April 2026 SP-API Release

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AWS Weekly Roundup: AWS Transform at 1 year, Claude Platform on AWS, EC2 M3 Ultra Mac instances, and more (May 18, 2026)

Just a year ago, we launched AWS Transform for .NET, Mainframe and VMware workloads, the first agentic AI service purpose-built for modernizing enterprise applications at scale. At re:Invent 2025, we introduced AWS Transform custom, which enables organizations to modernize and transform code at scale using AWS-managed and custom transformations. You can upgrade language versions, migrate frameworks, optimize performance, and analyze code bases using transformations that are ready to use or can be customized to meet your organization’s specific requirements. We also introduced full-stack Windows modernization capabilities and Reimagine capabilities and automated testing functionality for mainframe.

In 12 months, thousands of customers migrated hundreds of thousands of servers, saved 1.6+ million hours, and processed 4.5+ billion lines of code with AWS Transform. Celebrating its 1-year anniversary, AWS Transform agents now available in Kiro, Claude, Cursor, and Codex, including the agent builder toolkit Kiro power for building customized transformation agents.

To learn what happened in 12 months, the four things we learned, and how that evolved our roadmap, visit the one-year anniversary blog post.

Last week’s launches
Here are last week’s launches that caught my attention:

• The general availability of Claude Platform on AWS – You can get direct access to Anthropic’s native Claude Platform experience, including APIs, console, and early-access beta features, directly through your existing AWS account, without managing separate accounts, billing, or tracking. Claude Platform on AWS is operated by Anthropic, and customer data is processed outside the AWS security boundary. To learn more, visit the deep dive blog post.
• Amazon EC2 M3 Ultra Mac instances – These instances are built on Apple M3 Ultra Mac Studio computers featuring a 28-core CPU, 60-core GPU, 32-core Neural Engine, and 256GB of unified memory. Compared to EC2 M4 Max Mac instances, M3 Ultra Mac instances provide 2x the unified memory, 1.75x the CPU cores, 1.5x the GPU cores, and 2x the Neural Engine cores, giving Apple developers the headroom to run significantly more Xcode simulators in parallel and accelerate on-device ML workflows to improve product time to market.
• Amazon Redshift RG instances powered by AWS Graviton – These instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift’s custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes.
• Amazon Bedrock Advanced Prompt Optimization – You can optimize your prompts for any model on Bedrock, while comparing your original prompts to your optimized prompts across up to 5 models simultaneously. You can also use this if you are migrating to a new model or just want to get better performance on your current model.
• AWS Security Agent full repository code scanning (preview) – You can use a new capability in AWS Security Agent that performs deep, context-aware security analysis of your entire codebase. When vulnerabilities are found, the scanner generates code remediation—specific fixes tied to the exact file and line—enabling teams to remediate security vulnerabilities faster than ever before. This capability is available at no additional charge for existing AWS Security Agent customers during the preview.
• AWS Interconnect – multicloud connectivity with Oracle Cloud Infrastructure (preview) – You can quickly provision resilient, scalable private connections to other cloud providers using AWS Interconnect – multicloud connectivity. OCI is the latest CSP to adopt the open specification that powers AWS Interconnect. This allows AWS to provide a consistent, simple experience to our customers on OCI (preview), Google Cloud (generally available), and Microsoft Azure (coming later in 2026).

Additional updates
Here are some additional news items that you might find interesting:

• Accelerate AI research and education with Build on Trainium program – Read how the next generation of AI researchers is using Amazon chips to accelerate discovery. AWS invested $110 million to give university researchers access to purpose-built AI chips. AWS Trainium is speeding up AI research at UC Berkeley, MIT, Carnegie Mellon, and more. All research is open source, meaning improvements flow back to the broader developer community.
• A full list of AWS Community Days 2026 – There’s something different about an event where the speakers are your peers, the organizers are volunteers who do this out of passion, and the agenda was shaped by the community itself. That’s exactly what AWS Community Days are, and they’re happening in cities across every continent, every year.
• The Kiro Startups Credit program is back – Thousands of founders applied in the first round, and now applications are open again. Apply to receive up to one year of Kiro Pro+ credits automatically applied to your organization’s AWS account.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— Channy

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Amazon Bedrock introduces new advanced prompt optimization and migration tool

Today, we’re announcing Amazon Bedrock Advanced Prompt Optimization, a new tool that you can use to optimize your prompts for any model on Amazon Bedrock, while comparing your original prompts to optimized prompts across up to 5 models simultaneously. With the new prompt optimization, you can migrate to a new model or improve performance from your current model. You can test them to make sure they see no regressions on known use cases and also improve on underperforming tasks.

The new prompt optimizer takes in your prompt template, example user inputs for the variable values, ground truth answers, and an evaluation metric to use as a guide. You can even use this with multimodal user inputs – it supports png, jpg, and pdf as inputs to your prompt templates so you can optimize prompts for tasks like document and image analysis.

You can also provide an AWS Lambda function, LLM-as-a-judge rubric, or a short natural language description to guide the optimization. The prompt optimizer works in a metric-driven feedback loop to optimize the prompt and resulting model responses for the evaluation metric, and outputs the original and final prompt templates with evaluation scores, cost estimates, and latency.

Bedrock Advanced Prompt Optimization in action
To get started with the new prompt optimization, choose Create prompt optimization on the Advanced Prompt Optimization page of Amazon Bedrock console.

Pick up to 5 inference models for which to optimize your prompts. You can use this if you are migrating to a new model or just want to get better performance on their current model. If you’re changing models, you can select your current model as a baseline and up to 4 other models. If you aren’t changing models, then just select your current model to see before and after optimization.

You should prepare your prompt templates in JSONL format with example user data, ground truth answers, and an evaluation metric or rewriting guidance. For .jsonl files, each JSON object must be on a single line.

{
    "version": "bedrock-2026-05-14",           // required; Fixed value
    "templateId": "string",                    // required
    "promptTemplate": "string",                // required
    "steeringCriteria": ["string"],            // optional
    "customEvaluationMetricLabel": "string",   // required if customLLMJConfig or evaluationMetricLambdaArn is used
    "customLLMJConfig": {                      // optional
        "customLLMJPrompt": "string",          // required if customLLMJConfig present
        "customLLMJModelId": "string"          // required if customLLMJConfig present
    },
    "evaluationMetricLambdaArn": "string",     // optional
    "evaluationSamples": [                     // required
        {
            "inputVariables": [                // required
                {
                    "variableName1": "string",
                    "variableName2": "string"
                }
            ],
            "referenceResponse": "string"      // optional
            "inputVariablesMultimodal": [      // optional
                {
                "Arbitrary_Name": {            // required for your multimodal variable.
                    "type": "string",          // choose from "PDF" or "IMAGE". Acceptable filetypes for IMAGE = png, jpg,  
                    "s3Uri": "string"          // input the S3 path of the file
                }
            ]
        }
    ]
}

You can upload files directly or import prompt templates from Amazon Simple Storage Service (Amazon S3) and set an S3 output location where prompt optimization results and evaluation data will be stored. Then, choose Create optimization.

Amazon Bedrock automatically sends your prompt templates and example data with optional ground truth to your inference models, evaluates the responses with your evaluation metric, then rewrites the prompt in a feedback loop to optimize it for your inference models. You’ll see evaluation results based on your provided metric and your final optimized prompts.

As you noted, you can evaluate prompt quality in three ways: a Lambda function with your own Python scoring logic, LLM-as-a-Judge with a custom rubric, or natural-language steering criteria. You can just choose one per prompt template, but can do multiple prompt templates in a job, so they can use a different method for each prompt template if they want.

• Lambda function — If you have a concrete metric (accuracy, F1, execution accuracy, structured-JSON match, etc.), you can deploy a Lambda function containing your custom scoring logic and configure evaluationMetricS3Uri field of the prompt template. Inside the Lambda, the core is a compute_score implementation that programmatically compares model outputs against reference responses.
• LLM-as-a-Judge — If your task is open-ended (summarization, generation, reasoning explanations) and you want a rubric-based score, you can configure the S3 config file in the customLLMJConfig field of the prompt template to define named metrics with structured instructions and a rating scale. A Bedrock judge model evaluates each prompt-response pair and returns a score with reasoning. The default model is Claude Sonnet 4.6 and you can also select your own from a list of judge models.
• Steering criteria — If you know the qualities you want (brand voice, format, safety constraints) but don’t want to author a full judge prompt, you can define criteria in the input dataset through the steeringCriteria array of the prompt template. Instead of structured metrics with rating scales, you provide free-form natural language criteria that the LLM judge evaluates holistically. If you use this option, then a default LLM-as-a-judge prompt will evaluate the responses and incorporate your steering criteria into the judge prompt. The judge model in this case is Anthropic Claude Sonnet 4.6.

To learn more about how to use the advanced prompt optimization and migration, visit the advanced prompt optimization in Bedrock guide and the sample codes in Github.

Now available
Amazon Bedrock Advanced Prompt Optimization is available today in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Zurich), and South America (São Paulo) Regions. You are charged based on the Bedrock model-inference tokens consumed during optimization, at the same per-token rates as regular Bedrock inference. To learn more, visit the Amazon Bedrock pricing page.

Give the advanced prompt optimization a try in the Amazon Bedrock console or with CreateAdvancedPromptOptimizationJob API today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

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Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine

Since 2013, Amazon Redshift has given the full power of a data warehouse in the cloud, at a fraction of the on-premises cost. Every architectural generation—from dense compute to Amazon RA3 instances, from provisioned to Amazon Redshift Serverless—has made each query cheaper, faster, and more efficient than the last.

For over a decade, as data volumes have grown and analytics requirements have evolved, organizations increasingly leverage both data warehouse tables for structured, frequently-accessed data and data lakes for cost-effective storage of diverse datasets. Add AI agents to the mix and they query your data warehouse at a scale that dwarfs typical human usage, leading to spiraling operational costs.

Amazon Redshift has doubled down on its core strengths to meet the demands of any workload — whether driven by humans or AI agents. For example, in March 2026, Amazon Redshift improved the performance of business intelligence (BI) dashboards and ETL workloads by speeding up new queries by up to 7 times. This significantly improves the response times of low-latency SQL queries, such as those used in near-real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Today, we’re announcing Amazon Redshift RG instances, a new instance family powered by AWS Graviton. RG instances deliver better performance, running data warehouse workloads up to 2.2x as fast as RA3 instances at 30% lower price per vCPU. Their integrated data lake query engine lets you run SQL analytics across your data warehouse and data lake from a single engine with performance up to 2.4x as fast as RA3 for Apache Iceberg and up to 1.5x as fast as RA3 for Apache Parquet. This blend of speed, cost efficiency, and an integrated data lake query engine makes Redshift RG instances well-suited to handle the high query volumes and low-latency requirements of today’s analytics and agentic AI workloads.

You can compare new RG instances and current RA3 instances:

Current RA3 Instance	Recommended RG instance	vCPU	Memory (GB)	Primary Use Case
ra3.xlplus	rg.xlarge	4	32	Small cluster departmental analytics
ra3.4xlarge	rg.4xlarge	12 → 16 (1.33:1)	96 GB → 128 GB (1.33:1)	Standard production workloads, medium data volumes

This approach reduces total analytics costs for customers running combined data warehouse and data lake workloads, while simplifying operations through a single system for querying both warehouse tables and Amazon Simple Storage Service (Amazon S3) data lakes. We recommend using the AWS Pricing Calculator with your specific workload patterns to estimate savings.

Getting started with Amazon Redshift RG instances
You can launch new clusters or migrate existing clusters through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS API. The integrated data lake query engine is enabled by default.

In the Amazon Redshift console, you can choose new RG instances when you create a cluster.

You can migrate previous-generation instances to RG instances with optimal paths based on your cluster configuration to estimate costs, validate compatibility, and automate execution.

• Elastic Resize—in-place migration with 10-15 minutes downtime for compatible configurations
• Snapshot and Restore—create a RG cluster from an RA3 snapshot. This is best for customers who want to make configuration changes during the migration

Your external tables, schemas, and query syntax—including existing Spectrum queries—remain unchanged. There is no need to recreate external tables or modify application code. To learn more, visit the Redshift Management Guide.

Amazon Redshift now executes data lake queries on cluster nodes—the same compute that processes data warehouse workloads. As a result, Amazon Redshift Spectrum is no longer required. Data lake queries stay within your VPC boundary, use existing IAM roles, and incur zero per-terabyte scanning charges. This removes the $5/TB Spectrum scanning fees that previously added to total Redshift costs.

Now available
Amazon Redshift RG instances are now available in the following AWS Regions: US East (N. Virginia, Ohio), US West (N. California, Oregon), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Taiwan, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, Milan, London, Paris, Spain, Stockholm), Middle East (UAE), and South America (São Paulo). For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For Redshift Provisioned, you can select On-Demand Instances with hourly billing and no commitments or choose Reserved Instances for cost savings. To learn more, visit the Amazon Redshift Pricing page.

Give RG instances a try in the Redshift console and send feedback to AWS re:Post for Amazon Redshift or through your usual AWS Support contacts.

— Channy

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AWS Weekly Roundup: Amazon Bedrock AgentCore payments, Agent Toolkit for AWS, and more (May 11, 2026)

My most exciting news of last week: Amazon Bedrock AgentCore previewed the first managed payment capabilities enabling AI agents to autonomously access and pay for APIs, MCP servers, web content, and other agents. Built in partnership with Coinbase and Stripe, it removes the undifferentiated heavy lifting of building customized systems for billing, credential management, and compliance.

You can connect a Coinbase CDP wallet or Stripe Privy wallet as a payment connection, set session-level spending limits, and your agent transacts autonomously during execution. What excites me most is what AgentCore payments can unlock—like a research agent that can pay for real-time market data on the fly, or a coding agent calling paid APIs mid-task.

To learn more, visit the blog post, dive deeper using the documentation, and get started with the AgentCore CLI.

Last week’s launches
Here are last week’s launches that caught my attention:

• Agent Toolkit for AWS – A production-ready suite of tools and guidance, available at no additional charge, that helps AI coding agents build on AWS with fewer errors, lower token costs, and enterprise-grade security controls. The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs. To get started, visit the quick start guide or browse the available skills and plugins on GitHub.
• AWS MCP Server GA – You can use 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. It is part of the Agent Toolkit for AWS. To learn more, visit Seb Stormacq’s blog post.
• Amazon WorkSpaces for AI agents (Preview) – You can use AI agents to securely access and operate desktop applications through managed WorkSpaces environments. This capability allows organizations to automate everyday workflows at scale while maintaining full enterprise-grade governance and compliance. To learn more, visit Micah Walter’s blog post.
• Amazon EC2 M8idn/M8idb and R8idn/R8idb instances – These instances are powered by custom sixth-generation Intel Xeon Scalable processors available only on AWS and the latest sixth-generation AWS Nitro cards. These instances deliver up to 43% better compute performance per vCPU compared to previous-generation instances. M8idn/R8idn instances offer up to 600 Gbps network bandwidth, and M8idb/R8idb instances deliver up to 300 Gbps EBS bandwidth.

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

Additional updates
Here are some additional news items that you might find interesting:

• Valkey turns two – Valkey stands as proof that open, community-driven technology innovates faster, scales further, and delivers more value than any single-vendor model. Valkey has surpassed 100 million Docker pulls (up 17x year over year) and attracted more than 225 contributors who have submitted over 1,500 pull requests, roughly double the development pace of Redis over the same period. You can also use the latest Valkey 9.0 in Amazon ElastiCache.
• Query billion-scale vectors with SQL – You can learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with relational filters in a single query, for example, finding the most semantically similar products and then filtering by price, stock status, or tenant in one SQL statement.
• Building an end-to-end agentic SRE using AWS DevOps Agent – Learn how to configure DevOps Agent Spaces that define an investigation scope, integrating seamlessly with Amazon CloudWatch, Splunk, GitHub, and Slack. You can also learn how to trigger automated investigations via webhooks, generate mitigation plans, and hand off agent-ready specs to coding agents like Kiro for implementation.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— Channy

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Update: May updates to listing attribute usage and enumeration values

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The AWS MCP Server is now generally available

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"]}'

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

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Modernize your workflows: Amazon WorkSpaces now gives AI agents their own desktop (preview)

Enterprises face a significant challenge when deploying AI agents: the desktop and legacy applications that power most business workflows are simply inaccessible to modern AI systems. According to a 2024 Gartner report, 75% of organizations run legacy applications that lack modern APIs, and 71% of Fortune 500 companies operate critical processes on mainframe systems without adequate programmatic access. For many organizations, this has meant choosing between delaying AI adoption or undertaking expensive and risky modernization projects.

Today, we are announcing that Amazon WorkSpaces now enables AI agents to securely operate desktop applications without requiring application modernization. The same managed virtual desktops that millions of employees use and trust can now also serve AI agents, turning WorkSpaces into infrastructure for scaling enterprise productivity, not just delivering it. Because agents operate within your existing WorkSpaces environment, there are no APIs to build, no application migrations to plan, and no new infrastructure to manage.

Some of our customers had an early opportunity to give their agents a WorkSpace. Chris Noon, Director, Nuvens Consulting shared with us, “WorkSpaces lets our clients give AI agents the same secure, governed desktop environment their employees already use — no custom API integrations, full audit trails, and enterprise-grade isolation out of the box. For regulated industries, that’s not a nice-to-have — it’s the baseline.”

Secure cloud desktop access for AI agents
With WorkSpaces, AI agents can securely access and operate desktop applications running inside managed WorkSpaces environments to complete complex business workflows. Agents authenticate through AWS Identity and Access Management (IAM) and connect via Workspaces with complete audit trails available through AWS CloudTrail and Amazon CloudWatch. Because agents operate within secure WorkSpaces environments rather than on local machines, your existing security controls and compliance policies remain fully intact.

Amazon Workspaces supports the industry-standard Model Context Protocol (MCP), which means WorkSpaces works with any agent framework, such as LangChain, CrewAI and Strands Agents.

Let’s try it out
To set up a WorkSpaces environment for AI agents, I started in the AWS Management Console by creating a new WorkSpaces Applications stack—the environment definition that controls how agents connect and what they’re allowed to do.

From the Amazon WorkSpaces console, I chose Create stack and configured the basics: name, fleet association, and VPC endpoints. In Step 3 of the stack creation workflow, I noticed the new AI agents section with two options. The first, No AI agent access, is the default configuration for standard WorkSpaces designed for people. The second, Add AI Agents, allows AI agents to securely access and operate applications using their own identity and permissions. I selected Add AI Agents to enable agent connections on this stack.

Next, I will enable storage before configuring the agent access settings to define how agents interact with the desktop.

Under Agent features, I enabled three capabilities. Computer input allows the agent to click, type, and scroll within the desktop. Computer vision allows the agent to capture screenshots of the desktop, which is how it “sees” the application. Finally, screenshot storage configures where session screenshots are stored for audit and debugging.

Under Desktop screen layout, I set the screen resolution to 1280×720 and image format to PNG. The resolution determines the fidelity of what the agent sees during a session—a complex application with dense UI elements might benefit from higher resolution, while a terminal-style interface works well at 720p.

With my stack configured, WorkSpaces exposes a managed MCP endpoint. I pointed my agent framework to this endpoint, provided IAM credentials for authentication, and my agent began interacting with the desktop applications installed on the fleet’s image.

To see this in action, here’s an agent built with the Strands Agent SDK and Amazon Bedrock handling a prescription refill, looking up the patient record, searching for the medication, placing the order, and confirming a successful refill, all inside a sample pharmacy system with no API.

The application doesn’t know an agent is driving it. Nothing about the software was modified, rebuilt, or integrated. The agent worked with it exactly as it exists today.

Now available
This feature is available today in public preview at no additional cost in US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Frankfurt, Ireland, Paris), and Asia (Tokyo, Mumbai, Sydney, Seoul, Singapore) Regions.

Get started building today using our GitHub repo, or visit the WorkSpaces page for more details.

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AWS Weekly Roundup: What’s Next with AWS 2026, Amazon Quick, OpenAI partnership, and more (May 4, 2026)

Last week, I took some time off in York, England, often described as the most haunted city in the country. I wandered through the ruins of abbeys that have stood for nearly a thousand years, walked along medieval walls, and spent an evening on a ghost tour hearing stories passed down through centuries. There’s something grounding about standing in a place that has witnessed so much history. Now I’m back at my desk, and the contrast is hard to miss: those abbey stones have stood for a thousand years largely unchanged, while in the span of a single week away, the pace of technological change has moved forward yet again.

The ruins of Whitby Abbey in North Yorkshire. Stones that have seen a thousand years, while this week alone brought another wave of change.

Now, let’s get into this week’s AWS news.

Headlines
On April 28, Matt Garman, CEO of AWS, Colleen Aubrey, SVP Amazon Applied AI Solutions, Julia White, CMO of AWS, and OpenAI leaders took the stage to share how customers are changing the way businesses operate with agents. The event brought a packed slate of announcements across Amazon Quick, Amazon Connect, and a deeper partnership with OpenAI. Here’s a roundup of the biggest announcements from the event.

Amazon Quick expands with a desktop app, new pricing plans, and visual asset generation – Amazon Quick is an AI assistant for work that connects to your apps, learns what matters to you, and takes action on your behalf. This week, Quick introduced a new desktop app (Preview) that keeps you connected to your local files, calendar, and communications without opening a browser. You can sign up within minutes using your personal email address or existing Google, Apple, Github, or Amazon credentials—no AWS account required. Quick can now generate polished documents, presentations, infographics, and images directly from the chat interface, and native integrations expand to include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams. A new Build custom apps with Quick capability (Preview) lets you create intelligent apps, dashboards, and web pages connected to the rest of your business using natural language.

Amazon Connect expands into four agentic AI solutions – Amazon Connect is expanding from a single product into a set of four agentic AI solutions designed to work within your existing workflows. Amazon Connect Decisions is a supply chain planning and intelligence solution that shifts teams from crisis management to proactive planning, combining 30 years of Amazon operational science with more than 25 specialized supply chain tools. Amazon Connect Talent (Preview) is an agentic AI hiring solution that delivers AI-led interviews, science-backed assessments, and consistent evaluation for talent acquisition leaders managing scaled hiring. Amazon Connect Customer, previously known as Amazon Connect, delivers personalized customer experiences across voice, chat, and digital channels, with new configuration capabilities that enable organizations to set up conversational AI in weeks rather than months. Amazon Connect Health delivers agentic patient verification, appointment management, patient insights, ambient documentation, and medical coding, giving patients faster access to care and clinicians more time to deliver it.

AWS and OpenAI expand their partnership across Amazon Bedrock – AWS and OpenAI are bringing the latest OpenAI models to Amazon Bedrock, launching Codex on Amazon Bedrock, and introducing Amazon Bedrock Managed Agents powered by OpenAI — all in limited preview. OpenAI models on Amazon Bedrock (Limited preview) brings the latest OpenAI models, including GPT-5.5 and GPT-5.4, to the Bedrock APIs you already use, with unified security, governance, and cost controls. No additional infrastructure to configure, no new security model to learn. Codex on Amazon Bedrock (Limited preview) lets you access the OpenAI coding agent within your existing AWS environments, authenticating with your AWS credentials, processing inference through Bedrock, and applying Codex usage toward your AWS cloud commitments. Codex on Bedrock is available through the Bedrock API, starting with the Codex CLI, the Codex desktop app, and a Visual Studio Code extension. Amazon Bedrock Managed Agents, powered by OpenAI (Limited preview) combines OpenAI frontier models with AWS infrastructure to build production-ready OpenAI-powered agents in the cloud, built with the OpenAI harness for faster execution, sharper reasoning, and reliable steering of long-running tasks.

To learn more, visit Top announcements of the What’s Next with AWS, 2026.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Amazon EC2 M8in and M8ib instances are now generally available – Powered by custom 6th-gen Intel Xeon Scalable processors and 6th-gen AWS Nitro cards, these instances deliver up to 43% higher performance over M6in and M6ib. M8in offers 600 Gbps network bandwidth, while M8ib delivers up to 300 Gbps EBS bandwidth. Available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain).
• Amazon EC2 R8in and R8ib instances are now generally available – Memory-optimized instances built on the same 6th-gen Intel Xeon Scalable processors and Nitro cards, with the same 600 Gbps network and 300 Gbps EBS bandwidth profiles. Well-suited for large commercial databases, data lakes, and in-memory databases such as SAP HANA. Available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Spain).
• Amazon EC2 C8ine and M8ine instances are now generally available – Network-optimized instances offering up to 2.5x higher packet performance per vCPU and up to 2x higher network throughput for traffic through internet gateways compared to C6in and M6in. Designed for security and network virtual appliances including virtual firewalls, load balancers, and 5G UPF workloads. Available in US East (N. Virginia), US West (Oregon), and Asia Pacific (Tokyo) for C8ine; US East (N. Virginia) and US West (Oregon) for M8ine.
• Amazon Bedrock AgentCore adds optimization capabilities (Preview) – AgentCore now offers recommendations, batch evaluations, and A/B tests to complete the observe-evaluate-improve loop for agents in production. Recommendations analyze production traces and evaluation outputs to propose optimized system prompts and tool descriptions, which you can validate with batch evaluations against pre-defined test cases or A/B tests against live traffic. Every recommendation requires your approval before it ships.
• AWS Lambda adds support for Ruby 4.0 – Ruby 4.0, the latest LTS release, is available as a Lambda managed runtime and container base image. It includes support for Lambda advanced logging controls, including JSON structured logs, configurable logging levels, and target CloudWatch log group configuration. Available in all AWS Regions, including China Regions and AWS GovCloud (US).

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 Q Developer end-of-support announcement – Amazon Q Developer IDE plugins and paid subscriptions will reach end of support on April 30, 2027, giving customers 12 months to transition to Kiro. New signups will be blocked starting May 15, 2026, although existing subscriptions can continue to add users. Starting May 29, 2026, Opus 4.6 will no longer be available on Q Developer Pro; Opus 4.5 and other existing models remain available, and the latest coding models including Opus 4.7 are available exclusively on Kiro. Amazon Q Developer in the AWS Management Console and first-party AWS experiences (documentation, mobile app, Slack, and Microsoft Teams) are not affected.
• AWS 10,000 AIdeas Competition: Meet the Winners – AWS announced the 20 winners of the 10,000 AIdeas Competition, a global challenge where builders submitted AI applications built entirely with Kiro and the AWS Free Tier, with submissions from 115 countries narrowed down through four rounds of evaluation and two rounds of community voting. Winners span Global Champions, Regional Champions, Innovation Awards, and Creative Track categories, with cash prizes and AWS credits awarded across each tier.
• AWS Student Builder Groups – AWS Cloud Clubs is evolving to AWS Student Builder Groups. The community now spans 600+ colleges and universities across 63 countries. Existing Cloud Club memberships, badges, and progress carry forward, and Cloud Club Captains become Group Leaders. Membership is open to any learner 18 or older. You can find a group near you on AWS Builder Center or apply to launch a new group on your campus.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free in-person events covering cloud and AI. Coming up in May: Singapore (May 6), Tel Aviv (May 6), Warsaw (May 6), Stockholm (May 7), Sydney (May 13–14), Hamburg (May 20), Seoul (May 20), Amsterdam (May 27), Milano (May 28), and Mumbai (May 28).
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include İstanbul, Türkiye (May 9) and Panama City, Panama (May 23).

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building. You can also browse upcoming AWS-led in-person and virtual events, plus developer-focused sessions.

— Esra

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

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Top announcements of the What’s Next with AWS, 2026

Today at the What’s Next with AWS, Matt Garman, CEO of AWS, Colleen Aubrey, SVP Amazon Applied AI Solutions, Julia White, CMO of AWS, and OpenAI leaders discussed how they and their customers are changing how businesses operate with agents.

Here’s our roundup of the biggest announcements from the event:

Amazon Quick is an AI assistant for work that connects to all of them, learns what matters to you, and takes action on your behalf. Starting today, you can use the new desktop app, sign up for Free and Plus pricing plans, generate visual assets in the chat, and easily connect Quick to even more apps.

• Quick’s new desktop app (Preview): You can create a personalized experience by staying connected to your local files, calendar, and communications without opening a browser.
• New Free and Plus pricing plans for Quick: You can sign up within minutes using your personal email address or existing Google, Apple, Github, or Amazon credentials—no AWS account required.
• Generate visual assets on the fly: Available today, Quick now lets you create polished documents, presentations, infographics, and images directly from the chat interface, no design skills or hours of formatting required.
• Easily connect Quick to even more apps: Also available today, Quick is expanding its native integrations to include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams.

To learn more, visit the About Amazon News post.

Amazon Connect is expanding from a single product into a set of four agentic AI solutions designed to work within your existing workflows: Amazon Connect Decisions (supply chains), Talent (hiring), Customer (customer experience), and Health (health care).

• Amazon Connect Decisions is a supply chain planning and intelligence solution that shifts teams from crisis management to proactive planning and decisioning. AI teammates, combining 30 years of Amazon operational science and 25+ specialized supply chain tools, adapt to your business, learn from your team, and continuously improve your operations.
• Amazon Connect Talent (Preview) is an agentic AI hiring solution built for talent acquisition leaders managing scaled hiring. It delivers AI-led interviews, science-backed assessments, and consistent evaluation, helping recruiters hire high quality candidates faster while providing applicants with a flexible interview experience that reduces human preconceptions.
• Amazon Connect Customer, previously known as Amazon Connect, delivers intelligent, personalized customer experiences across voice, chat, and digital channels. Amazon Connect Customer now offers new configuration capabilities that enable organizations to set up conversational AI in weeks, not months, and configure experiences without technical expertise.
• Amazon Connect Health delivers agentic patient verification, appointment management, patient insights, ambient documentation, and medical coding — giving patients faster access to care, clinicians more time for care, and staff capacity for specialized work.

To learn more, visit the About Amazon News post.

AWS and OpenAI extended partnership
AWS and OpenAI are bringing the latest OpenAI models to Amazon Bedrock, launching Codex on Amazon Bedrock, and launching Amazon Bedrock Managed Agents, powered by OpenAI (all in limited preview), giving enterprises the frontier intelligence they want on the infrastructure they trust.

• OpenAI models on Amazon Bedrock (Limited preview): The latest OpenAI models, including GPT-5.5 and GPT-5.4, will be available in preview on Amazon Bedrock. Use OpenAI’s frontier models through the same Bedrock APIs you already rely on, with unified security, governance, and cost controls. No additional infrastructure to configure, no new security model to learn.
• Codex on Amazon Bedrock (Limited preview): You can access the OpenAI coding agent within the AWS environments where they already operate at scale. You can authenticate using their AWS credentials, process inference through Amazon Bedrock infrastructure, and apply Codex usage toward their AWS cloud commitments. Codex on Bedrock is available through the Bedrock API, starting with the Codex CLI, the Codex desktop app, and Visual Studio Code extension.
• Amazon Bedrock Managed Agents, powered by OpenAI (Limited preview): Amazon Bedrock Managed Agents combines frontier AI models with trusted AWS infrastructure, enabling customers to quickly and easily build production-ready OpenAI-powered agents in the cloud. It is built with the OpenAI harness, which is engineered to unlock the full potential of OpenAI frontier models, delivering faster execution, sharper reasoning, and reliable steering of long-running tasks.

To learn more, visit the AWS What’s New post and About Amazon News post.

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AWS Weekly Roundup: Anthropic & Meta partnership, AWS Lambda S3 Files, Amazon Bedrock AgentCore CLI, and more (April 27, 2026)

Late March took me to Seattle for the Specialist Tech Conference, one of the most energizing gatherings of AWS specialists from around the world. It was an incredible opportunity to connect with peers, exchange experiences, and go deep on the latest advancements in Generative AI and Amazon Bedrock — and a powerful reminder of something I truly believe in: when specialists come together to challenge each other, explore edge cases, and co-create solutions, the impact goes far beyond the meeting room. In a fast-moving space like AI, having a strong internal community isn’t a nice-to-have — it’s a competitive advantage.

Now, let’s get into this week’s AWS news…

Headlines

Anthropic partnership: Claude on AWS Trainium and Graviton, and Claude Cowork in Amazon Bedrock – This week, AWS and Anthropic deepened their product collaboration in meaningful ways for builders. Anthropic is now training its most advanced foundation models on AWS Trainium and Graviton infrastructure, co-engineering directly at the silicon level with Annapurna Labs to maximize computational efficiency from the hardware up through the full stack.

Claude Cowork is now available in Amazon Bedrock — Claude Cowork brings Anthropic’s collaborative AI capabilities directly to enterprise builders within the AWS ecosystem, enabling teams to work alongside Claude as a true collaborator, not just a tool. You can now deploy Claude Cowork within your existing Amazon Bedrock environment, keeping your data secure within AWS while leveraging the full power of Claude for team-based AI workflows.

Claude Platform on AWS (Coming soon) — A unified developer experience to build, deploy, and scale Claude-powered applications without leaving AWS. If you’re building with Generative AI on AWS, this is a significant step forward in what you’ll be able to do with Claude directly through Amazon Bedrock.

Meta signs agreement with AWS to power agentic AI on Amazon’s Graviton chips — Meta has signed an agreement to deploy AWS Graviton processors at scale, starting with tens of millions of Graviton cores to power CPU-intensive agentic AI workloads — including real-time reasoning, code generation, search, and multi-step task orchestration.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• AWS Lambda functions can now mount Amazon S3 buckets as file systems with S3 Files — You can now mount Amazon S3 buckets as file systems in AWS Lambda using S3 Files, enabling your functions to perform standard file operations without downloading data for processing. Built on Amazon EFS, S3 Files provides the simplicity of a file system with the scalability, durability, and cost-effectiveness of S3 — and multiple Lambda functions can connect to the same file system simultaneously, sharing data through a common workspace. This is particularly valuable for AI and machine learning workloads where agents need to persist memory and share state across pipeline steps.
• Amazon EKS Hybrid Nodes gateway for hybrid Kubernetes networking — Amazon Elastic Kubernetes Service now offers the Amazon EKS Hybrid Nodes gateway, which automates networking between your EKS cluster VPC and Kubernetes Pods running on EKS Hybrid Nodes. You can now eliminate the need to make on-premises pod networks routable or coordinate network infrastructure changes, greatly simplifying hybrid Kubernetes environments. The gateway automatically enables pod-to-pod traffic across cloud and on-premises environments, control plane-to-webhook communication, and connectivity for AWS services like Application Load Balancers, and is available at no additional charge.
• Amazon Aurora Serverless: Up to 30% better performance, smarter scaling, and still scales to zero — Amazon Aurora Serverless just got faster and smarter, with up to 30% better performance than the previous version and an enhanced scaling algorithm designed to handle workloads where multiple tasks compete for resources — like busy APIs and agentic AI applications with bursts of activity and long idle windows. You can now run even more demanding workloads serverlessly, paying only for what you use, and automatically scaling to zero when not in use. All improvements are available in platform version 4 at no additional cost.
• Amazon Bedrock AgentCore adds new features to help developers build agents faster — Amazon Bedrock AgentCore introduces a managed harness (preview), the AgentCore CLI, and AgentCore skills for coding assistants, helping developers go from idea to working agent prototype faster. The managed harness lets you define an agent by specifying a model, system prompt, and tools and run it immediately with no orchestration code required — and when you’re ready for full control, you can export the harness orchestration as Strands-based code. The AgentCore CLI deploys your agents with the governance and auditability of infrastructure-as-code (AWS CDK today, Terraform coming soon), and is available in 14 AWS Regions at no additional charge.

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:

• Introducing granular cost attribution for Amazon Bedrock — This post walks through how Amazon Bedrock’s granular cost attribution works and covers practical example cost tracking scenarios. You can now tag and track Bedrock usage costs at a finer level of detail — useful for organizations running multiple teams or projects on Bedrock who need precise cost visibility and chargeback capabilities.
• Automating Incident Investigation with AWS DevOps Agent and Salesforce MCP Server — This post (co-written with Salesforce) shows how AWS DevOps Agent, integrated with the Salesforce MCP Server, automates the full lifecycle of infrastructure incident investigation — from identifying issues and diagnosing root causes to notifying customers through Salesforce Service Cloud. It’s a compelling real-world example of how AI agents and MCP-based tool connectivity are reshaping DevOps workflows in production, dramatically reducing mean time to resolution.
• Microcredentials from AWS are now free — Here’s why that matters — You can now access AWS microcredentials at no cost through AWS Skill Builder in all countries where the platform is offered. Unlike traditional multiple-choice certifications, microcredentials are hands-on assessments that place builders in simulated business scenarios where they configure, troubleshoot, and optimize directly in a live AWS environment — the same way they would on the job. A great opportunity to validate real cloud skills without a cost barrier.
• Amazon SageMaker AI now supports optimized generative AI inference recommendations — You can now use Amazon SageMaker AI to automatically identify optimized deployment configurations for your generative AI models, including instance type, container, and inference parameters. This new capability takes the guesswork out of tuning inference infrastructure, helping you reduce costs and improve latency for your AI applications in production.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• What’s Next with AWS — Tune in on April 28 for What’s Next with AWS, a virtual event featuring the latest announcements and product updates directly from AWS teams. A great opportunity to get up to speed on what’s new before diving into the week’s launches.
• AWS Summits — AWS Summits are free in-person events where you can explore the latest in cloud and AI innovation, learn best practices, and network with builders and experts. Coming up in May: Singapore (May 6), Tel Aviv (May 6), Warsaw (May 6), Stockholm (May 7), Sydney (May 13–14), Hamburg (May 20), Seoul (May 20), Amsterdam (May 27), Bangkok (May 28), Milan (May 28), and Mumbai (May 28). And in June, join us in Los Angeles (June 10). Check the full schedule and register at the link above.
• AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Athens, Greece (April 28), Vancouver, Canada (May 1), İstanbul, Türkiye (May 9), and Panama City, Panama (May 23). If you’re in Latin America, mark your calendar for the AWS Community Day Belo Horizonte (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!

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AWS Weekly Roundup: Claude Opus 4.7 in Amazon Bedrock, AWS Interconnect GA, and more (April 20, 2026)

Last week I had the honor of delivering a commencement speech at the University of Namur (uNamur) for their 2025 graduation ceremony.

Standing in front of freshly minted computer science graduates, I talked about the future of software development in the age of AI. My message to them was simple: AI will not make you obsolete. We’ve seen tools evolve over the decades, from punch cards to IDEs to AI-assisted coding, but the work remains yours, not the tool’s. The developers who will thrive are those who stay curious, think in systems, communicate with precision, and take ownership of what they build. The world needs more people with coding skills, not fewer. AI raises the bar on what we can accomplish, and that’s a good thing.

Now, let’s get into this week’s AWS news.

Headlines
Anthropic’s Claude Opus 4.7 is now available in Amazon Bedrock – Anthropic’s most intelligent Opus model is now available in Amazon Bedrock, with improved performance across coding, long-running agents, and professional knowledge work. Claude Opus 4.7 scores 64.3% on SWE-bench Pro and 87.6% on SWE-bench Verified, extending its lead in agentic coding with stronger long-horizon autonomy and complex code reasoning. It also does better on knowledge work tasks like document creation, financial analysis, and multi-step research.

The model runs on Bedrock’s next-generation inference engine with dynamic capacity allocation, adaptive thinking (letting Claude allocate thinking token budgets based on request complexity), and the full 1M token context window. It also adds high-resolution image support for better accuracy on charts, dense documents, and screen UIs. Claude Opus 4.7 is available at launch in US East (N. Virginia), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm), with up to 10,000 requests per minute per account per Region.

AWS Interconnect is now generally available with a new option to simplify last-mile connectivity – AWS Interconnect brings two managed private connectivity capabilities to general availability. The first, AWS Interconnect – Multicloud, provides Layer 3 private connections between AWS VPCs and other cloud providers (Google Cloud available now, Azure and OCI coming later in 2026). Traffic flows over the AWS global backbone and the partner cloud’s private network, never over the public internet, with built-in MACsec encryption, multi-facility resiliency, and CloudWatch monitoring. AWS published the underlying specification on GitHub under Apache 2.0 so any cloud provider can become an Interconnect partner.

The second capability, AWS Interconnect – Last Mile, simplifies high-speed private connections from branch offices, data centers, and remote locations to AWS through existing network providers. It provisions 4 redundant connections across 2 physical locations automatically, configures BGP routing, activates MACsec encryption and Jumbo Frames by default, and offers bandwidth from 1 Gbps to 100 Gbps adjustable from the console without reprovisioning. Last Mile launches in US East (N. Virginia) with Lumen as the initial partner.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Amazon ECR pull through cache now supports referrer discovery and sync — ECR’s pull through cache now automatically discovers and syncs OCI referrers (image signatures, SBOMs, attestations) from upstream registries into your private repositories. This means end-to-end image signature verification and SBOM discovery workflows work without client-side workarounds.
• AWS Transform is now available in Kiro and VS Code — AWS Transform, the agentic migration and modernization factory, is now accessible via Kiro (as a Power) and VS Code (as an extension). You can run custom transformations for common patterns like Java/Python/Node.js version upgrades and AWS SDK migrations directly from your IDE, with job state shared across the web console, CLI, and IDE.
• Aurora DSQL launches connector for PHP — A new Aurora DSQL Connector for PHP (PDO_PGSQL) simplifies building PHP applications on Aurora DSQL by automatically generating IAM tokens, handling SSL configuration, managing connection pooling, and providing opt-in optimistic concurrency control retry with exponential backoff.
• Amazon Q supports document-level access controls for Google Drive — Amazon Q now enforces document-level access controls for Google Drive knowledge bases, combining indexed ACL replication for fast pre-retrieval filtering with real-time permission checks against Google Drive at query time.
• AWS Secrets Manager now supports hybrid post-quantum TLS — Secrets Manager now supports hybrid post-quantum key exchange using ML-KEM to protect secrets against both current and future quantum computing threats. This is automatically enabled in Secrets Manager Agent 2.0.0+, Lambda Extension v19+, and the CSI Driver 2.0.0+.
• Amazon EC2 C8in and C8ib instances are now generally available — Powered by custom 6th-gen Intel Xeon Scalable processors and 6th-gen AWS Nitro cards, these instances deliver up to 43% higher performance over C6in. C8in offers 600 Gbps network bandwidth (highest among enhanced networking EC2 instances), while C8ib delivers up to 300 Gbps EBS bandwidth (highest among non-accelerated compute instances), scaling up to 384 vCPUs.

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:

• Navigating enterprise networking challenges with Amazon EKS Auto Mode — This post explains how EKS Auto Mode automates Kubernetes networking infrastructure including VPC CNI configuration, load balancer provisioning, and DNS management, reducing operational overhead while preserving enterprise security controls.
• Introducing granular cost attribution for Amazon Bedrock — My colleague Micah talked about this feature last week already, but the blog post came out after last week’s roundup. Amazon Bedrock now automatically attributes inference costs to the specific IAM principal that made each API call, with results flowing into AWS Cost and Usage Reports (CUR 2.0). You can aggregate costs by team, project, or cost center using IAM principal tags and session tags.
• Accelerate development workflows with Amazon EBS Volume Clones — EBS Volume Clones let you create instant, point-in-time copies of EBS volumes that are immediately usable without waiting for data transfer. The post highlights use cases including dev/test environment refreshes, disaster recovery testing, and CI/CD pipeline acceleration.
• Modernize VB6 applications at scale with AWS Transform Custom — A walkthrough of using AWS Transform Custom’s agentic AI capabilities to convert legacy Visual Basic 6.0 applications to modern C# ASP.NET Core web applications, addressing challenges like COM dependencies, ADO-to-Entity Framework migration, and VB6 forms-to-Blazor UI conversion.
• Migrating and decomposing APIs with zero-downtime using CloudFront — A zero-downtime API migration strategy using CloudFront Functions with CloudFront KeyValueStore for intelligent, user-aware traffic routing based on the Strangler Fig pattern. This is similar to what AWS Migration Hub Refactor Spaces offers, but implemented with just CloudFront and edge functions.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Events — Browse upcoming AWS-led in-person and virtual events, startup events, and developer-focused events near you.
• AWS Power Hour — Weekly live training sessions on Twitch covering various AWS topics.
• Community.aws — Find community-led events, meetups, and user groups in your area.

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

— seb

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