Tag Archives: news

AWS Weekly Roundup: Kiro waitlist, EBS Volume Clones, EC2 Capacity Manager, and more (October 20, 2025)

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-kiro-waitlist-ebs-volume-clones-ec2-capacity-manager-and-more-october-20-2025/

I’ve been inspired by all the activities that tech communities around the world have been hosting and participating in throughout the year. Here in the southern hemisphere we’re starting to dream about our upcoming summer breaks and closing out on some of the activities we’ve initiated this year. The tech community in South Africa is participating in Amazon Q Developer coding challenges that my colleagues and I are hosting throughout this month as a fun way to wind down activities for the year. The first one was hosted in Johannesburg last Friday with Durban and Cape Town coming up next.

Last week’s launches
These are the launches from last week that caught my attention:

Additional updates
I thought these projects, blog posts, and news items were also interesting:

Upcoming AWS events
Keep a look out and be sure to sign up for these upcoming events:

AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

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

Veliswa.

Community, Coffee, and Code: A Zabbix Summit 2025 Recap

Post Syndicated from Michael Kammer original https://blog.zabbix.com/community-coffee-and-code-a-zabbix-summit-2025-recap/31577/

Zabbix Summit 2025 is officially in the history books, so now is the perfect time for a casual, behind‑the‑scenes run‑through of what went down. If you were there, this should ring a few bells (or spark some “oh hey, I forgot about that” moments). If you couldn’t make it, consider this your own personal highlight reel!

Featuring approximately 550 attendees from 42 countries, the Summit took place from October 8-10 at the Radisson Blu Hotel Latvija in the heart of downtown Riga. The 13th in-person version of our premier yearly event was in many ways our biggest and boldest yet, and it included keynote sessions, two parallel tracks (including a developer track), workshops, hands-on sessions, training and certification exams, and a variety of evening social and networking events.

Open source, open house

On October 8, we welcomed nearly 100 guests to our brand-new headquarters for Zabbix Summit 2025’s Open House Day. The new facility gave us plenty of space to host everyone, and visitors got to explore our new HQ, take part in a fun quiz with Zabbix facts, and catch up with longtime colleagues while meeting new ones from the community and the Zabbix team.

Day 1: Looking ahead 

The Summit officially kicked off with Zabbix Founder and CEO Alexei Vladishev’s keynote address, entitled “Zabbix 8.0: A New Chapter in Monitoring.” The address laid out in detail what’s around the corner for Zabbix, including:

  • Zabbix Academy – a new learning hub with self-paced, expert-built courses to boost Zabbix skills anytime and from anywhere.
  • Zabbix France – Zabbix is acquiring IZI-IT and opening a new office in France to provide localized support and closer collaboration with French clients and partners.
  • Zabbix Cloud – a host of new features, including automatic upgrades and backups, plus predictable pricing and simplified user management.
  • Zabbix 8.0 LTS (coming in 2026) – a major leap forward with APM and OpenTelemetry for end-to-end visibility, Complex Event Processing (CEP) and AI-based correlation, plus new UI & visualizations for a smoother experience.
  • Zabbix Mobile App – coming with 8.0 LTS for iOS & Android, the app will offer instant push notifications, issue management, collaboration, seamless connection with Zabbix Cloud, and multi-server views in your pocket.
  • Zabbix Marketplace (2026) – A new global space to connect Zabbix users with vendor and partner solutions, Zabbix Marketplace will extend the power of Zabbix beyond our core product.

Next up was initMAX Founder and CEO Tomáš Heřmánek, who showed how to turn physical sensor data from analog inputs into Zabbix metrics with budget hardware and integrations, complete with templates and triggers.

Another crowd-pleasing session reached the audience thanks to Richard Germanus of CANCOM, who shared the story of how CANCOM consolidated six monitoring systems into one, managing approximately 30,000 hosts, deploying 162 Zabbix proxies, standardizing templates, integrating Power BI for dashboards, automating with APIs, and offering monitoring-as-a-service.

Shortly thereafter, a lightning talk by SEB Bank’s Giedrius Stasiulionis explored “Monitoring Sounds with Zabbix” – in other words, converting audio and sound waves into meaningful metrics, a fresh and inventive notion.

The day’s other lightning talk, “Monitor Your Nearby Areas and Events with Zabbix” by longtime Summit fixture and Zabbix superfan Janne Pikkarainen, showed how anyone can use Zabbix to centralize event data like train timetables, traffic patterns, or cinema showtimes.

Developer track: Something for everyone

Meanwhile, the Summit Developer track was full of special sessions for builders and extension authors, such as “Extend Zabbix Agent 2 with Your Plugin”, which saw Senior Golang Developer Eriks Sneiders show an appreciative audience how Zabbix agent 2’s plugin architecture works, how to use existing plugins, and how to build brand-new custom ones.

Other topics in the Developer track included template design, advanced scripting, API tips, and internal tooling, giving Zabbix techies some food for thought and hopefully sparking a batch of fresh ideas!

Day 2: Showing the big picture

After a long first day and night, Zabbix Summit 2025’s special guest Dylan Beattie made some noise and woke everyone up with a talk entitled “Open Source, Open Mind: The Cost of Free Software.”

Dylan took the Summit audience on a journey through the history and philosophy of free and open source software, touching on questions about licensing issues, looking at the motivations of developers, discussing edge cases and challenges, and asking whether truly sustainable open-source ecosystems can exist.

Later, Inqbeo Founder Christian Anton shared a system in which a central Zabbix instance serves multiple tenants, with the architecture leveraging Kafka to stream metric data partitioned per tenant, storing results in S3 (in Prometheus format), and visualizing via Grafana. This enables isolation and the creation of custom dashboards.

Other main-stage sessions tackled topics like scaling Zabbix, managing large datasets, tag and template strategies, and AI/automation in monitoring.

Connecting people with the Community track

Zabbix Summit 2025 also introduced a Community track, a dedicated space at Zabbix where users, enthusiasts, and contributors could share ideas and shape the future of Zabbix. Instead of deeply technical or development-level presentations, this track focused on community-driven topics like integrations, templates, connectors, media types, and open resources.

A key highlight was the “Zabbix Book Breakout Room”, led by Alexei Vladishev himself along with longtime community members Patrik Uytterhoeven, Brian van Baekel, and Nathan Liefting. Zabbix users were able to brainstorm ideas for new chapters, missing topics, translations, and community contributions to the online Zabbix Book.

Turning ideas into action

Day 2 was also full of hands-on workshops, including a fascinating one from the team at initMAX that was based on their day 1 presentation. Participants got kits with an ESP32 board, a camera, a 3D-printed counter mount, and a few other odds and ends. They were then guided step-by-step as they integrated the device into Zabbix, built monitoring scenarios, and used AI models to interpret camera images.

Meanwhile, the Summit also hosted training and certification exams before, during, and after the main event. Attendees could take courses like Automation & Integration with API, Database Monitoring, SNMP Monitoring, and level-up exams (Specialist and Professional) at discounted rates.

A different kind of networking

One of the things that makes the Zabbix Summit experience so special is the depth of the networking experience – there’s no awkward small talk or simple business card exchanges here, but rather a series of real connections made, deals closed, and new partnerships cemented.

Accordingly, a lot of the magic at Zabbix Summit 2025 happened after hours, with everyone gathering at Riga’s famed Monkey Club for the Summit Welcome Event on October 8 to enjoy a lively atmosphere, a wide selection of cocktails, and plenty of opportunities to connect with fellow monitoring and observability enthusiasts.

October 9’s Main Event took place in the Tallinn Quarter Angārs, which blended concert hall energy with an open-plan street food kitchen and bar that gave everyone plenty of room to mingle.

A special treat was provided in the form of an original Zabbix-related song by Zabbix PHP Developer and part-time rock star Vladimirs Maksimovs, which got the entire crowd on its feet and set the tone for an unforgettable evening.

In what has become a bit of a tradition within a tradition, the Summit officially wrapped up on October 10 at Riga’s Burzma Food Hall, with its relaxed atmosphere, multiple cuisines, and communal tables. It’s proven to be the perfect place for reflecting on Summit highlights, swapping contact info, or plotting collaborations.

Thank you to our sponsors!

We want to extend our heartfelt thanks to all the sponsors of Zabbix Summit 2025, whose commitment not only helped us bring everyone together under one roof but also contributed to the growth of both Zabbix and the entire global monitoring ecosystem. We value your partnership and look forward to working with you for many years to come!

Thanks again to our sponsors and everyone else who helped make Zabbix Summit 2025 possible!

In case you couldn’t make it…

If you didn’t manage to make the trip, you can still enjoy the Summit atmosphere in the privacy of your own home! Recordings of both days are available on Zabbix’s YouTube channel:

Zabbix Summit 2025 Day 1 

Zabbix Summit 2025 Day 2 

The slides and texts of the presentations are also available here.

And that’s a wrap on Zabbix Summit 2025! From mind-blowing tech talks to caffeinated hallway chats and everything in between, this year’s Summit experience delivered. Whether you came for the deep dives or just the cool merch (no shame in that), we hope you went away inspired, connected, and maybe just a little more obsessed with monitoring and observability than before. See you in 2026!

The post Community, Coffee, and Code: A Zabbix Summit 2025 Recap appeared first on Zabbix Blog.

Introducing Amazon EBS Volume Clones: Create instant copies of your EBS volumes

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/introducing-amazon-ebs-volume-clones-create-instant-copies-of-your-ebs-volumes/

 

As someone that used to work at Sun Microsystems, where ZFS was invented, I’ve always loved working with storage systems that offer instant volume copies for my development and testing needs.

Today, I’m excited to share that AWS is bringing similar capabilities to Amazon Elastic Block Store (Amazon EBS) with the launch of Amazon EBS Volume Clones, a new capability that lets you create instant point-in-time copies of your EBS volumes within the same Availability Zone.

Many customers need to create copies of their production data to support development and testing activities in a separate nonproduction environment. Until now, this process required taking an EBS snapshot (stored in Amazon Simple Storage Service (Amazon S3)) and then creating a new volume from that snapshot. Although this approach works, the process creates operational overhead due to multiple steps.

With Amazon EBS Volume Clones, you can now create copies of your EBS volumes with a single API call or console click. The copied volumes are available within seconds and provide immediate access to your data with single-digit millisecond latency. This makes Volume Clones particularly useful for quickly setting up test environments with production data or creating temporary copies of databases for development purposes.

Let me show you how Volume Clones works
For this post, I created a small Amazon Elastic Compute Cloud (Amazon EC2) instance, with an attached volume. I created a file on the root file system with the command echo "Hello CopyVolumes" > hello.txt.

To initiate the copy, I open a browser on the AWS Management Console and I navigate to EC2, Elastic Block Store, Volumes. I select the volume I want to copy.

Note that, at the time of publication of this post, only encrypted volumes can be copied.

On the Actions menu, I choose the Copy Volume option.

Copy Volume - initiate

Next, I choose the details of the target volume. I can change the Volume type and adjust the Size, IOPS, and Throughput parameters. I choose Copy volume to start the Volume Clone operation.

Copy Volume - Parameters

The copied volume enters the Creating state and becomes available within seconds. I can then attach it to an EC2 instance and start using it immediately.

Data blocks are copied from the source volume and written to the volume copy in the background. The volume remains in the Initializing state until the process is complete. I can monitor its progress with the describe-volume-status API. The initializing operation doesn’t affect the performance of the source volume. I can continue using it normally during the copy process.

I love that the copied volume is available immediately. I don’t need to wait for its initialization to complete. During the initialization phase, my copied volume delivers performance based on the lowest of: a baseline of 3,000 IOPS and 125 MiB/s, the source volume’s provisioned performance, or the copied volume’s provisioned performance.

After initialization is completed, the copied volume becomes fully independent of the source volume and delivers its full provisioned performance.

Copy Volume - InitializingAlternatively, I can use the AWS Command Line Interface (AWS CLI) to initiate the copy:

aws ec2 copy-volumes                          \
     --source-volume-id vol-1234567890abcdef0 \
     --size 500                               \
     --volume-type gp3

After the volume copy is created, I attach it to my EC2 instance and mount it. I can check the file I created at start is present.

First, I attach the volume from my laptop, using the attach-volume command:

aws ec2 attach-volume \
         --volume-id 'vol-09b700e3a23a9b4ad' \
         --instance-id 'i-079e6504ad25b029e'   \
         --device '/dev/sdb'

Then, I connect to the instance, and I type these commands:

$ sudo lsblk -f
NAME          FSTYPE FSVER LABEL UUID                                 FSAVAIL FSUSE% MOUNTPOINTS
nvme0n1                                                                              
├─nvme0n1p1   xfs          /     49e26d9d-0a9d-4667-b93e-a23d1de8eacd    6.2G    22% /
└─nvme0n1p128 vfat   FAT16       3105-2F44                               8.6M    14% /boot/efi
nvme1n1                                                                              
├─nvme1n1p1   xfs          /     49e26d9d-0a9d-4667-b93e-a23d1de8eacd                
└─nvme1n1p128 vfat   FAT16       3105-2F44     

$ sudo mount -t xfs /dev/nvme1n1p1 /data

$ df -h
Filesystem        Size  Used Avail Use% Mounted on
devtmpfs          4.0M     0  4.0M   0% /dev
tmpfs             924M     0  924M   0% /dev/shm
tmpfs             370M  476K  369M   1% /run
/dev/nvme0n1p1    8.0G  1.8G  6.2G  22% /
tmpfs             924M     0  924M   0% /tmp
/dev/nvme0n1p128   10M  1.4M  8.7M  14% /boot/efi
tmpfs             185M     0  185M   0% /run/user/1000
/dev/nvme1n1p1    8.0G  1.8G  6.2G  22% /data

$ cat /data/home/ec2-user/hello.txt 
Hello CopyVolumes

Things to know
Volume Clones creates copies within the same Availability Zone as your source volume. You can create copies from encrypted volumes only, and the size of your copy must be equal to or greater than the source volume.

Volume Clones creates crash-consistent copies of your volumes, exactly like snapshots. For application consistency, you need to pause application I/O operations before creating the copy. For example, with PostgreSQL databases, you can use the pg_start_backup() and pg_stop_backup() functions to pause writes and create a consistent copy. At the operating system level on Linux with XFS, you can use the xfs_freeze command to temporarily suspend and resume access to the file system and ensure all cached updates are written to disk.

Although Volume Clones creates point-in-time copies, it complements rather than replaces EBS snapshots for backup purposes. EBS snapshots remain the recommended solution for data backup and protection against AZ-level and volume failures. Snapshots provide incremental backups to Amazon S3 with 11 nines of durability, compared to Volume Clones which maintains EBS volume durability (99.999% for io2, 99.9% for other volume types). Consider using Volume Clones specifically for test and development environment scenarios where you need instant access to volume copies.

Copied volumes exist independently of their source volumes and continue to incur standard EBS volume charges until you delete them. To manage costs effectively, implement governance rules to identify and remove copied volumes that are no longer needed for your development or testing activities.

Pricing and availability
Volume Clones supports all EBS volume types and works with volumes in the same AWS account and Availability Zone. This new capability is available in all AWS commercial Regions, selected Local Zones, and in the AWS GovCloud (US).

For pricing, you’re charged a one-time fee per GiB of data on the source volume at initiation and standard EBS pricing for the new volume.

I find Volume Clones particularly valuable for database workloads and continuous integration (CI) scenarios. For instance, you can quickly create a copy of your production database for testing new features or troubleshooting issues without impacting your production environment or waiting for data to hydrate from Amazon S3.

To get started with Amazon EBS Volume Clones, visit the Amazon EBS section on the console or check out the EBS documentation. I look forward to hearing how you use this capability to improve your development workflows.

— seb

AWS Weekly Roundup: Amazon Quick Suite, Amazon EC2, Amazon EKS, and more (October 13, 2025)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-quick-suite-amazon-ec2-amazon-eks-and-more-october-13-2025/

This week I was at the inaugural AWS AI in Practice meetup from the AWS User Group UK. AI-assisted software development and agents were the focus of the evening! Next week I’ll be in Italy for Codemotion (Milan) and an AWS User Group meetup (Rome). I am also excited to try the new Amazon Quick Suite that brings AI-powered research, business intelligence, and automation capabilities into a single workspace.

Last week’s launches
Here are the launches that got my attention this week:

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

Upcoming AWS events
Check your calendars so that you can sign up for these upcoming events:

  • AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
  • AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions, meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Budapest (October 16).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here upcoming in-person events, developer-focused events, and events for startups.

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

Danilo

Announcing Amazon Quick Suite: your agentic teammate for answering questions and taking action

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/reimagine-the-way-you-work-with-ai-agents-in-amazon-quick-suite/

Today, we’re announcing Amazon Quick Suite, a new agentic teammate that quickly answers your questions at work and turns those insights into actions for you. Instead of switching between multiple applications to gather data, find important signals and trends, and complete manual tasks, Quick Suite brings AI-powered research, business intelligence, and automation capabilities into a single workspace. You can now analyze data through natural language queries, find critical information across enterprise and external sources in minutes, and automate processes from simple tasks to complex multi-department workflows.

Here’s a look into Quick Suite.

Business users often need to gather data across multiple applications—pulling customer details, checking performance metrics, reviewing internal product information, and performing competitive intelligence. This fragmented process often requires consultation with specialized teams to analyze advanced datasets, and in some cases, must be repeated regularly, reducing efficiency and leading to incomplete insights for decision-making.

Quick Suite helps you overcome these challenges by combining agentic teammates for research, business intelligence, and automation into a unified digital workspace for your day-to-day work.

Integrated capabilities that power productivity 
Quick Suite includes the following integrated capabilities:

  • Research – Quick Research accelerates complex research by combining enterprise knowledge, premium third-party data, and data from the internet for more comprehensive insights.
  • Business intelligence – Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations, helping everyone make faster decisions and achieve better business outcomes.
  • Automation – Quick Flows and Quick Automate help users and technical teams to automate any business process from simple, routine tasks to complex multi-department workflows, enabling faster execution and reducing manual work across the organization.

Let’s dive into some of these key capabilities.

Quick Index: Your unified knowledge foundation
Quick Index creates a secure, searchable repository that consolidates documents, files, and application data to power AI-driven insights and responses across your organization.

As a foundational component of Quick Suite, Quick Index operates in the background to bring together all your data—from databases and data warehouses to documents and email. This creates a single, intelligent knowledge base that makes AI responses more accurate and reduces time spent searching for information.

Quick Index automatically indexes and prepares any uploaded files or unstructured data you add to your Quick Suite, enabling efficient searching, sorting, and data access. For example, when you search for a specific project update, Quick Index instantly returns results from uploaded documents, meeting notes, project files, and reference materials—all from one unified search instead of checking different repositories and file systems.

To learn more, visit the Quick Index overview page.

Quick Research: From complex business challenges to expert-level insights
Quick Research is a powerful agent that conducts comprehensive research across your enterprise data and external sources to deliver contextualized, actionable insights in minutes or hours — work that previously could take longer.

Quick Research systematically breaks down complex questions into organized research plans. Starting with a simple prompt, it automatically creates detailed research frameworks that outline the approach and data sources needed for comprehensive analysis.

After Quick Research creates the plan, you can easily refine it through natural language conversations. When you are happy with the plan, it works in the background to gather information from multiple sources, using advanced reasoning to validate findings and provide thorough analysis with citations.

Quick Research integrates with your enterprise data connected to Quick Suite, the unified knowledge foundation that connects to your dashboards, documents, databases, and external sources, including Amazon S3, Snowflake, Google Drive, and Microsoft SharePoint. Quick Research grounds key insights to original sources and reveals clear reasoning paths, helping you verify accuracy, understand the logic behind recommendations, and present findings with confidence. You can trace findings back to their original sources and validate conclusions through source citations. This makes it ideal for complex topics requiring in-depth analysis.

To learn more, visit the Quick Research overview page.

Quick Sight: AI-powered business intelligence
Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations.

You can create dashboards and executive summaries using conversational prompts, reducing dashboard development time while making advanced analytics accessible without specialized skills.

Quick Sight helps you ask questions about your data in natural language and receive instant visualizations, executive summaries, and insights. This generative AI integration provides you with answers from your dashboards and datasets without requiring technical expertise.

Using the scenarios capability, you can perform what-if analysis in natural language with step-by-step guidance, exploring complex business scenarios and finding answers faster than before.

Additionally, you can respond to insights with one-click actions by creating tickets, sending alerts, updating records, or triggering automated workflows directly from your dashboards without switching applications.

To learn more, visit Quick Sight overview page.

Quick Flows: Automation for everyone
With Quick Flows, any user can automate repetitive tasks by describing their workflow using natural language without requiring any technical knowledge. Quick Flows fetches information from internal and external sources, takes action in business applications, generates content, and handles process-specific requirements.

Starting with straightforward business requirements, it creates a multi-step flow including input steps for gathering information, reasoning groups for AI-powered processing, and output steps for generating and presenting results.

After the flow is configured, you can share it with a single click to your coworkers and other teams. To execute the flow, users can open it from the library or invoke it from chat, provide the necessary inputs, and then chat with the agent to refine the outputs and further customize the results.

To learn more, visit the Quick Flows overview page.

Quick Automate: Enterprise-scale process automation
Quick Automate helps technical teams build and deploy sophisticated automation for complex, multistep processes that span departments, systems, and third-party integrations. Using AI-powered natural language processing, Quick Automate transforms complex business processes into multi-agent workflows that can be created merely by describing what you want to automate or uploading process documentation.

While Quick Flows handles straightforward workflows, Quick Automate is designed for comprehensive and complex business processes like customer onboarding, procurement automations, or compliance procedures that involve multiple approval steps, system integrations, and cross-departmental coordination. Quick Automate offers advanced orchestration capabilities with extensive monitoring, debugging, versioning, and deployment features.

Quick Automate then generates a comprehensive automation plan with detailed steps and actions. You will find a UI agent that understands natural language instructions to autonomously navigate websites, complete form inputs, extract data, and produces structured outputs for downstream automation steps.

Additionally, you can define a custom agent, complete with instructions, knowledge, and tools, to complete process-specific tasks using the visual building experience – no code required.

Quick Automate includes enterprise-grade features such as user role management and human-in-the-loop capabilities that route specific tasks to users or groups for review and approval before continuing workflows. The service provides comprehensive observability with real-time monitoring, success rate tracking, and audit trails for compliance and governance.

To learn more, visit the Quick Automate overview page.

Additional foundational capabilities
Quick Suite includes other foundational capabilities that deliver seamless data organization and contextual AI interactions across your enterprise.

Spaces – Spaces provide a straightforward way for every business user to add their own context by uploading files or connecting to specific datasets and repositories specific to their work or to a particular function. For example, you might create a space for quarterly planning that includes budget spreadsheets, market research reports, and strategic planning documents. Or you could set up a product launch space that connects to your project management system and customer feedback databases. Spaces can scale from personal use to enterprise-wide deployment while maintaining access permissions and seamless integration with Quick Suite capabilities.

Chat agents – Quick Suite includes insights agents that you can use to interact with your data and workflows through natural language. Quick Suite includes a built-in agent to answer questions across all of your data and custom chat agents that you can configure with specific expertise and business context. Custom chat agents can be tailored for particular departments or use cases—such as a sales agent connected to your product catalog data and pricing information stored in a space or a compliance agent configured with your regulatory requirements and actions to request approvals.

Additional things to know
If you’re an existing Amazon QuickSight customer – Amazon QuickSight customers will be upgraded to Quick Suite, a unified digital workspace that includes all your existing QuickSight business intelligence capabilities (now called “Quick Sight”) plus new agentic AI capabilities. This is an interface and capability change—your data connectivity, user access, content, security controls, user permissions, and privacy settings remain exactly the same. No data is moved, migrated, or changed.

Quick Suite offers per-user subscription-based pricing with consumption-based charges for the Quick Index and other optional features. You can find more detail on the Quick Suite pricing page.

Now available
Amazon Quick Suite gives you a set of agentic teammates that helps you get the answers you need using all your data and move instantly from answers to action so you can focus on high value activities that drive better business and customer outcomes.

Visit the getting started page to start using Amazon Quick Suite today.

Happy building
— Esra and Donnie

New general-purpose Amazon EC2 M8a instances are now available

Post Syndicated from Betty Zheng (郑予彬) original https://aws.amazon.com/blogs/aws/new-general-purpose-amazon-ec2-m8a-instances-are-now-available/

Today, we’re announcing the availability of Amazon Elastic Compute Cloud (Amazon EC2) M8a instances, the latest addition to the general-purpose M instance family. These instances are powered by the 5th Generation AMD EPYC (codename Turin) processors with a maximum frequency of 4.5GHz. Customers can expect up to 30% higher performance and up to 19% better price performance compared to M7a instances. They also provide higher memory bandwidth, improved networking and storage throughput, and flexible configuration options for a broad set of general-purpose workloads.

Improvements in M8a
M8a instances deliver up to 30% better performance per vCPU compared to M7a instances, making them ideal for applications that require benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, midsize data stores, application development environments, and caching fleets.

They provide 45% more memory bandwidth compared to M7a instances, accelerating in-memory databases, distributed caches, and real-time analytics.

For workloads with high I/O requirements, M8a instances provide up to 75 Gbps of networking bandwidth and 60 Gbps of Amazon Elastic Block Store (Amazon EBS) bandwidth, a 50% improvement over the previous generation. These enhancements support modern applications that rely on rapid data transfer and low-latency network communication.

Each vCPU on an M8a instance corresponds to a physical CPU core, meaning there is no simultaneous multithreading (SMT). In application benchmarks, M8a instances delivered up to 60% faster performance for GroovyJVM and up to 39% faster performance for Cassandra compared to M7a instances.

M8a instances support instance bandwidth configuration (IBC), which provides flexibility to allocate resources between networking and EBS bandwidth. This gives customers the flexibility to scale network or EBS bandwidth by up to 25% and improve database performance, query processing, and logging speeds.

M8a is available in ten virtualized sizes and two bare metal options (metal-24xl and metal-48xl), providing deployment choices that scale from small applications to large enterprise workloads. All of these improvements are built on the AWS Nitro System, which delivers low virtualization overhead, consistent performance, and advanced security across all instance sizes. These instances are built using the latest sixth generation AWS Nitro Cards, which offload and accelerate I/O for functions, increasing overall system performance.

M8a instances feature sizes of up to 192 vCPU with 768GiB RAM. Here are the detailed specs:

M8a vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
medium 1 4 Up to 12.5 Up to 10
large 2 8 Up to 12.5 Up to 10
xlarge 4 16 Up to 12.5 Up to 10
2xlarge 8 32 Up to 15 Up to 10
4xlarge 16 64 Up to 15 Up to 10
8xlarge 32 128 15 10
12xlarge 48 192 22.5 15
16xlarge 64 256 30 20
24xlarge 96 384 40 30
48xlarge 192 768 75 60
metal-24xl 96 384 40 30
metal-48xl 192 768 75 60

For a complete list of instance sizes and specifications, refer to the Amazon EC2 M8a instances page.

When to use M8a instances
M8a is a strong fit for general-purpose applications that need a balance of compute, memory, and networking. M8a instances are ideal for web and application hosting, microservices architectures, and databases where predictable performance and efficient scaling are important.

These instances are SAP certified and also well suited for enterprise workloads such as financial applications and enterprise resource planning (ERP) systems. They’re equally effective for in-memory caching and customer relationship management (CRM), in addition to development and test environments that require cost efficiency and flexibility. With this versatility, M8a supports a wide spectrum of workloads while helping customers improve price performance.

Now available
Amazon EC2 M8a instances are available today in US East (Ohio) US West (Oregon) and Europe (Spain) AWS Regions. M8a instances can be purchased as On-Demand, Savings Plans, and Spot Instances. M8a instances are also available on Dedicated Hosts. To learn more, visit the Amazon EC2 Pricing page.

To learn more, visit the Amazon EC2 M8a instances page and send feedback to AWS re:Post for EC2 or through your usual AWS support contacts.

Betty

Introducing new compute-optimized Amazon EC2 C8i and C8i-flex instances

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-new-compute-optimized-amazon-ec2-c8i-and-c8i-flex-instances/

After launching Amazon Elastic Compute Cloud (Amazon EC2) memory-optimized R8i and R8i-flex instances and general-purpose M8i and M8i-flex instances, I am happy to announce the general availability of compute-optimized C8i and C8i-flex instances powered by custom Intel Xeon 6 processors available only on AWS with sustained all-core 3.9 GHz turbo frequency and feature a 2:1 ratio of memory to vCPU. These instances deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud.

The C8i and C8i-flex instances offer up to 15 percent better price-performance, and 2.5 times more memory bandwidth compared to C7i and C7i-flex instances. The C8i and C8i-flex instances are up to 60 percent faster for NGINX web applications, up to 40 percent faster for AI deep learning recommendation models, and 35 percent faster for Memcached stores compared to C7i and C7i-flex instances.

C8i and C8i-flex instances are ideal for running compute-intensive workloads, such as web servers, caching, Apache.Kafka, ElasticSearch, batch processing, distributed analytics, high performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding.

As like other 8th generation instances, these instances use the new sixth generation AWS Nitro Cards, delivering up to two times more network and Amazon Elastic Block Storage (Amazon EBS) bandwidth compared to the previous generation instances. They also support bandwidth configuration with 25 percent allocation adjustments between network and Amazon EBS bandwidth, enabling better database performance, query processing, and logging speeds.

C8i instances
C8i instances provide up to 384 vCPUs and 768 TB memory including bare metal instances that provide dedicated access to the underlying physical hardware. These instances help you to run compute-intensive workloads, such as CPU-based inference, and video streaming that need the largest instance sizes or high CPU continuously.

Here are the specs for C8i instances:

Instance size vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
c8i.large 2 4 Up to 12.5 Up to 10
c8i.xlarge 4 8 Up to 12.5 Up to 10
c8i.2xlarge 8 16 Up to 15 Up to 10
c8i.4xlarge 16 32 Up to 15 Up to 10
c8i.8xlarge 32 64 15 10
c8i.12xlarge 48 96 22.5 15
c8i.16xlarge 64 128 30 20
c8i.24xlarge 96 192 40 30
c8i.32xlarge 128 256 50 40
c8i.48xlarge 192 384 75 60
c8i.96xlarge 384 768 100 80
c8i.metal-48xl 192 384 75 60
c8i.metal-96xl 384 768 100 80

C8i-flex instances
C8i-flex instances are a lower-cost variant of the C8i instances, with 5 percent better price performance at 5 percent lower prices. These instances are designed for workloads that benefit from the latest generation performance but don’t fully utilize all compute resources. These instances can reach up to the full CPU performance 95 percent of the time.

Here are the specs for the C8i-flex instances:

Instance size vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
c8i-flex.large 2 4 Up to 12.5 Up to 10
c8i-flex.xlarge 4 8 Up to 12.5 Up to 10
c8i-flex.2xlarge 8 16 Up to 15 Up to 10
c8i-flex.4xlarge 16 32 Up to 15 Up to 10
c8i-flex.8xlarge 32 64 Up to 15 Up to 10
c8i-flex.12xlarge 48 96 Up to 22.5 Up to 15
c8i-flex.16xlarge 64 128 Up to 30 Up to 20

If you’re currently using earlier generations of compute-optimized instances, you can adopt C8i-flex instances without having to make changes to your application or your workload.

Now available
Amazon EC2 C8i and C8i-flex instances are available today in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Spain) AWS Regions. C8i and C8i-flex instances can be purchased as On-Demand, Savings Plan, and Spot instances. C8i instances are also available in Dedicated Instances and Dedicated Hosts. To learn more, visit the Amazon EC2 Pricing page.

Give C8i and C8i-flex instances a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 C8i instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

Channy

AWS IAM Identity Center now supports customer-managed KMS keys for encryption at rest

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-iam-identity-center-now-supports-customer-managed-kms-keys-for-encryption-at-rest/

Starting today, you can use your own AWS Key Management Service (AWS KMS) keys to encrypt identity data, such as user and group attributes, stored in AWS IAM Identity Center organization instances.

Many organizations operating in regulated industries need complete control over encryption key management. While Identity Center already encrypts data at rest using AWS-owned keys, some customers require the ability to manage their own encryption keys for audit and compliance purposes.

With this launch, you can now use customer-managed KMS keys (CMKs) to encrypt Identity Center identity data at rest. CMKs provide you with full control over the key lifecycle, including creation, rotation, and deletion. You can configure granular access controls to keys with AWS Key Management Service (AWS KMS) key policies and IAM policies, helping to ensure that only authorized principals can access your encrypted data. At launch time, the CMK must reside in the same AWS account and Region as your IAM Identity Center instance. The integration between Identity Center and KMS provides detailed AWS CloudTrail logs for auditing key usage and helps meet regulatory compliance requirements.

Identity Center supports both single-Region and multi-Region keys to match your deployment needs. While Identity Center instances can currently only be deployed in a single Region, we recommend using multi-Region AWS KMS keys unless your company policies restrict you to single-Region keys. Multi-Region keys provide consistent key material across Regions while maintaining independent key infrastructure in each Region. This gives you more flexibility in your encryption strategy and helps future-proof your deployment.

Let’s get started
Let’s imagine I want to use a CMK to encrypt the identity data of my Identity Center organization instance. My organization uses Identity Center to give employees access to AWS managed applications, such as Amazon Q Business or Amazon Athena.

As of today, some AWS managed applications cannot be used with Identity Center configured with a customer managed KMS key. See AWS managed applications that you can use with Identity Center to keep you updated with the ever evolving list of compatible applications.

The high-level process requires first to create a symmetric customer managed key (CMK) in AWS KMS. The key must be configured for encrypt and decrypt operations. Next, I configure the key policies to grant access to Identity Center, AWS managed applications, administrators, and other principals who need access the Identity Center and IAM Identity Center service APIs. Depending on your usage of Identity Center, you’ll have to define different policies for the key and IAM policies for IAM principals. The service documentation has more details to help you cover the most common use cases.

This demo is in three parts. I first create a customer managed key in AWS KMS and configure it with permissions that will authorize Identity Center and AWS managed applications to use it. Second, I update the IAM policies for the principals that will use the key from another AWS account, such as AWS applications administrators. Finally, I configure Identity Center to use the key.

Part 1: Create the key and define permissions

First, let’s create a new CMK in AWS KMS.

AWS KMW, screate key, part 1

The key must be in the same AWS Region and AWS account as the Identity Center instance. You must create the Identity Center instance and the key in the management account of your organization within AWS Organization.

I navigate to the AWS Key Management Service (AWS KMS) console in the same Region as my Identity Center instance, then I choose Create a key. This launches me into the key creation wizard.

AWS KMW, screate key, part 2

Under Step 1–Configure key, I select the key type–either Symmetric (a single key used for both encryption and decryption) or Asymmetric (a public-private key pair for encryption/decryption and signing/verification). Identity Center requires symmetric keys for encryption at rest. I select Symmetric.

For key usage, I select Encrypt and decrypt which allows the key to be used only for encrypting and decrypting data.

Under Advanced options, I select KMS – recommended for Key material origin, so AWS KMS creates and manages the key material.

For Regionality, I choose between Single-Region or Multi-Region key. I select Multi-Region key to allow key administrators to replicate the key to other Regions. As explained already, Identity Center doesn’t require this today but it helps to future-proof your configuration. Remember that you can not transform a single-Region key to a multi-Region one after its creation (but you can change the key used by Identity Center).

Then, I choose Next to proceed with additional configuration steps, such as adding labels, defining administrative permissions, setting usage permissions, and reviewing the final configuration before creating the key.

AWS KMS, screate key, part 3

Under Step 2–Add Labels, I enter an Alias name for my key and select Next.

In this demo, I am editing the key policy by adding policy statements using templates provided in the documentation. I skip Step 3 and Step 4 and navigate to Step 5–Edit key policy.

AWS KMS, screate key, part 5

Identity Center requires, at the minimum, permissions allowing Identity Center and its administrators to use the key. Therefore, I add three policy statements, the first and second authorize the administrators of the service, the third one to authorize the Identity Center service itself.

{
	"Version": "2012-10-17",
	"Id": "key-consolepolicy-3",
	"Statement": [
		{
			"Sid": "Allow_IAMIdentityCenter_Admin_to_use_the_KMS_key_via_IdentityCenter_and_IdentityStore",
			"Effect": "Allow",
			"Principal": {
				"AWS": "ARN_OF_YOUR_IDENTITY_CENTER_ADMIN_IAM_ROLE"
			},
			"Action": [
				"kms:Decrypt",
				"kms:Encrypt",
				"kms:GenerateDataKeyWithoutPlaintext"
			],
			"Resource": "*",
			"Condition": {
				"StringLike": {
					"kms:ViaService": [
						"sso.*.amazonaws.com",
						"identitystore.*.amazonaws.com"
					]
				}
			}
		},
		{
			"Sid": "Allow_IdentityCenter_admin_to_describe_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"AWS": "ARN_OF_YOUR_IDENTITY_CENTER_ADMIN_IAM_ROLE"
			},
			"Action": "kms:DescribeKey",
			"Resource": "*"
		},
		{
			"Sid": "Allow_IdentityCenter_and_IdentityStore_to_use_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"Service": [
					"sso.amazonaws.com",
					"identitystore.amazonaws.com"
				]
			},
			"Action": [
				"kms:Decrypt",
				"kms:ReEncryptTo",
				"kms:ReEncryptFrom",
				"kms:GenerateDataKeyWithoutPlaintext"
			],
			"Resource": "*",
            "Condition": {
    	       "StringEquals": { 
                      "aws:SourceAccount": "<Identity Center Account ID>" 
	           }
            }		
		},
		{
			"Sid": "Allow_IdentityCenter_and_IdentityStore_to_describe_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"Service": [
					"sso.amazonaws.com",
					"identitystore.amazonaws.com"
				]
			},
			"Action": [
				"kms:DescribeKey"
			],
			"Resource": "*"
		}		
	]
}

I also have to add additional policy statements to allow my use case: the use of AWS managed applications. I add these two policy statements to authorize AWS managed applications and their administrators to use the KMS key. The document lists additional use cases and their respective policies.

{
    "Sid": "Allow_AWS_app_admins_in_the_same_AWS_organization_to_use_the_KMS_key",
    "Effect": "Allow",
    "Principal": "*",
    "Action": [
        "kms:Decrypt"
    ],
    "Resource": "*",
    "Condition": {
        "StringEquals" : {
           "aws:PrincipalOrgID": "MY_ORG_ID (format: o-xxxxxxxx)"
        },
        "StringLike": {
            "kms:ViaService": [
                "sso.*.amazonaws.com", "identitystore.*.amazonaws.com"
            ]
        }
    }
},
{
   "Sid": "Allow_managed_apps_to_use_the_KMS_Key",
   "Effect": "Allow",
   "Principal": "*",
   "Action": [
      "kms:Decrypt"
    ],
   "Resource": "*",
   "Condition": {
      "Bool": { "aws:PrincipalIsAWSService": "true" },
      "StringLike": {
         "kms:ViaService": [
             "sso.*.amazonaws.com", "identitystore.*.amazonaws.com"
         ]
      },
      "StringEquals": { "aws:SourceOrgID": "MY_ORG_ID (format: o-xxxxxxxx)" }
   }
}

You can further restrict the key usage to a specific Identity Center instance, specific application instances, or specific application administrators. The documentation contains examples of advanced key policies for your use cases.

To help protect against IAM role name changes when permission sets are recreated, use the approach described in the Custom trust policy example.

Part 2: Update IAM policies to allow use of the KMS key from another AWS account

Any IAM principal that uses the Identity Center service APIs from another AWS account, such as Identity Center delegated administrators and AWS application administrators, need an IAM policy statement that allows use of the KMS key via these APIs.

I grant permissions to access the key by creating a new policy and attaching the policy to the IAM role relevant for my use case. You can also add these statements to the existing identity-based policies of the IAM role.

To do so, after the key is created, I locate its ARN and replace the key_ARNin the template below. Then, I attach the policy to the managed application administrator IAM principal. The documentation also covers IAM policies that grants Identity Center delegated administrators permissions to access the key.

Here is an example for managed application administrators:

{
      "Sid": "Allow_app_admins_to_use_the_KMS_key_via_IdentityCenter_and_IdentityStore",
      "Effect": "Allow",
      "Action": 
        "kms:Decrypt",
      "Resource": "<key_ARN>",
      "Condition": {
        "StringLike": {
          "kms:ViaService": [
            "sso.*.amazonaws.com",
            "identitystore.*.amazonaws.com"
          ]
        }
      }
    }

The documentation shares IAM policies template for the most common use cases.

Part 3: Configure IAM Identity Center to use the key

I can configure a CMK either during the enablement of an Identity Center organization instance or on an existing instance, and I can change the encryption configuration at any time by switching between CMKs or reverting to AWS-owned keys.

Please note that an incorrect configuration of KMS key permissions can disrupt Identity Center operations and access to AWS managed applications and accounts through Identity Center. Proceed carefully to this final step and ensure you have read and understood the documentation.

After I have created and configured my CMK, I can select it under Advanced configuration when enabling Identity Center.

IDC with CMK configuration

To configure a CMK on an existing Identity Center instance using the AWS Management Console, I start by navigating to the Identity Center section of the AWS Management Console. From there, I select Settings from the navigation pane, then I select the Management tab, and select Manage encryption in the Key for encrypting IAM Identity Center data at rest section.

Change key on existing IDC

At any time, I can select another CMK from the same AWS Account, or switch back to an AWS-managed key.

After choosing Save, the key change process takes a few seconds to complete. All service functionalities continue uninterrupted during the transition. If, for whatever reasons, Identity Center can not access the new key, an error message will be returned and Identity Center will continue to use the current key, keeping your identity data encrypted with the mechanism it is already encrypted with.

CMK on IDC, select a new key

Things to keep in mind
The encryption key you create becomes a crucial component of your Identity Center. When you choose to use your own managed key to encrypt identity attributes at rest, you have to verify the following points.

  • Have you configured the necessary permissions to use the KMS key? Without proper permissions, enabling the CMK may fail or disrupt IAM Identity Center administration and AWS managed applications.
  • Have you verified that your AWS managed applications are compatible with CMK keys? For a list of compatible applications, see AWS managed applications that you can use with IAM Identity Center. Enabling CMK for Identity Center that is used by AWS managed applications incompatible with CMK will result in operational disruption for those applications. If you have incompatible applications, do not proceed.
  • Is your organization using AWS managed applications that require additional IAM role configuration to use the Identity Center and Identity Store APIs? For each such AWS managed application that’s already deployed, check the managed application’s User Guide for updated KMS key permissions for IAM Identity Centre usage and update them as instructed to prevent application disruption.
  • For brevity, the KMS key policy statements in this post omit the encryption context, which allows you to restrict the use of the KMS key to Identity Center including a specific instance. For your production scenarios, you can add a condition like this for Identity Center:
    "Condition": {
       "StringLike": {
          "kms:EncryptionContext:aws:sso:instance-arn": "${identity_center_arn}",
          "kms:ViaService": "sso.*.amazonaws.com"
        }
    }

    or this for Identity Store:

    "Condition": {
       "StringLike": {
          "kms:EncryptionContext:aws:identitystore:identitystore-arn": "${identity_store_arn}",
          "kms:ViaService": "identitystore.*.amazonaws.com"
        }
    }

Pricing and availability
Standard AWS KMS charges apply for key storage and API usage. Identity Center remains available at no additional cost.

This capability is now available in all AWS commercial Regions, AWS GovCloud (US), and AWS China Regions. To learn more, visit the IAM Identity Center User Guide.

We look forward to learning how you use this new capability to meet your security and compliance requirements.

— seb

AWS Weekly Roundup: Amazon Bedrock, AWS Outposts, Amazon ECS Managed Instances, AWS Builder ID, and more (October 6, 2025)

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-bedrock-aws-outposts-amazon-ecs-managed-instances-aws-builder-id-and-more-october-6-2025/

Last week, Anthropic’s Claude Sonnet 4.5—the world’s best coding model according to SWE-Bench – became available in Amazon Q command line interface (CLI) and Kiro. I’m excited about this for two reasons:

First, a few weeks ago I spent 4 intensive days with a global customer delivering an AI-assisted development workshop, where I experienced firsthand how Amazon Q CLI boosts developer productivity. During the workshop, the customer was able to add a new feature in their application within a day using Amazon Q CLI, which would have traditionally taken them at least a couple of weeks. With Anthropic’s Claude Sonnet 4.5 in Amazon Q CLI, I know developer productivity will be enhanced further.

Second, I’ve started preparing for my code talk at AWS re:Invent 2025, where my co-speaker and I will show live coding to modernize a legacy codebase using Kiro. I can’t wait to use Anthropic’s Claude Sonnet 4.5 in Kiro to create a live demo. If you want to see this demo and over a thousand other sessions on cloud and AI, join us at AWS re:Invent 2025 in Las Vegas from December 1–5.

Last week’s launches
Here are some launches that got my attention:

  • Availability of Claude Sonnet 4.5 in Amazon Bedrock – Anthropic’s most intelligent model, best for coding and complex agents, is now available in Amazon Bedrock. By using Claude Sonnet 4.5 in Amazon Bedrock, developers gain access to a fully managed service that not only provides a unified API for foundation models (FMs) but keeps their data under complete control with enterprise-grade tools for security, and optimization.
  • AWS Outposts supports third-party storage integration with Dell and HPE – AWS Outposts third-party storage integration now includes Dell PowerStore and HPE Alletra Storage MP B10000 systems, joining the list of existing integrations with NetApp on-premises enterprise storage arrays and Pure Storage FlashArray. This integration serves three key purposes. First, it helps you maintain your existing storage infrastructure while migrating VMware workloads to AWS. Second, it helps you meet strict data residency requirements by keeping your data on premises while using AWS services. Third, it means you can use AWS Outposts with third-party storage arrays through AWS tooling.
  • Amazon ECS Managed Instances now available – Amazon ECS Managed Instances for containerized applications is a new fully managed compute option for Amazon ECS designed to eliminate infrastructure management overhead while giving you access to the full capabilities of Amazon EC2. ECS Managed Instances helps you quickly launch and scale your workloads while enhancing performance and reducing your total cost of ownership.
  • Application map is now generally available for Amazon CloudWatch – Amazon CloudWatch now helps you monitor large-scale distributed applications by automatically discovering and organizing services into groups based on configurations and their relationships. With this new application performance monitoring (APM) capability, you can quickly visualize which applications and dependencies to focus on while troubleshooting your distributed applications.
  • Amazon Bedrock AgentCore Model Context Protocol (MCP) server now available – With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, or to scale your agent infrastructure.

Additional Updates
Here are some additional news items and blog posts that I found interesting:

  • AWS Builder ID now supports Sign in with Google – You can now create an AWS Builder ID using sign in with Google. AWS Builder ID is a personal profile that provides access to AWS applications including Kiro, AWS Builder Center, AWS Training and Certification, AWS re:Post and AWS Startups.
  • AWS API MCP Server v1.0.0 release – AWS API MCP server acts as a bridge between AI assistants and AWS services enabling foundation models to interact with any AWS API through natural language by creating and executing syntactically correct CLI commands. The AWS API MCP Server is open-source and available now on AWS Labs GitHub repository.
  • AWS Knowledge MCP Server now generally available – The AWS Knowledge server gives AI agents and MCP clients access to authoritative knowledge, including documentation, blog posts, What’s New announcements, and Well-Architected best practices, in an LLM-compatible format. With this release, the server also includes knowledge about the regional availability of AWS APIs and CloudFormation resources.
  • AWS Transform now enables Terraform for VMware network automation – AWS Transform now offers Terraform as an additional option to generate network infrastructure code automatically from VMware environments. The service converts your source network definitions into reusable Terraform modules, complementing current AWS CloudFormation and AWS Cloud Development Kit (CDK) support.

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

  • AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
  • AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions, meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Munich (October 7), Budapest (October 16).

You can browse all upcoming AWS events and AWS startup events.

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

Prasad

Announcing Amazon ECS Managed Instances for containerized applications

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/announcing-amazon-ecs-managed-instances-for-containerized-applications/

Today, we’re announcing Amazon ECS Managed Instances, a new compute option for Amazon Elastic Container Service (Amazon ECS) that enables developers to use the full range of Amazon Elastic Compute Cloud (Amazon EC2) capabilities while offloading infrastructure management responsibilities to Amazon Web Service (AWS). This new offering combines the operational simplicity of offloading infrastructure with the flexibility and control of Amazon EC2, which means customers can focus on building applications that drive innovation, while reducing total cost of ownership (TCO) and maintaining AWS best practices.

Customers running containerized workloads told us they want to combine the simplicity of serverless with the flexibility of self-managed EC2 instances. Although serverless options provide an excellent general-purpose solution, some applications require specific compute capabilities, such as GPU acceleration, particular CPU architectures, or enhanced networking performance. Additionally, customers with existing Amazon EC2 capacity investments through EC2 pricing options couldn’t fully use these commitments with serverless offerings.

Amazon ECS Managed Instances provides a fully managed container compute environment that supports a broad range of EC2 instance types and deep integration with AWS services. By default, it automatically selects the most cost-optimized EC2 instances for your workloads, but you can specify particular instance attributes or types when needed. AWS handles all aspects of infrastructure management, including provisioning, scaling, security patching, and cost optimization, enabling you to concentrate on building and running your applications.

Let’s try it out

Looking at the AWS Management Console experience for creating a new Amazon ECS cluster, I can see the new option for using ECS Managed Instances. Let’s take a quick tour of all the new options.

Creating a ECS cluster with Managed Instances

After I’ve selected Fargate and Managed Instances, I’m presented with two options. If I select Use ECS default, Amazon ECS will choose general purpose instance types based on grouping together pending Tasks, and picking the optimum instance type based on cost and resilience metrics. This is the most straightforward and recommended way to get started. Selecting Use custom – advanced opens up additional configuration parameters, where I can fine-tune the attributes of instances Amazon ECS will use.

Creating a ECS cluster with Managed Instances

By default, I see CPU and Memory as attributes, but I can select from 20 additional attributes to continue to filter the list of available instance types Amazon ECS can access.

Creating a ECS cluster with Managed Instances

After I’ve made my attribute selections, I see a list of all the instance types that match my choices.

Creating a ECS cluster with Managed Instances

From here, I can create my ECS cluster as usual and Amazon ECS will provision instances for me on my behalf based on the attributes and criteria I’ve defined in the previous steps.

Key features of Amazon ECS Managed Instances

With Amazon ECS Managed Instances, AWS takes full responsibility for infrastructure management, handling all aspects of instance provisioning, scaling, and maintenance. This includes implementing regular security patches initiated every 14 days (due to instance connection draining, the actual lifetime of the instance may be longer), with the ability to schedule maintenance windows using Amazon EC2 event windows to minimize disruption to your applications.

The service provides exceptional flexibility in instance type selection. Although it automatically selects cost-optimized instance types by default, you maintain the power to specify desired instance attributes when your workloads require specific capabilities. This includes options for GPU acceleration, CPU architecture, and network performance requirements, giving you precise control over your compute environment.

To help optimize costs, Amazon ECS Managed Instances intelligently manages resource utilization by automatically placing multiple tasks on larger instances when appropriate. The service continually monitors and optimizes task placement, consolidating workloads onto fewer instances to dry up, utilize and terminate idle (empty) instances, providing both high availability and cost efficiency for your containerized applications.

Integration with existing AWS services is seamless, particularly with Amazon EC2 features such as EC2 pricing options. This deep integration means that you can maximize existing capacity investments while maintaining the operational simplicity of a fully managed service.

Security remains a top priority with Amazon ECS Managed Instances. The service runs on Bottlerocket, a purpose-built container operating system, and maintains your security posture through automated security patches and updates. You can see all the updates and patches applied to the Bottlerocket OS image on the Bottlerocket website. This comprehensive approach to security keeps your containerized applications running in a secure, maintained environment.

Available now

Amazon ECS Managed Instances is available today in US East (North Virginia), US West (Oregon), Europe (Dublin), Africa (Cape Town), Asia Pacific (Singapore), and Asia Pacific (Tokyo) AWS Regions. You can start using Managed Instances through the AWS Management Console, AWS Command Line Interface (AWS CLI), or infrastructure as code (IaC) tools such as AWS Cloud Development Kit (AWS CDK) and AWS CloudFormation. You pay for the EC2 instances you use plus a management fee for the service.

To learn more about Amazon ECS Managed Instances, visit the documentation and get started simplifying your container infrastructure today.

Announcing AWS Outposts third-party storage integration with Dell and HPE

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/announcing-aws-outposts-third-party-storage-integration-with-dell-and-hpe/

Since announcing second-generation AWS Outposts racks in April with breakthrough performance and scalability, we’ve continued to innovate on behalf of our customers at the edge of the cloud. Today, we’re expanding AWS Outposts third-party storage integration program to include Dell PowerStore and HPE Alletra Storage MP B10000 systems, joining our list of existing integrations with NetApp on-premises enterprise storage arrays and Pure Storage FlashArray. This program makes it easy for customers to use AWS Outposts with third-party storage arrays through AWS native tooling. The solution integration is particularly important for organizations migrating VMware workloads to AWS who need to maintain their existing storage infrastructure during the transition, and for those who must meet strict data residency requirements by keeping their data on-premises while using AWS services.

Outposts compute rack_Gen2_front_45This announcement builds upon two significant storage integration milestones we achieved in the past year. In December 2024, we introduced the ability to attach block data volumes from third-party storage arrays to Amazon EC2 instances on Outposts directly through the AWS Management Console. Then in July 2025, we enabled booting Amazon EC2 instances directly from these external storage arrays. Now, with the addition of Dell and HPE, customers have even more choice in how they integrate their on-premises storage investments with AWS Outposts.

Enhanced storage integration capabilities

Our third-party storage integration supports both data and boot volumes, offering two boot methods: iSCSI SANboot and Localboot. The iSCSI SANboot option enables both read-only and read-write boot volumes, while Localboot supports read-only boot volumes using either iSCSI or NVMe-over-TCP protocols. With this comprehensive approach, customers can centrally manage their storage resources while maintaining the consistent hybrid experience that Outposts provides.

Through the Amazon EC2 Launch Instance Wizard in the AWS Management Console, customers can configure their instances to use external storage from any of our supported partners. For boot volumes, we provide AWS-verified AMIs for Windows Server 2022 and Red Hat Enterprise Linux 9, with automation scripts available through AWS Samples to simplify the setup process.

Support for various Outposts configurations

All third-party storage integration features are supported on Outposts 2U servers and both generations of Outposts racks. Support for second-generation Outposts racks means customers can combine the enhanced performance of our latest EC2 instances on Outposts—including twice the vCPU, memory, and network bandwidth—with their preferred storage solutions. The integration works seamlessly with both our new simplified network scaling capabilities and specialized Amazon EC2 instances designed for ultra-low latency and high throughput workloads.

Things to know

Customers can begin using these capabilities today with their existing Outposts deployments or when ordering new Outposts through the AWS Management Console. If you are using third-party storage integration with Outposts servers, you can have either your onsite personnel or a third-party IT provider install the servers for you. After the Outposts servers are connected to your network, AWS will remotely provision compute and storage resources so you can start launching applications. For Outposts rack deployments, the process involves a setup where AWS technicians verify site conditions and network connectivity before the rack installation and activation. Storage partners assist with the implementation of the third-party storage components.

Third-party storage integration for Outposts with all compatible storage vendors is available at no additional charge in all AWS Regions where Outposts is supported. See the FAQs for Outposts servers and Outposts racks for the latest list of supported Regions.

This expansion of our Outposts third-party storage integration program demonstrates our continued commitment to providing flexible, enterprise-grade hybrid cloud solutions, meeting customers where they are in their cloud migration journey. To learn more about this capability and our supported storage vendors, visit the AWS Outposts partner page and our technical documentation for Outposts servers, second-generation Outposts racks, and first-generation Outposts racks. To learn more about partner solutions, check out Dell PowerStore integration with AWS Outposts and HPE Alletra Storage MP B10000 integration with AWS Outposts.

Introducing Claude Sonnet 4.5 in Amazon Bedrock: Anthropic’s most intelligent model, best for coding and complex agents

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/introducing-claude-sonnet-4-5-in-amazon-bedrock-anthropics-most-intelligent-model-best-for-coding-and-complex-agents/

Today, we’re excited to announce that Claude Sonnet 4.5, powered by Anthropic, is now available in Amazon Bedrock, a fully managed service that offers a choice of high- performing foundation models from leading AI companies. This new model builds upon Claude 4’s foundation to achieve state-of-the-art performance in coding and complex agentic applications.

Claude Sonnet 4.5 demonstrates advancements in agent capabilities, with enhanced performance in tool handling, memory management, and context processing. The model shows marked improvements in code generation and analysis, from identifying optimal improvements to exercising stronger judgment in refactoring decisions. It particularly excels at autonomous long-horizon coding tasks, where it can effectively plan and execute complex software projects spanning hours or days while maintaining consistent performance and reliability throughout the development cycle.

By using Claude Sonnet 4.5 in Amazon Bedrock, developers gain access to a fully managed service that not only provides a unified API for foundation models but ensures their data stays under complete control with enterprise-grade tools for security, and optimization.

Claude Sonnet 4.5 also seamlessly integrates with Amazon Bedrock AgentCore, enabling developers to maximize the model’s capabilities for building complex agents. AgentCore’s purpose-built infrastructure complements the model’s enhanced abilities in tool handling, memory management, and context understanding. Developers can leverage complete session isolation, 8-hour long-running support, and comprehensive observability features to deploy and monitor production-ready agents from autonomous security operations to complex enterprise workflows.

Business applications and use cases
Beyond its technical capabilities, Sonnet 4.5 delivers practical business value through consistent performance and advanced problem-solving abilities. The model excels at producing and editing business documents while maintaining reliable performance across complex workflows.

The model demonstrates strength in several key industries:

  • Cybersecurity – Claude Sonnet 4.5 can be used to deploy agents that autonomously patch vulnerabilities before exploitation, shifting from reactive detection to proactive defense.
  • Finance – Sonnet 4.5 handles everything from entry-level financial analysis to advanced predictive analysis, helping transform manual audit preparation into intelligent risk management.
  • Research – Sonnet 4.5 can better handle tools, context, and deliver ready-to-go office files to drive expert analysis into final deliverables and actionable insights.

Sonnet 4.5 features in the Amazon Bedrock API
Here are some highlights of Sonnet 4.5 in the Amazon Bedrock API:

Smart Context Window Management – The new API introduces intelligent handling when AI models reach their maximum capacity. Instead of returning errors when conversations get too long, Claude Sonnet 4.5 will now generate responses up to the available limit and clearly indicate why it stopped. This eliminates frustrating interruptions and allows users to maximize their available context window.

Tool Use Clearing for Efficiency – Claude Sonnet 4.5 enables automatic cleanup of tool interaction history during long conversations. When conversations involve multiple tool calls, the system can automatically remove older tool results while preserving recent ones. This keeps conversations efficient and prevents unnecessary token consumption, reducing costs while maintaining conversation quality.

Cross-Conversation Memory – A new memory capability enables Sonnet 4.5 to remember information across different conversations through the use of a local memory file. Users can explicitly ask the model to remember preferences, context, or important information that persists beyond a single chat session. This creates more personalized and contextually aware interactions while keeping the information safe within the local file.

With these new capabilities for managing context, developers can build AI agents capable of handling long-running tasks at higher intelligence without hitting context limits or losing critical information as frequently.

Getting started
To begin working with Claude Sonnet 4.5, you can access it through Amazon Bedrock using the correct model ID. A good practice is to use the Amazon Bedrock Converse API to write code once and seamlessly switch between different models, making it easier to experiment with Sonnet 4.5 or any of the other models available in Amazon Bedrock.

Let’s see this in action with a simple example. I’m going to use the Amazon Bedrock Converse API to send a prompt to Sonnet 4.5. I start by importing the modules I’m going to use. For this short example, I only need AWS SDK for Python (Boto3) so I can create a BedrockRuntimeClient. I’m also importing the rich package so I can format my output nicely later on.

Following best practices, I create a boto3 session and create an Amazon Bedrock client from it instead of creating one directly. This gives you explicit control over configuration, improves thread safety, and makes your code more predictable and testable compared to relying on the default session.

I want to give the model something with a bit of complexity instead of asking a simple question to demonstrate the power of Sonnet 4.5. So I’m going to give the model the current state of an imaginary legacy monolithic application written in Java with a single database and ask for a digital transformation plan which includes a migration strategy, risk assessment, estimated timeline and key milestones and specific AWS services recommendations.

Because the prompt is quite long I put it in a text file locally and just load it up in code. I then set up the Amazon Bedrock converse payload setting the role to “user” to indicate that this is a message by the user of the application and add the prompt to the content.

This is where the magic happens! We put it all together and call Claude Sonnet 4.5 using its model ID. Well, kind of. You can only access Sonnet 4.5 through an inference profile. This defines which AWS Regions will process your model requests and helps manage throughput and performance.

For this demo, I’ll be using one of Amazon Bedrock’s system-defined cross-Region inference profiles, which automatically routes requests across multiple Regions for optimal performance.

Now I just need to print to the screen to see the results. This is where I use the rich package I imported earlier just so we may have a nicely formatted output as I’m expecting a long response for this one. I also save the output to a file so I can have it handy as something to share with my teams.

Ok, let’s check the results! As expected, Sonnet 4.5 worked through my requirements and provided extensive and deep guidance for my digital transformation plan that I could start putting into practice. It included an executive summary, a step-by-step migration strategy split into phases with time estimates, and even some code samples to seed the development process and start breaking things down into microservices. It also provided the business cases for introducing technology and recommended the correct AWS services for each scenario. Here are some highlights from the report.

Claude Sonnet 4.5 is able to maintain consistency while delivering creative solutions making it an ideal choice for businesses seeking to use AI for complex problem-solving and development tasks. Its enhanced capabilities in following directions and using tools effectively translate into more reliable and innovative solutions across various business contexts.

Things to know
Claude Sonnet 4.5 represents a significant step forward in agent capabilities, particularly excelling in areas where consistent performance and creative problem-solving are essential. Its enhanced abilities in tool handling, memory management, and context processing make it particularly valuable across key industries such as finance, research, and cybersecurity. Whether handling complex development lifecycles, executing long-running tasks, or tackling business-critical workflows, Claude Sonnet 4.5 combines technical excellence with practical business value.

Claude Sonnet 4.5 is available today. For detailed information about its availability please visit the documentation.

To learn more about Amazon Bedrock explore our self-paced Amazon Bedrock Workshop and discover how to use available models and their capabilities in your applications.

AWS Weekly Roundup: Amazon S3, Amazon Bedrock AgentCore, AWS X-Ray and more (September 29, 2025)

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-s3-amazon-bedrock-agentcore-aws-x-ray-and-more-september-29-2025/

Wow, can you all believe it? We’re nearing the end of the year already. Next thing you know, AWS re:Invent will be here! This is our biggest event that takes place every year in Las Vegas from December 1st to December 5th where we reveal and release many of the things that we’ve been working on. If you haven’t already, buy your tickets to AWS re:Invent 2025 to experience it in person. If you can’t make it to Vegas, don’t worry, make sure to stay tuned here on the AWS News Blog where will be covering many of the announcements as they happen.

However, there are plenty of new exciting new releases between now and then, so, as usual, let’s take a quick look at some of the highlights from last week so you can catch up on what’s been recently launched, starting with one of the most popular services: Amazon S3!

S3 updates
The S3 team has been working really hard to make working with S3 even better. This month alone has seen releases such as bulk target selection for S3 Batch Operations, support for conditional deletes in S3 general purpose buckets, increased file size and archive scanning limits for malware protection, and more.

Last week was another S3 milestone with the addition of a preview in the AWS Console for Amazon S3 Tables. You can now take a quick peek at your S3 Tables right from the console, making it easier to understand their data structure and content without writing any SQL. This viewer-friendly feature is ready to use across all regions where S3 Tables are supported, with costs limited to just the S3 requests needed to display your table preview.

Other releases
Here are some highlights from other services which also released some great stuff this week.

Amazon Bedrock AgentCore expands enterprise integration and automation options — Bedrock AgentCore services are leveling up their enterprise readiness with new support for Amazon VPC connectivity, AWS PrivateLink, AWS CloudFormation, and resource tagging, giving developers more control over security and infrastructure automation. These enhancements let you deploy AI agents that can securely access private resources, automate infrastructure deployment, and maintain organized resource management whether you’re using AgentCore Runtime for scalable agent deployment, Browser for web interactions, or Code Interpreter for secure code execution.

AWS X-Ray brings smart sampling for better error detection — AWS X-Ray now offers adaptive sampling that automatically adjusts trace capture rates within your defined limits, helping DevOps teams and SREs catch critical issues without oversampling during normal operations. The new capability includes Sampling Boost for increased sampling during anomalies and Anomaly Span Capture for targeted error tracing, giving teams better observability exactly when they need it while keeping costs in check.

AWS Clean Rooms enhances real-time collaboration wilth incremental ID mapping — AWS Clean Rooms now lets you update ID mapping tables with only new, modified, or deleted records through AWS Entity Resolution, making data synchronization across collaborators more efficient and timely. This improvement helps measurement providers maintain fresh datasets with advertisers and publishers while preserving privacy controls, enabling always-on campaign measurement without the need to reprocess entire datasets.

Short and sweet
Here are some bite-sized updates that could prove really handy for your teams or workloads.

Keeping up with the latest EC2 instance types can be challenging. AWS Compute Optimizer now supports 99 additional instance types including the latest C8, M8, R8, and I8 families.

In competitive gaming, every millisecond counts! Amazon GameLift has launched a new Local Zone in Dallas bringing ultra-low latency game servers closer to players in Texas.

When managing large-scale Amazon EC2 deployments, control is everything! Amazon EC2 Allowed AMIs setting now supports filtering by marketplace codes, deprecation time, creation date, and naming patterns to help prevent the use of non-compliant images. Additionally, EC2 Auto Scaling now lets you force cancel instance refreshes immediately, giving you faster control during critical deployments.

Making customer service more intelligent and secure across languages! Amazon Connect introduces enhanced analytics in its flow designer for better customer journey insights, adds custom attributes for precise interaction tracking, and expands Contact Lens sensitive data redaction to support seven additional European and American languages.

That’s it for this week!

Don’t forget to check out all the upcoming AWS events happening across the globe. There are many exciting opportunities for you to attend free events where you can meet lots of people and learn a lot while enjoying a great day amongst other like-minded people in the tech industry.

And if you feel like competing for some cash, time is running out to be part of something extraordinary! The AWS AI Agent Global Hackathon continues until October 20, offering developers a unique opportunity to build innovative AI agents using AWS’s comprehensive gen AI stack. With over $45,000 in prizes and exclusive go-to-market opportunities up for grabs, don’t miss the chance to showcase your creativity and technical prowess in this global competition.

I hope you have found something useful or exciting within this last week’s launches. We post a weekly review every Monday to help you keep up with the latest from AWS so make sure to bookmark this and hopefully see you for the next one!

Matheus Guimaraes | @codingmatheus

Accelerate AI agent development with the Nova Act IDE extension

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/accelerate-ai-agent-development-with-the-nova-act-ide-extension/

Today, I’m excited to announce the Nova Act extension — a tool that streamlines the path to build browser automation agents without leaving your IDE. The Nova Act extension integrates directly into IDEs like Visual Studio Code (VS Code), Kiro, and Cursor, helping you to create web-based automation agents using natural language with the Nova Act model.

Here’s a quick look at the Nova Act extension in Visual Studio Code:

The Nova Act extension is built on top of the Amazon Nova Act SDK (preview), our browser automation agents SDK (Software Development Kit). The Nova Act extension transforms traditional workflow development by eliminating context switching between coding and testing environments. You can now build, customize, and test production-grade agent scripts—all within your IDE—using features like natural language based generation, atomic cell-style editing, and integrated browser testing. This unified experience accelerates development velocity for tasks like form filling, QA automation, search, and complex multi-step workflows.

You can start with the Nova Act extension by describing your workflow in natural language to quickly generate an initial agent script. Customize it using the notebook-style builder mode to integrate APIs, data sources, and authentication, then validate it with local testing tools that simulate real-world conditions, including live step-by-step debugging of lengthy multi-step workflows.

Getting started with the Nova Act extension
First, I need to install the Nova Act extension from the extension manager in my IDE. 

I’m using Visual Studio Code, and after choosing Extensions, I enter Nova Act. Then, I select the extension and choose Install

To get started, I need to obtain an API key. To do this, I navigate to the Nova Act page and follow the instructions to get the API key. I select Set API Key by opening the Command Palette with Cmd+Shift+P / Ctrl+Shift+P.

After I’ve entered my API key, I can try Builder Mode. This is a notebook-style builder mode that breaks complex automation scripts into modular cells, allowing me to test and debug each step individually before moving to the next.

Here, I can use the Nova Act SDK to build my agent. On the right side, I have a Live view panel to preview my agent’s actions in the browser and an Output panel to monitor execution logs, including the model’s thinking and actions.

To test the Nova Act extension, I choose Run all cells. This will start a new browser instance and act based on the given prompt.

I choose Fullscreen to see how browser automation works.

Another useful feature in Builder Mode is that I can navigate to the Output panel and select the cell to see its logs. This helps me debug or review logs specific to the cell I’m working on.

I can also select a template to get started.

Besides using Builder Mode, I can also chat with Nova Act to create a script for me. To do that, I select the extension and choose Generate Nova Act Script. The Nova Act extension opens a chat dialog in the right panel and automatically creates a script for me.

After I finish creating the script, I can choose Start Builder Mode, and the Nova Act extension will help me create a Python file in Builder Mode. This creates a seamless integration because I can switch between chat capability and Builder Mode.

In the chat interface, I see three workflow modes available:

  • Ask: Describe tasks in natural language to generate automation scripts
  • Edit: Refine or customize generated scripts before execution
  • Agent: Run, monitor, and interact with the AI agent performing the workflow

I can also add Context to provide relevant information about my active documents, instructions, problems, or additional Model Context Protocol (MCP) resources the agent can use, plus a screenshot of the current window. Providing this information helps the agent understand any specific requirements for the automation task.

The Nova Act extension also provides a set of predefined templates that I can access by entering / in the chat. These templates are predefined automation scenarios designed to help quickly generate scripts for common web tasks.

I can use these templates (for example, @novaAct /shopping [my requirements]) to get tailored Python scripts for my workflow. At launch, Nova Act extension provides the following templates:

  • /shopping: Automates online shopping tasks (searching, comparing, purchasing)
  • /extract: Handles data extraction
  • /search: Performs search and information gathering
  • /qa: Automates quality assurance and testing workflows
  • /formfilling: Completes forms and data entry tasks

This extension transforms my agent development workflow by positioning Nova Act extension as a full-stack agent builder tool—a complete agent IDE for the entire development lifecycle. I can prototype with natural language, customize with modular scripting, and validate with local testing—all without leaving my IDE—ensuring production-grade scripts.

Things to know
Here are key points to note:

  • Supported IDEs: At launch, the Nova Act extension is available for Visual Studio Code, Cursor, and Kiro, with additional IDE support planned
  • Open source: The Nova Act extension is available under the Apache 2.0 license, allowing for community contributions and customization
  • Pricing: The Nova Act extension is available at no charge.

Get started with Nova Act extension by installing it from your IDE’s extension marketplace or visiting the GitHub repository for documentation and examples.

Happy automating!
Donnie

AWS Weekly Roundup: Amazon Q Developer, AWS Step Functions, AWS Cloud Club Captain deadline, and more (September 22, 2025)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-q-developer-aws-step-functions-aws-cloud-club-captain-deadline-and-more-september-22-2025/

Three weeks ago, I published a post about the new AWS Region in New Zealand (ap-southeast-6). This led to an incredible opportunity to visit New Zealand, where I met passionate builders and presented at several events including Serverless and Platform Engineering meetup, AWS Tools and Programming meetup, AWS Cloud Clubs in Auckland, and AWS Community Day New Zealand.

During my content creation process for these presentations, I discovered a useful feature in Amazon Q CLI called tangent mode. This feature has transformed how I stay focused by creating conversation checkpoints that let you explore side topics without losing your main thread.

This feature is in experimental mode, and you can enable it with q settings chat.enableTangentMode true. Try it out and see if it helps you.

Last week’s launches
Here are some launches that got my attention:

  • New Foundation Models in Amazon Bedrock — Amazon Bedrock expands its model selection with Qwen model family, DeepSeek-V3.1, and Stability AI image services now generally available, giving developers access to powerful multilingual models and advanced image generation capabilities for text generation, code generation, image creation, and complex problem-solving tasks.
  • Amazon VPC Reachability Analyzer Expands to Seven New Regions — Network Access Analyzer capabilities are now available in additional regions, helping customers analyze and troubleshoot network connectivity issues across their VPC infrastructure with improved global coverage.
  • Amazon Q Developer Supports Remote MCP Servers — Amazon Q Developer now integrates with remote Model Context Protocol (MCP) servers, enabling developers to extend their AI assistant capabilities with custom tools and data sources for enhanced development workflows.
  • AWS Step Functions Enhances Distributed Map with New Data Source Options — Step Functions introduces additional data source options and improved observability features for Distributed Map, making it easier to process large-scale parallel workloads with better monitoring and debugging capabilities.
  • Amazon Corretto 25 Generally Available — Amazon’s no-cost, multiplatform distribution of OpenJDK 25 is now generally available, providing Java developers with long-term support, performance enhancements, and security updates for building modern applications.
  • Amazon SageMaker HyperPod Introduces Autoscaling — SageMaker HyperPod now supports automatic scaling capabilities, allowing machine learning teams to dynamically adjust compute resources based on workload demands, optimizing both performance and cost for distributed training jobs.

Additional Updates

  • AWS Named Leader in 2025 Gartner Magic Quadrant for AI Code Assistants – AWS has been recognized as a Leader in Gartner’s Magic Quadrant for AI Code Assistants, highlighting Amazon Q Developer’s capabilities in helping developers write code faster and more securely with AI-powered suggestions.
  • Become an AWS Cloud Club Captain – Only a couple of days before it closes! Join a growing network of student cloud enthusiasts by becoming an AWS Cloud Club Captain! As a Captain, you’ll get to organize events and build cloud communities while developing leadership skills. The application window is open September 1-28, 2025.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events as well as AWS re:Invent and AWS Summits:

  • AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
  • AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions and meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Mexico City (September 30–October 2), Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: South Africa (September 20), Bolivia (September 20), Portugal (September 27), and Manila (October 4-5).

You can browse all upcoming AWS events and AWS startup events.

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

Happy building!

— Donnie

Qwen models are now available in Amazon Bedrock

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/qwen-models-are-now-available-in-amazon-bedrock/

Today we are adding Qwen models from Alibaba in Amazon Bedrock. With this launch, Amazon Bedrock continues to expand model choice by adding access to Qwen3 open weight foundation models (FMs) in a full managed, serverless way. This release includes four models: Qwen3-Coder-480B-A35B-Instruct, Qwen3-Coder-30B-A3B-Instruct, Qwen3-235B-A22B-Instruct-2507, and Qwen3-32B (Dense). Together, these models feature both mixture-of-experts (MoE) and dense architectures, providing flexible options for different application requirements.

Amazon Bedrock provides access to industry-leading FMs through a unified API without requiring infrastructure management. You can access models from multiple model providers, integrate models into your applications, and scale usage based on workload requirements. With Amazon Bedrock, customer data is never used to train the underlying models. With the addition of Qwen3 models, Amazon Bedrock offers even more options for use cases like:

  • Code generation and repository analysis with extended context understanding
  • Building agentic workflows that orchestrate multiple tools and APIs for business automation
  • Balancing AI costs and performance using hybrid thinking modes for adaptive reasoning

Qwen3 models in Amazon Bedrock
These four Qwen3 models are now available in Amazon Bedrock, each optimized for different performance and cost requirements:

  • Qwen3-Coder-480B-A35B-Instruct – This is a mixture-of-experts (MoE) model with 480B total parameters and 35B active parameters. It’s optimized for coding and agentic tasks and achieves strong results in benchmarks such as agentic coding, browser use, and tool use. These capabilities make it suitable for repository-scale code analysis and multistep workflow automation.
  • Qwen3-Coder-30B-A3B-Instruct – This is a MoE model with 30B total parameters and 3B active parameters. Specifically optimized for coding tasks and instruction-following scenarios, this model demonstrates strong performance in code generation, analysis, and debugging across multiple programming languages.
  • Qwen3-235B-A22B-Instruct-2507 – This is an instruction-tuned MoE model with 235B total parameters and 22B active parameters. It delivers competitive performance across coding, math, and general reasoning tasks, balancing capability with efficiency.
  • Qwen3-32B (Dense) – This is a dense model with 32B parameters. It is suitable for real-time or resource-constrained environments such as mobile devices and edge computing deployments where consistent performance is critical.

Architectural and functional features in Qwen3
The Qwen3 models introduce several architectural and functional features:

MoE compared with dense architectures – MoE models such as Qwen3-Coder-480B-A35B, Qwen3-Coder-30B-A3B-Instruct, and Qwen3-235B-A22B-Instruct-2507, activate only part of the parameters for each request, providing high performance with efficient inference. The dense Qwen3-32B activates all parameters, offering more consistent and predictable performance.

Agentic capabilities – Qwen3 models can handle multi-step reasoning and structured planning in one model invocation. They can generate outputs that call external tools or APIs when integrated into an agent framework. The models also maintain extended context across long sessions. In addition, they support tool calling to allow standardized communication with external environments.

Hybrid thinking modes – Qwen3 introduces a hybrid approach to problem-solving, which supports two modes: thinking and non-thinking. The thinking mode applies step-by-step reasoning before delivering the final answer. This is ideal for complex problems that require deeper thought. Whereas the non-thinking mode provides fast and near-instant responses for less complex tasks where speed is more important than depth. This helps developers manage performance and cost trade-offs more effectively.

Long-context handling – The Qwen3-Coder models support extended context windows, with up to 256K tokens natively and up to 1 million tokens with extrapolation methods. This allows the model to process entire repositories, large technical documents, or long conversational histories within a single task.

When to use each model
The four Qwen3 models serve distinct use cases. Qwen3-Coder-480B-A35B-Instruct is designed for complex software engineering scenarios. It’s suited for advanced code generation, long-context processing such as repository-level analysis, and integration with external tools. Qwen3-Coder-30B-A3B-Instruct is particularly effective for tasks such as code completion, refactoring, and answering programming-related queries. If you need versatile performance across multiple domains, Qwen3-235B-A22B-Instruct-2507 offers a balance, delivering strong general-purpose reasoning and instruction-following capabilities while leveraging the efficiency advantages of its MoE architecture. Qwen3-32B (Dense) is appropriate for scenarios where consistent performance, low latency, and cost optimization are important.

Getting started with Qwen models in Amazon Bedrock
To begin using Qwen models, in the Amazon Bedrock console, I choose Model Access from the Configure and learn section of the navigation pane. I then navigate to the Qwen models to request access. In the Chat/Text Playground section of the navigation pane, I can quickly test the new Qwen models with my prompts.

To integrate Qwen3 models into my applications, I can use any AWS SDKs. The AWS SDKs include access to the Amazon Bedrock InvokeModel and Converse API. I can also use these model with any agentic framework that supports Amazon Bedrock and deploy the agents using Amazon Bedrock AgentCore. For example, here’s the Python code of a simple agent with tool access built using Strands Agents:

from strands import Agent
from strands_tools import calculator

agent = Agent(
    model="qwen.qwen3-coder-480b-instruct-v1:0",
    tools=[calculator]
)

agent("Tell me the square root of 42 ^ 9")

with open("function.py", 'r') as f:
    my_function_code = f.read()

agent(f"Help me optimize this Python function for better performance:\n\n{my_function_code}")

Now available
Qwen models are available today in the following AWS Regions:

  • Qwen3-Coder-480B-A35B-Instruct is available in the US West (Oregon), Asia Pacific (Mumbai, Tokyo), and Europe (London, Stockholm) Regions.
  • Qwen3-Coder-30B-A3B-Instruct, Qwen3-235B-A22B-Instruct-2507, and Qwen3-32B are available in the US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Tokyo), Europe (Ireland, London, Milan, Stockholm), and South America (São Paulo) Regions.

Check the full Region list for future updates. You can start testing and building immediately without infrastructure setup or capacity planning. To learn more, visit the Qwen in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Try Qwen models on the Amazon Bedrock console now, and offer feedback through AWS re:Post for Amazon Bedrock or your typical AWS Support channels.

Danilo

DeepSeek-V3.1 model now available in Amazon Bedrock

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/deepseek-v3-1-now-available-in-amazon-bedrock/

In March, Amazon Web Services (AWS) became the first cloud service provider to deliver DeepSeek-R1 in a serverless way by launching it as a fully managed, generally available model in Amazon Bedrock. Since then, customers have used DeepSeek-R1’s capabilities through Amazon Bedrock to build generative AI applications, benefiting from the Bedrock’s robust guardrails and comprehensive tooling for safe AI deployment.

Today, I am excited to announce DeepSeek-V3.1 is now available as a fully managed foundation model in Amazon Bedrock. DeepSeek-V3.1 is a hybrid open weight model that switches between thinking mode (chain-of-thought reasoning) for detailed step-by-step analysis and non-thinking mode (direct answers) for faster responses.

According to DeepSeek, the thinking mode of DeepSeek-V3.1 achieves comparable answer quality with better results, stronger multi-step reasoning for complex search tasks, and big gains in thinking efficiency compared with DeepSeek-R1-0528.

Benchmarks DeepSeek-V3.1 DeepSeek-R1-0528
Browsecomp 30.0 8.9
Browsecomp_zh 49.2 35.7
HLE 29.8 24.8
xbench-DeepSearch 71.2 55.0
Frames 83.7 82.0
SimpleQA 93.4 92.3
Seal0 42.6 29.7
SWE-bench Verified 66.0 44.6
SWE-bench Multilingual 54.5 30.5
Terminal-Bench 31.3 5.7
(c)
https://api-docs.deepseek.com/news/news250821

DeepSeek-V3.1 model performance in tool usage and agent tasks has significantly improved through post-training optimization compared to previous DeepSeek models. DeepSeek-V3.1 also supports over 100 languages with near-native proficiency, including significantly improved capability in low-resource languages lacking large monolingual or parallel corpora. You can build global applications to deliver enhanced accuracy and reduced hallucinations compared to previous DeepSeek models, while maintaining visibility into its decision-making process.

Here are your key use cases using this model:

  • Code generation – DeepSeek-V3.1 excels in coding tasks with improvements in software engineering benchmarks and code agent capabilities, making it ideal for automated code generation, debugging, and software engineering workflows. It performs well on coding benchmarks while delivering high-quality results efficiently.
  • Agentic AI tools – The model features enhanced tool calling through post-training optimization, making it strong in tool usage and agentic workflows. It supports structured tool calling, code agents, and search agents, positioning it as a solid choice for building autonomous AI systems.
  • Enterprise applications – DeepSeek models are integrated into various chat platforms and productivity tools, enhancing user interactions and supporting customer service workflows. The model’s multilingual capabilities and cultural sensitivity make it suitable for global enterprise applications.

As I mentioned in my previous post, when implementing publicly available models, give careful consideration to data privacy requirements when implementing in your production environments, check for bias in output, and monitor your results in terms of data security, responsible AI, and model evaluation.

You can access the enterprise-grade security features of Amazon Bedrock and implement safeguards customized to your application requirements and responsible AI policies with Amazon Bedrock Guardrails. You can also evaluate and compare models to identify the optimal model for your use cases by using Amazon Bedrock model evaluation tools.

Get started with the DeepSeek-V3.1 model in Amazon Bedrock
If you’re new to using the DeepSeek-V3.1 model, go to the Amazon Bedrock console, choose Model access under Bedrock configurations in the left navigation pane. To access the fully managed DeepSeek-V3.1 model, request access for DeepSeek-V3.1 in the DeepSeek section. You’ll then be granted access to the model in Amazon Bedrock.

Next, to test the DeepSeek-V3.1 model in Amazon Bedrock, choose Chat/Text under Playgrounds in the left menu pane. Then choose Select model in the upper left, and select DeepSeek as the category and DeepSeek-V3.1 as the model. Then choose Apply.

Using the selected DeepSeek-V3.1 model, I run the following prompt example about technical architecture decision.

Outline the high-level architecture for a scalable URL shortener service like bit.ly. Discuss key components like API design, database choice (SQL vs. NoSQL), how the redirect mechanism works, and how you would generate unique short codes.

You can turn the thinking on and off by toggling Model reasoning mode to generate a response’s chain of thought prior to the final conclusion.

You can also access the model using the AWS Command Line Interface (AWS CLI) and AWS SDK. This model supports both the InvokeModel and Converse API. You can check out a broad range of code examples for multiple use cases and a variety of programming languages.

To learn more, visit DeepSeek model inference parameters and responses in the AWS documentation.

Now available
DeepSeek-V3.1 is now available in the US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (London), and Europe (Stockholm) AWS Regions. Check the full Region list for future updates. To learn more, check out the DeepSeek in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give the DeepSeek-V3.1 model a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy