue designed to support collaboration and community engagement.

If you’re looking for more ways to stay connected beyond this event, join the AWS Builder Center to learn, build, and connect with builders in the AWS community.

Check back next Monday for another Weekly Roundup.

–betty

The post AWS Weekly Roundup: Amazon Bedrock agent workflows, Amazon SageMaker private connectivity, and more (February 2, 2026) appeared first on The NOKIA Blog.

Today, I’m excited to announce that Amazon S3 Vectors is now generally available with significantly increased scale and production-grade performance capabilities. S3 Vectors is the first cloud object storage with native support to store and query vector data. You can use it to help you reduce the total cost of storing and querying vectors by up to 90% when compared to specialized vector database solutions.

Since we announced the preview of S3 Vectors in July, I’ve been impressed by how quickly you adopted this new capability to store and query vector data. In just over four months, you created over 250,000 vector indexes and ingested more than 40 billion vectors, performing over 1 billion queries (as of November 28th).

You can now store and search across up to 2 billion vectors in a single index, that’s up to 20 trillion vectors in a vector bucket and a 40x increase from 50 million per index during preview. This means that you can consolidate your entire vector dataset into one index, removing the need to shard across multiple smaller indexes or implement complex query federation logic.

Query performance has been optimized. Infrequent queries continue to return results in under one second, with more frequent queries now resulting in latencies around 100ms or less, making it well-suited for interactive applications such as conversational AI and multi-agent workflows. You can also retrieve up to 100 search results per query, up from 30 previously, providing more comprehensive context for retrieval augmented generation (RAG) applications.

The write performance has also improved substantially, with support for up to 1,000 PUT transactions per second when streaming single-vector updates into your indexes, delivering significantly higher write throughput for small batch sizes. This higher throughput supports workloads where new data must be immediately searchable, helping you ingest small data corpora quickly or handle many concurrent sources writing simultaneously to the same index.

The fully serverless architecture removes infrastructure overhead—there’s no infrastructure to set up or resources to provision. You pay for what you use as you store and query vectors. This AI-ready storage provides you with quick access to any amount of vector data to support your complete AI development lifecycle, from initial experimentation and prototyping through to large-scale production deployments. S3 Vectors now provides the scale and performance needed for production workloads across AI agents, inference, semantic search, and RAG applications.

Two key integrations that were launched in preview are now generally available. You can use S3 Vectors as a vector storage engine for Amazon Bedrock Knowledge Base. In particular, you can use it to build RAG applications with production-grade scale and performance. Moreover, S3 Vectors integration with Amazon OpenSearch is now generally available, so that you can use S3 Vectors as your vector storage layer while using OpenSearch for search and analytics capabilities.

You can now use S3 Vectors in 14 AWS Regions, expanding from five AWS Regions during the preview.

Let’s see how it works

In this post, I demonstrate how to use S3 Vectors through the AWS Console and CLI.

First, I create an S3 Vector bucket and an index.

echo "Creating S3 Vector bucket..."
aws s3vectors create-vector-bucket 
    --vector-bucket-name "$BUCKET_NAME"

echo "Creating vector index..."
aws s3vectors create-index 
    --vector-bucket-name "$BUCKET_NAME" 
    --index-name "$INDEX_NAME" 
    --data-type "float32" 
    --dimension "$DIMENSIONS" 
    --distance-metric "$DISTANCE_METRIC" 
    --metadata-configuration "nonFilterableMetadataKeys=AMAZON_BEDROCK_TEXT,AMAZON_BEDROCK_METADATA"

The dimension metric must match the dimension of the model used to compute the vectors. The distance metric indicates to the algorithm to compute the distance between vectors. S3 Vectors supports cosine and euclidian distances.

I can also use the console to create the bucket. We’ve added the capability to configure encryption parameters at creation time. By default, indexes use the bucket-level encryption, but I can override bucket-level encryption at the index level with a custom AWS Key Management Service (AWS KMS) key.

I also can add tags for the vector bucket and vector index. Tags at the vector index help with access control and cost allocation.

And I can now manage Properties and Permissions directly in the console.

Similarly, I define Non-filterable metadata and I configure Encryption parameters for the vector index.

Next, I create and store the embeddings (vectors). For this demo, I ingest my constant companion: the AWS Style Guide. This is an 800-page document that describes how to write posts, technical documentation, and articles at AWS.

I use Amazon Bedrock Knowledge Bases to ingest the PDF document stored on a general purpose S3 bucket. Amazon Bedrock Knowledge Bases reads the document and splits it in pieces called chunks. Then, it computes the embeddings for each chunk with the Amazon Titan Text Embeddings model and it stores the vectors and their metadata on my newly created vector bucket. The detailed steps for that process are out of the scope of this post, but you can read the instructions in the documentation.

When querying vectors, you can store up to 50 metadata keys per vector, with up to 10 marked as non-filterable. You can use the filterable metadata keys to filter query results based on specific attributes. Therefore, you can combine vector similarity search with metadata conditions to narrow down results. You can also store more non-filterable metadata for larger contextual information. Amazon Bedrock Knowledge Bases computes and stores the vectors. It also adds large metadata (the chunk of the original text). I exclude this metadata from the searchable index.

There are other methods to ingest your vectors. You can try the S3 Vectors Embed CLI, a command line tool that helps you generate embeddings using Amazon Bedrock and store them in S3 Vectors through direct commands. You can also use S3 Vectors as a vector storage engine for OpenSearch.

Now I’m ready to query my vector index. Let’s imagine I wonder how to write “open source”. Is it “open-source”, with a hyphen, or “open source” without a hyphen? Should I use uppercase or not? I want to search the relevant sections of the AWS Style Guide relative to “open source.”

# 1. Create embedding request
echo '{"inputText":"Should I write open source or open-source"}' | base64 | tr -d 'n' > body_encoded.txt

# 2. Compute the embeddings with Amazon Titan Embed model
aws bedrock-runtime invoke-model 
  --model-id amazon.titan-embed-text-v2:0 
  --body "$(cat body_encoded.txt)" 
  embedding.json

# Search the S3 Vectors index for similar chunks
vector_array=$(cat embedding.json | jq '.embedding') && 
aws s3vectors query-vectors 
  --index-arn "$S3_VECTOR_INDEX_ARN" 
  --query-vector "{"float32": $vector_array}" 
  --top-k 3 
  --return-metadata 
  --return-distance | jq -r '.vectors[] | "Distance: (.distance) | Source: (.metadata."x-amz-bedrock-kb-source-uri" | split("/")[-1]) | Text: (.metadata.AMAZON_BEDROCK_TEXT[0:100])..."'

The first result shows this JSON:

        {
            "key": "348e0113-4521-4982-aecd-0ee786fa4d1d",
            "metadata": {
                "x-amz-bedrock-kb-data-source-id": "0SZY6GYPVS",
                "x-amz-bedrock-kb-source-uri": "s3://sst-aws-docs/awsstyleguide.pdf",
                "AMAZON_BEDROCK_METADATA": "{"createDate":"2025-10-21T07:49:38Z","modifiedDate":"2025-10-23T17:41:58Z","source":{"sourceLocation":"s3://sst-aws-docs/awsstyleguide.pdf"",
                "AMAZON_BEDROCK_TEXT": "[redacted] open source (adj., n.) Two words. Use open source as an adjective (for example, open source software), or as a noun (for example, the code throughout this tutorial is open source). Don't use open-source, opensource, or OpenSource. [redacted]",
                "x-amz-bedrock-kb-document-page-number": 98.0
            },
            "distance": 0.63120436668396
        }

It finds the relevant section in the AWS Style Guide. I must write “open source” without a hyphen. It even retrieved the page number in the original document to help me cross-check the suggestion with the relevant paragraph in the source document.

One more thing

S3 Vectors has also expanded its integration capabilities. You can now use AWS CloudFormation to deploy and manage your vector resources, AWS PrivateLink for private network connectivity, and resource tagging for cost allocation and access control.

Pricing and availability

S3 Vectors is now available in 14 AWS Regions, adding Asia Pacific (Mumbai, Seoul, Singapore, Tokyo), Canada (Central), and Europe (Ireland, London, Paris, Stockholm) to the existing five Regions from preview (US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Frankfurt))

Amazon S3 Vectors pricing is based on three dimensions. PUT pricing is calculated based on the logical GB of vectors you upload, where each vector includes its logical vector data, metadata, and key. Storage costs are determined by the total logical storage across your indexes. Query charges include a per-API charge plus a $/TB charge based on your index size (excluding non-filterable metadata). As your index scales beyond 100,000 vectors, you benefit from lower $/TB pricing. As usual, the Amazon S3 pricing page has the details.

To get started with S3 Vectors, visit the Amazon S3 console. You can create vector indexes, start storing your embeddings, and begin building scalable AI applications. For more information, check out the Amazon S3 User Guide or the AWS CLI Command Reference.

I look forward to seeing what you build with these new capabilities. Please share your feedback through AWS re:Post or your usual AWS Support contacts.

— seb

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