Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its own, handling features across an entire codebase, code review, and performance work over extended sessions.

Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. Claude Fable 5.1 is subject to data retention for up to 30 days and human review by Amazon personnel, with a new aws_review data retention mode. In this mode, AWS retains your prompts and outputs for human safety review within the AWS boundary. The provider_data_share mode is legacy, and Amazon Bedrock does not share your data with the model provider. In addition, Enterprise Frontier Safeguards (EFS), built in partnership between AWS and Anthropic, will let eligible customers use Covered Models while keeping their data in a cloud environment they control.

You have two ways to access Claude Fable 5.1: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Claude Fable 5.1 model card on Amazon Bedrock and Claude Platform on AWS.

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

• Amazon Linux 2027 (AL2027) in public preview: AL2027 is the next version of the Amazon Linux operating system. It runs on kernel 7.1+, purpose-built for cloud-native workloads on AWS with performance, scale, and security in mind. Built on AL2023’s baseline, AL2027 is designed for customers who need a secure, stable, and AWS-native operating system running web applications, databases, containerized microservices, AI/ML workloads, and large-scale infrastructure.
• Amazon EC2 R9g and R9gd memory-optimized instances: These instances are powered by AWS Graviton5 processors, delivering the best price performance for memory-intensive workloads running on Amazon EC2. R9g and R9gd instances deliver up to 25% better compute performance compared to AWS Graviton4-based R8g and R8gd instances. They are up to 30% faster for databases, up to 35% faster for web applications, and up to 35% faster for machine learning. To learn more, read Daniel’s blog post.
• AWS Lambda SnapStart for container image functions: Lambda SnapStart is an opt-in capability that makes it easier for you to build highly responsive and scalable applications without provisioning resources or implementing complex performance optimizations. Previously, SnapStart was only supported for managed runtimes (Python, .NET, and Java). You can now use SnapStart for container images to reduce startup times from several seconds to as low as sub-second for latency-sensitive workloads such as ML inference and interactive APIs.
• AWS Agent Registry now generally available: AWS Agent Registry provides a private, governed catalog and discovery layer for agents, tools, skills, MCP servers, and custom resources within your organization. In addition to the capabilities launched in preview (manual and URL-based record creation, approval workflows, semantic and keyword search, and AWS CloudTrail audit trails), Registry now adds new enterprise features. To learn more, visit the AI Blog post.
• Amazon Redshift now supports Apache Iceberg v3 tables: You can read from and write to Apache Iceberg v3 tables in your data lake of Amazon Redshift. With this launch, Amazon Redshift introduces support for default column values, row lineage, and deletion vectors. Amazon Redshift’s Graviton based provisioned and serverless clusters support the new v3 format. To learn more, visit Apache Iceberg v3 features in Redshift.

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

Other AWS news
Here are some additional projects and news items you may find interesting:

• AWS named a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services: For the 16th consecutive year, Gartner has recognized AWS as a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services, and once again placed AWS highest on the Ability to Execute axis. We believe this recognition reflects our commitment to delivering the broadest and deepest set of cloud capabilities from infrastructure and AI to security and operations, so you can build, innovate, and scale with confidence.
• AWS Certified AI Business Strategist: This new certification targets professionals who evaluate, champion, and scale AI initiatives in their organizations: line-of-business leaders driving adoption across their teams, sales professionals articulating AI value to customers, consultants guiding client strategy from experimentation through production, program managers aligning AI investments to business outcomes. Beta exam registration opened September 1, 2026, with exam delivery beginning September 29.
• Agentic Security: Detection and Response at Machine Speed: We believe security should evolve ahead of AI adoption, not behind it. That belief drove our team to collaborate with the SANS Institute on a new chapter in the 2026 Cloud Security Exchange eBook, where we lay out a practical framework for securing agentic workloads at enterprise scale. Our chapter goes deeper on securing agentic workloads, with specific architectural patterns, implementation guidance, and frameworks for security teams at every stage of agentic AI maturity, whether you’re evaluating, piloting, or operating at scale.

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

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

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

— Channy

During my time at AWS, I have always looked for opportunities to work with students. I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator. It reminds me of why I do this work, and that the students I meet today may well become our customers and collaborators tomorrow. That is why I am happy to open this week with Student Rewards on AWS Builder Center.

Rick Suttles published Introducing Student Rewards on AWS Builder Center, a new benefit for verified higher education students. When you verify your enrollment through SheerID and complete your Builder Center profile, you unlock 12 months of premium AWS Skill Builder access (900+ courses, hands-on labs, certification exam prep, and game-based learning). From there, you earn badges through actions on Builder Center: publishing articles, commenting, and maintaining engagement. At 7 badges, you unlock $10 in AWS Credits. At 14 badges, another $20 in credits. At 21 badges, you earn an AWS Foundational Certification exam voucher ($100 value).

This represents a commitment of over $500 million in resources during this back-to-school season, providing students with the training, tools, and certification needed to start building their careers in cloud and AI. Student Rewards is available to students 18 years or older and enrolled at accredited higher education institutions worldwide, subject to verification and applicable terms.

Verify your student status and start learning, earning badges, and unlocking rewards!

Last week’s launches
Here’s what else happened this week.

• A new AWS Local Zone in Las Vegas, Nevada – This new Local Zone supports Amazon EC2 C7i, M7i, R7i, and C8gn instances, Amazon EBS, Amazon ECS, Amazon EKS, Application Load Balancer, and AWS Direct Connect. AWS Local Zones are now available in more than 30 metropolitan areas worldwide. In addition, AWS added a fourth Availability Zone to the Europe (London) Region, delivering next-generation AI and ML capacity with Trn3 and P6 accelerated instances alongside general-purpose compute.
• Amazon EC2 Auto Scaling now supports batch instance termination – You can now pass up to 100 instance IDs to the TerminateInstanceInAutoScalingGroup API to terminate them as a batch, reducing the number of API calls needed to scale down your Auto Scaling groups. Batch termination is designed for workloads that need to rapidly scale down, such as AI/ML training jobs, container orchestrators, or event-driven architectures that spin up large fleets temporarily.
• AWS CloudShell now includes a built-in visual file editor – CloudShell now includes a visual file editor that you can launch directly from your shell session using a single edit command. The editor supports syntax highlighting, find-and-replace, multi-line selection, copy-paste, and undo-redo in a single browser session. Whether you are updating a deployment script, modifying an agent steering file, editing a CloudFormation template, or fixing a Lambda function, the editor provides a seamless edit-and-run experience without leaving CloudShell.
• Amazon Bedrock now supports SpaceXAI Grok 4.6 with cross-Region inference – Grok 4.6, a frontier model built for coding, agentic tasks, and knowledge work, is now available on Amazon Bedrock. The model runs on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and works with existing account-level controls including model invocation logging, Amazon CloudWatch metrics, and cost itemization in AWS Cost Explorer.
• Amazon Bedrock expands API support and introduces cross-Region inference for OpenAI models – Amazon Bedrock now supports OpenAI GPT-5.6 models (Sol, Terra, and Luna) with the Responses, Converse, and Chat Completions APIs, and adds cross-Region inference. Geo cross-Region inference routes requests within a predefined geography (including new US Geo support with this launch), while Global cross-Region inference serves requests from any commercial AWS Region at a lower per-token cost.
• AgentCore payments is now generally available in Amazon Bedrock AgentCore – At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the “upto” scheme in the x402 protocol for pay-per-inference and dynamic pricing use cases. To learn more, visit the AI Blog post.
• AWS Glue 6.0 delivers 30% price reduction and Iceberg v3 support – AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions. With Iceberg v3, Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution.

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

Other AWS news
Here are some additional posts you may find useful:

• Updates to your AWS Sign-In experience – AWS is gradually introducing updates to the sign-in and sign-up experience. The redesigned sign-in page introduces a unified email entry point for root users and customers using the new email-based sign-in method, while IAM users continue signing in with their account ID, username, and password. The page also includes sign-in options for customers whose AWS account was created using a supported identity provider (Google, GitHub, Apple, or Amazon.com). A redesigned session selection page simplifies viewing and managing multiple active account and role sessions. If your organization relies on browser automation or scripted workflows that interact with the sign-in page, review the post to understand how these changes might affect your configuration.
• In the works: AWS Builder Lofts in Berlin, Hyderabad, and São Paulo – My colleague Channy announced plans to open new Builder Lofts in three cities. Since the first Builder Loft opened in San Francisco in July 2025, it has welcomed more than 22,500 developers through its doors. Each new location will be a permanent community space offering free workshops, networking events, pitch nights, content creation spaces, and co-working areas. Berlin will focus on digital sovereignty and security-readiness, Hyderabad on AI and cloud-native architecture, and São Paulo on supporting Latin America’s developer ecosystem.
• AWS and Amazon WorkSpaces recognized as a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service – AWS has been named a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service (DaaS) for the third consecutive year, evaluated on Completeness of Vision and Ability to Execute. Gartner noted strengths in operations, geographic strategy, and overall viability. This is also the first year the evaluation includes Amazon WorkSpaces for AI agents, a capability that runs AI agents within the same desktop environment, security perimeter, and audit trail as human users.

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

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

• AWS Summits – Free in-person events for builders and innovators to learn, connect, and explore the latest in cloud. Coming up: Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). You can also watch keynotes and sessions on the AWS Summits Global Livestream + On-Demand Hub. To access live and on-demand content for a particular Summit, you need to be registered for that event.
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include Bolivia, Santa Cruz (August 29), Toronto, Canada (August 29), JAWS SONIC, Tokyo (September 5), Warsaw, Poland (September 8).

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building.

Summer is slowly coming to an end, and I am already planning a few days off in the coming months to keep me motivated through the rainy autumn ahead. I hope you are doing the same. Come back next week for more!

Last week, AWS contributors joined the OpenSearch and Valkey communities at Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026 to meet open source developers and contributors. At the four-day event, community leaders and users of these Linux Foundation open source projects gathered to share knowledge, collaborate on solutions, and push the projects forward.

Leaders of the Korean OpenSearch communities volunteered to participate in the booth, and also had time to network and interact in the user group meetup.

OpenSearch is an open source, enterprise-grade search and observability suite that brings order to unstructured data at scale. On June 9, 2026, OpenSearch 3.7 introduced new tools designed to query, alert, and track SLOs across logs, traces, and metrics through a single interface and retrieve vectors up to 5.5x faster for improved search performance. Since July 30, 2026, you can run OpenSearch version 3.7 on Amazon OpenSearch Service for improvements in vector search performance, search relevance, and Query Insights.

Valkey is an open source high-performance key/value datastore that supports a variety of workloads such as caching, message queues, and it can act as a primary database. On May 19, 2026, Valkey 9.1 introduced a redesigned I/O threading model that improves throughput by up to 17% and reduces memory usage for strings under 128 bytes by up to 20%. Since June 23, 2026, you can run Valkey 9.1 in Amazon ElastiCache for node-based clusters, delivering higher throughput, improved memory efficiency, and stronger access control for multi-tenant workloads.

You can connect with AWS contributors at upcoming OpenSearch and Valkey community events.

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

• Amazon EC2 application status checks: Amazon EC2 introduces a new status check that helps you detect and respond to application-level issues on your EC2 instances. With application status checks, EC2 monitors applications to detect issues such as a web server that has stopped accepting requests, a Docker daemon that is not running, an incorrect networking configuration, or a network interface that is no longer passing traffic. To learn more, visit the Application status checks documentation.
• AWS IAM role manager to set up IAM roles automatically: You can use a new role manager that automatically sets up the IAM roles your AWS services need. When you set up a supported service in the console, role manager creates a default role on your behalf, or reuses one that already exists in your account if it matches the required permissions. Role manager supports six AWS service consoles at launch. To learn more, read How AWS IAM role manager rethinks the starting point for IAM roles.
• OpenAI Daybreak available to eligible customers on Amazon Bedrock: Daybreak is the cyber defense initiative from OpenAI that gives defenders governed access to frontier AI for cybersecurity work. For most security teams, Daybreak Blue, powered by GPT-5.6 Sol, serves as the starting point across defensive workflows including vulnerability discovery, detection engineering, and incident response. Daybreak Red, powered by a new GPT-5.6 Cyber, is designed for advanced, authorized tasks such as vulnerability research, exploit reproduction, and mitigation development. To enroll, contact OpenAI or reach out to your AWS account team for guidance on eligibility. To learn more, read the AI Blog post.
• New foundation models in Amazon SageMaker JumpStart: We’re expanding the portfolio of foundation models available to AWS customers. These models address different enterprise AI challenges with specialized capabilities:
  • NVIDIA’s Nemotron 3.5 Lightning model
  • NVIDIA’s Nemotron-Nano-12B-v2, Z.ai’s GLM-5.2 FP8, and GLM-OCR models
  • NVIDIA’s LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B models
  • Black Forest Labs’ FLUX.2-small-decoder and Google’s gemma-4-12B-it models
  • Redis’s langcache-embed-v3-small, JetBrains’ Mellum2-12B-A2.5B-Thinking, and LightOn’s LightOnOCR-2-1B models

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

Other AWS news
Here are some additional projects and news items you may find interesting:

• The deprecation of email validation in AWS Certificate Manager: ACM will discontinue support for email-validated public certificates by September 30, 2027. If you use email validation for your ACM public certificates, you need to migrate to DNS validation before that date. For Amazon CloudFront distributions, HTTP validation is also available.
• The next-generation AWS VPN Client with CLI support and admin controls: You can use a new AWS VPN Client built on OpenVPN3. With the new client, you get full backward compatibility with existing AWS Client VPN endpoints while delivering the automation capabilities and security posture that enterprise networking teams have been asking for.
• Oracle Exadata on Exascale for Oracle AI Database@AWS: ExaDB-XS brings Exadata-class performance and availability through a consumption-based model. With ExaDB-XS, you can scale compute and storage independently in small increments and pay only for what you consume.

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

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

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

— Channy

Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community.

The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions with internal AWS product and service teams.

Day 1 started with an inspiring fireside chat from AWS CEO Matt Garman. From an insightful AMA with James Hamilton on Day 2 to breakout sessions from various product teams that sparked new ideas, our AWS Heroes excelled at sharing knowledge, lifting each other up, and turning conversations into collaborations. To learn more, read the attendee feedback on LinkedIn.

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

• Web Search on Amazon Bedrock: Amazon Bedrock now enables OpenAI models (GPT-5.4, GPT-5.5, and GPT-5.6 Sol/Terra/Luna) to browse and retrieve information from the internet, allowing AI applications to access up-to-date information beyond their training data. This capability opens new possibilities for building AI agents and applications that can answer questions using real-time web content while maintaining data residency within your secured AWS environment with zero data egress. To get started, visit the AI blog post and the Amazon Bedrock User Guide.
• Runtime Instances on Amazon Bedrock AgentCore: You can now deploy and run AI agents on dedicated runtime instances through Amazon Bedrock AgentCore, providing more control over agent execution environments with predictable performance and cost. To get started, visit Sébastien’s blog post and AgentCore documentation.
• Vector search for Amazon DynamoDB: You can store and query vector embeddings alongside your existing data in DynamoDB without managing a separate vector database. DynamoDB already supports storing memory for AI agents, and with vector search you can now add semantic retrieval over that memory for agentic grounding, with predictable performance. To learn more, visit Esra’s blog post and Amazon DynamoDB Developer Guide.
• AWS Transform continuous modernization now generally available: This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. You can modernize mainframe and legacy workloads with an ongoing, automated approach rather than a one-time migration event. To learn more, visit Micah’s preview blog post. You can also try the AWS Transform Kiro Power and agent plugins.
• Up to 3,000 Mbps for AWS Lambda function bandwidth: AWS Lambda functions now support increased network bandwidth, enabling data-intensive workloads and faster communication between Lambda functions and other AWS services. This feature enables functions outside a VPC that are configured with 2 GB of memory or more to access network bandwidth that scales proportionally, from 625 Mbps at 2 GB up to 3,000 Mbps at 10 GB.

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

Other AWS news
Here are some additional projects and news items that you may find interesting:

• Introducing Dogwood: Runtime Verification for AI Agents: AWS open-sourced Dogwood, a purpose-built governance language for AI agents to support Cedar policies and add temporal conditions. Powering Dogwood, Amazon Bedrock AgentCore introduced temporal policies whose decisions depend on the history of an agent’s actions within a session, not on the current request alone.
• AWS supports Agent Plugins: An Open Standard for Portable Agent Extensions: AWS announced support for Agent Plugins, an open source, vendor-neutral specification that gives AI agent extensions a common packaging format so you can package an extension once and ship it to any client, including Kiro, VS Code, Cursor, or any tool that implements the spec.
• Introducing Kiro Crew: Kiro Crew is a persistent, self-evolving workspace that keeps work moving, online or off, enabling collaborative multi-agent development workflows. It’s built for engineering work that goes beyond a single chat session, and spans repos, tools, and days. You can run several efforts in parallel or hand work to subagents that report back, so nothing waits in line.

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

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

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

— Channy

When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.

Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.

What you get
Runtime instances provides AWS-managed EC2 infrastructure where you deploy multiple agents in a single runtime, each with their own dependencies and artifact types. Your agents can collaborate on the same host within shared sessions that persist for up to 14 days. The service supports GPU acceleration for compute-intensive tasks, session stop/restart to save costs during idle periods, and containerized deployments for teams that want to ship independently. For knowledge that needs to survive beyond a session, runtime instances pairs naturally with Amazon Elastic Block Store (Amazon EBS) and AgentCore Memory, which gives your agents long-term recall across sessions and environments.

Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.

A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework (CrewAI, LangGraph, LlamaIndex, Strands) and any model. Packaging is minimal, a @app.entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.

Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.

Let me show you how it works
I built two agents for this demo: a code writer agent that generates Python code from natural language descriptions, and a code reviewer agent that analyzes the generated code for bugs, security issues, and style improvements. Both agents share the same file system, so the reviewer can read whatever the writer produces without any data transfer or API calls between them.

Here is the code writer (simplified, no error handling):

writer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a senior Python engineer. "
        "Given a task, return ONLY a single Python code block — no prose."
    ),
)

@app.entrypoint
def handler(event, context):
    task = event.get("task") or event.get("prompt")
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    session_dir = SHARED_DIR / session_id
    session_dir.mkdir(parents=True, exist_ok=True)

    code = str(writer(task))
    (session_dir / "code.py").write_text(code)

    return {"agent": "writer", "wrote": str(session_dir / "code.py"), "code": code}

Here is the code reviewer agent (simplified, no error handling):

reviewer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a strict Python code reviewer. "
        "Given code, return 3 bullet points: bugs, style, suggestions."
    ),
)

@app.entrypoint
def handler(event, context):
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    code_path = SHARED_DIR / session_id / "code.py"
    code = code_path.read_text()
    review = str(reviewer(f"Review this code:\n\n{code}"))

    return {"agent": "reviewer", "read": str(code_path), "review": review}

Each agent is a Python application using Strands Agents with an @app.entrypoint decorator and a model of its choice. I package each one as a zip file. For this demo, I use the AWS Management Console. You can also use the AgentCore CLI, the AWS Command Line Interface (AWS CLI) or infrastructure as code.

Step 1: Create a capacity provider.

A capacity provider defines the EC2 infrastructure your agents run on. In the AgentCore console, I select Runtime in the left navigation, then select the Capacity providers tab and Create capacity provider.

I give it a Name, select Linux (64-bit ARM) as the Operating system, and choose c7g.2xlarge as the Allowed instance types. This gives me 8 vCPUs and 16 GiB of memory, enough for both agents to run comfortably side by side.

Further down, I configure the VPC, subnets, and security groups for network access. Under Storage configuration, I keep the default gp3 volume. Under Service access, I select Create a new service role and let the console create the infrastructure role that manages EC2 instances on my behalf.

I select Create capacity provider and wait a few seconds. The status moves to Active.

Note the capacity provider configuration summary: operating system, instance type, subnets, security group, instance profile, and infrastructure role. Once created, only the description can be edited, so verify your settings before you proceed.

Step 2: Create a runtime and deploy the first agent.

Back on the Runtime page, I select Create runtime. I give it a Name, select Instances as the Compute type, and choose the Capacity provider I created in the previous step.

Under Agent source, I select S3 Source, then Upload to S3. I choose my agent zip file (ACIDemoWriter.zip), set the Language runtime to Python 3.13, and specify agent.py as the Agent entry point. This is the file that contains my @app.entrypoint decorated function. Under Permissions, I select Create default role to let the console provision the IAM role my agent needs.

I select Create runtime and wait for the status to become Ready.

I repeat the same process for my code reviewer agent. I create a second runtime, select the same capacity provider, upload my reviewer agent zip file, and wait for it to become Ready. Both agents now share the same underlying EC2 infrastructure.

The console shows me a View invocation code section with ready-to-use Python, TypeScript, and JavaScript snippets to invoke my agent programmatically. But for this demo, I use the built-in test feature. I select Test on the writer agent’s page.

Step 3: Invoke agents and observe collaboration.

The Runtime playground opens. At the top, I see three fields: Runtime agent, Endpoint, and Session ID. The console generates a session ID automatically. I take note of it because I will reuse it with the reviewer agent.

In the Input field, I type a JSON payload asking the writer agent to generate code:

{"prompt": "write a fibonacci suite"}

I select Run. After a few seconds, the Output panel shows the agent’s response. The writer agent generated a Python module with two implementations of a Fibonacci sequence (a list-based function and a generator) and wrote it to /tmp/agentcore-session/ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2/code.py. Notice the session ID in the file path. That directory is the shared file system for this session.

Step 4: Invoke the reviewer agent in the same session.

Now I switch the Runtime agent dropdown to ACIDemoReviewer. The important part: I paste the same session ID (ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2) in the Session ID field. This is what connects the two agents.

I type a simple prompt:

{"prompt": "review the code"}

I select Run. The reviewer agent reads the file the writer produced from the shared session directory and returns a detailed code review. It finds no critical bugs but suggests adding type hints, input validation, and simplifying the edge case handling.

The two agents never exchanged messages or called each other’s APIs. They collaborated through the shared file system that runtime instances provide within a session. You can extend this pattern to any number of agents: a test agent that runs the code, a documentation agent that generates README files, a security agent that scans for vulnerabilities, all sharing the same working directory.

Key details
Here are a few things to know as you get started:

• Supported OS: Linux (ARM64 and x86_64) at launch.
• Session persistence: Sessions persist for up to 14 days.
• Runtimes: Python 3.11-14 with native code support. Container images also supported.
• GPU: Support for GPU-accelerated instance types.
• Integration: Uses the same AgentCore APIs, identity, observability, and policy controls as AgentCore Runtime.
• Pricing: Standard EC2 pricing plus a management fee for AgentCore orchestration.
• Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland)

To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.

Last week I had the joy of participating in Amazon’s “Bring Your Kids to Work Day” with my 7 year old son. We commuted together into the New York City office, his first real rush hour train ride, and spent the day exploring how Amazon uses AI, machine learning, and robotics to deliver packages to customers all over the world. Watching his eyes light up as he saw robots navigating a fulfillment center reminded me why so many of us got into technology in the first place. There’s nothing quite like seeing that sense of wonder when something complex clicks.

That same energy carried into the week’s launches. We’ve got updates across AI pricing, observability, multicloud networking, and data management. Let’s dive in.

Headlines
Amazon Bedrock announces up to 80% lower prices for OpenAI GPT‑5.6 models – If you’re using OpenAI’s GPT‑5.6 family through Amazon Bedrock, your costs just dropped significantly. Effective July 30, on-demand inference prices for GPT‑5.6 Luna are reduced by 80%, while GPT‑5.6 Terra prices are reduced by 20%. Luna now costs $0.20 per million input tokens and $1.20 per million output tokens, making it one of the most affordable frontier-class models available. These price reductions apply automatically — no action required on your part. Read more

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

• Amazon CloudWatch announces managed Prometheus collectors – Amazon CloudWatch now supports collecting Prometheus metrics from your AWS infrastructure using fully managed collectors, enabling you to monitor Amazon EKS, Amazon EC2, Amazon ECS, Amazon MSK, and Amazon OpenSearch Service workloads without deploying or managing any agents. If you’ve been maintaining your own Prometheus scraping infrastructure, this removes a significant operational burden. Read more
• AWS Interconnect — multicloud connectivity with Oracle Cloud Infrastructure is now generally available – AWS Interconnect is the first purpose-built multicloud connectivity product of its kind, allowing you to quickly provision resilient, scalable private connections between AWS and other cloud providers. With this GA launch for Oracle Cloud Infrastructure (OCI), you can establish private cross-cloud networking without traversing the public internet, making it easier to run multicloud architectures with the security and performance your workloads demand. Read more
• AWS IAM Identity Center extends multi-Region support to Identity Center directory – You can now replicate IAM Identity Center from your primary AWS Region to additional Regions when using the Identity Center directory as your identity source. If IAM Identity Center is affected by a disruption in the primary Region, your users continue to have access to their AWS accounts using provisioned entitlements in additional Regions. This feature was previously available only for instances connected to external identity providers. Read more
• Amazon S3 Tables now supports the Variant data type for Apache Iceberg V3 – Amazon S3 Tables adds support for the Variant data type, introduced in the Apache Iceberg V3 table format specification. Variant provides a high-performance, native solution for managing semi-structured data within your data lake — think IoT sensor data, application logs, and other schema-flexible payloads — without resorting to JSON blobs. Read more

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

• Installing and updating the AWS CLI with single-line commands – A new blog post from the Developer Tools team that simplifies AWS CLI installation and updates across platforms with single-line commands. If you manage CLI versions across teams or in CI pipelines, this is a nice quality-of-life improvement.
• Deploying Kimi K3 on Amazon SageMaker HyperPod and Amazon EKS – A step-by-step guide for deploying Moonshot AI’s Kimi K3 model on AWS infrastructure using SageMaker HyperPod and Amazon EKS. If you’re evaluating large-scale model deployment options, this walks through the full workflow.
• Deliver Apache Kafka data to streaming tables for Apache Iceberg with Amazon MSK Express brokers – Learn how to stream data from Apache Kafka into Apache Iceberg tables using Amazon MSK Express brokers, with throughput support of up to 10 GB/s for delivery to Apache Iceberg on Amazon S3 Tables.

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

• AWS Summits – AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders.

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

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

Last week I had the privilege of spending three days in São Paulo with technical builders from across Latin America, brought together for a regional tech event full of deep-dive sessions, hands-on workshops, and conversations with customers and partners. What struck me most wasn’t any single session, it was the energy of a technical community that so rarely gets to be in the same room. People traded architecture ideas over coffee, sketched out solutions on whiteboards, and left with a longer list of things to try than they arrived with. It’s a good reminder that, for all the tooling we build, the community around it is what makes the technology stick.

That community spirit connects nicely to the week’s biggest infrastructure news, which is all about bringing AWS closer to where builders actually are.

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

Headlines
AWS Local Zone in Athens, Greece: AWS has opened a new Local Zone in Athens, Greece, the second Local Zone in EMEA with support for Amazon S3 and Amazon EBS Local Snapshots, so you can store and process data within Greece to help meet local data residency requirements. The Athens Local Zone supports Amazon EC2 (C7i, M7i, and R7i instances), Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS, Amazon ECS, and more.

With this launch, you can process and store data in-country while delivering single-digit millisecond latency to your end users. AWS Local Zones place AWS infrastructure much closer to large population and industry hubs to support workloads such as financial services, healthcare, media production, and real-time gaming. For builders in Greece, this means running latency-sensitive workloads locally and meeting in-country data residency requirements, without managing your own data center infrastructure. To learn more, visit AWS Global Infrastructure and Sustainability Blog post.

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

• Claude Opus 5 on AWS: You can use Anthropic’s Claude Opus 5, the most advanced Opus model yet, matching Claude Fable 5’s top-tier intelligence in many domains at Opus-tier pricing. Amazon Bedrock offers Claude Opus 5 with zero data retention (ZDR) enabled by default, giving you Opus’ top-tier intelligence while meeting your data governance requirements unlike Claude Fable 5. You have two ways to access Claude Opus 5: Amazon Bedrock and Claude Platform on AWS. To learn more, visit the deep dive blog post.
• AWS Lambda durable execution SDK for .NET is now generally available: You can now build resilient, long-running workflows in C# using Lambda durable functions, without implementing custom progress tracking or integrating an external orchestration service. The SDK is a natural fit for multi-step applications like payment processing pipelines, AI agent orchestration, and human-in-the-loop approvals, it checkpoints progress automatically and can pause execution for up to a year. If you’re a .NET developer building serverless workflows, this removes a lot of the plumbing you used to write by hand.
• Amazon Bedrock AgentCore now delivers unified observability with traces and logs in a single log group: Amazon Bedrock AgentCore now delivers agent traces and prompts to the same Amazon CloudWatch log group as your agent’s logs. Previously, telemetry was split across destinations, trace spans went to a shared log group while prompts, inputs, and outputs went to a separate one, so debugging a single agent invocation meant searching in multiple places. You can now debug an invocation in one place, and apply fine-grained access control and customer-managed key (CMK) encryption at the individual agent level.
• Amazon Connect delivers more natural agentic voice experiences: Amazon Connect now supports more natural, human-sounding agentic voice experiences across 50+ languages, including Portuguese, Spanish, French, Italian, Japanese, Korean, and Thai, with over 100 new voice options and conversational improvements that make AI interactions sound more fluid. Connect’s agentic self-service lets AI agents understand, reason, and take action across voice and digital channels, adapting to a customer’s tone and sentiment. You can now build contact center experiences that feel natural to callers in far more of the languages your customers actually speak.
• Amazon SageMaker Unified Studio now supports Amazon OpenSearch: You can now query and analyze your search and log analytics data from Amazon OpenSearch directly alongside other data assets in Amazon SageMaker Unified Studio. With this connection, you can combine operational search data in OpenSearch with data from sources like Amazon Redshift, Amazon S3, and relational databases, all within a single, governed environment. It’s especially useful when you need to correlate analytical and operational workloads, such as joining application logs with transactional data to uncover insights.
• Amazon CloudWatch announces coding agent insights: Amazon CloudWatch now gives engineering leaders visibility into how AI coding tools are driving value across their organization. Coding agent insights integrates with the Claude apps gateway for AWS to collect telemetry from Claude Code without additional instrumentation, and also supports agents like Codex and GitHub Copilot. As teams scale AI coding adoption, you can now measure the return on that investment with metrics built on OpenTelemetry, no custom instrumentation required.

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

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

• Evaluating AI Agents: A production blueprint with Strands and AgentCore: A practical guide to evaluating AI agents before and after they reach production, using Strands Agents and Amazon Bedrock AgentCore. If you’re moving agents from prototype to production, this post is a great companion to the AgentCore observability update above, it walks through how to measure agent quality systematically rather than by gut feel.
• Building multi-region resiliency for AWS CloudFormation custom resource deployment: Learn how to architect CloudFormation custom resources for multi-region resiliency, so your infrastructure-as-code deployments stay reliable even when a single Region has issues.
• Introducing Amazon Simple Email Service (SES) pricing plans: Amazon SES now offers pricing plans that give you more predictable costs as your email volume grows. If you send at scale, this could simplify your billing significantly.

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

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

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

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

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

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

My team regularly visits countries across the Asia-Pacific region, listens to feedback from user group leaders, and works to support their communities. At this meeting, leaders honestly shared what they did well in the first half of the year, what needs improvement, and what they asked of AWS Developer Experience team. We also enjoyed a pleasant conversation during our Chimaek time together.

Now, let’s take a closer look at key launches of last week.

A one-click Lambda setup prompt for coding agents caught my eye most last week. This prompt configures your agent with AWS Serverless skills and the Serverless Model Context Protocol (MCP) server, embedding serverless best practices from the start. This prompt references the Lambda agent setup guide, which includes installation commands for Claude Code, Kiro, Cursor, GitHub Copilot, Codex, Devin Desktop, and OpenCode.

To get started, choose the Copy agent prompt button on the Lambda console screen or copy fetch https://docs.aws.amazon.com/lambda/latest/dg/samples/aws-lambda-agent-setup.md directly, and paste this URL in your preferred AI agent.

You can also use Agent Toolkit for AWS to give your coding agent current AWS knowledge and safe resource access. Use fetch https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md for installing AWS MCP Server.

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

• OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: You can use the smartest family of models from OpenAI yet on Bedrock’s next-generation inference engine built for high performance, security, and reliability. The three models span capability tiers from flagship reasoning (Sol) to balanced performance (Terra) to fast, cost-efficient inference (Luna), all accessible through the Responses API on Amazon Bedrock.
• Same-day transitions to Amazon S3 Standard-IA and S3 One Zone-IA: You can now transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) as soon as the day they are created, without the previous 30-day minimum retention period in S3 Standard. These storage classes offer up to 40% lower storage costs than S3 Standard while still providing millisecond access when needed, making them ideal for backups, log analytics, and compliance workloads where data becomes cold within hours or days.
• Self-managed code storage on AWS Lambda: With self-managed Amazon S3 buckets for code storage, you can reference source code directly from your own S3 buckets without Lambda creating intermediate copies. This eliminates code storage limits and reduces function activation time after function creates and updates by removing the copy step.
• Importing users with password hashes on Amazon Cognito: You can now import users with password hashes in CSV user imports. Previously, imported users had to reset their passwords on first sign-in. Now, you can include password hashes in the CSV import, enabling users to sign in immediately with their existing credentials. When creating a CSV import, you specify the password hashing algorithm used by your source system.

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

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

• Amazon SQS turns 20: Two decades of reliable messaging at scale: When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. Let’s look back important milestones after Jeff’s 15th anniversary post.
• Open Protocols with the Strands Agents SDK: Learn how open AI protocols such as MCP, A2A, UTCP, AG-UI, and x402 work together using Strands Agents SDK for building AI agents as an example implementation, though the patterns apply to any agent framework.
• Open source Bulk Executor for Amazon DynamoDB: Performing bulk operations against all items in a DynamoDB table has historically required custom coding. The Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale.
• Transform AWS Support Case Workflows with Kiro CLI: Explore how Kiro CLI’s MCP integration accelerates support case workflows by combining investigation, documentation lookup, and case creation into a single conversational interface across three real-world scenarios: AWS Glue job failures, AWS Lambda cold start investigation, and AWS WAF false positive analysis.

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

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

Finally, some customers experienced an issue with Cost Explorer displaying inaccurate estimated billing data in last weekend. They may have received erroneous budget and cost anomaly detection alerts, and observed inflated estimated cost and usage data. The issue has been resolved, and all AWS services are operating normally. We apologize for the concern this incident caused our customers and are conducting a thorough retrospective to prevent events like this from reoccurring, as well as improving our response when billing incidents occur. For more information, visit the AWS Health Dashboard.

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

— Channy

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

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

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

• Amazon EC2 C9g and C9gd instances powered by AWS Graviton5 processors: They deliver up to 25% better compute performance than Graviton4-based instances, 5x larger cache, fastest memory of any processor instances in the cloud, and local NVMe storage options (C9gd).
• A new AWS CloudFormation Express mode: You can speed up infrastructure deployment with AWS CloudFormation Express mode, enabling AI agents and developers to receive deployment confirmation in seconds and iterate faster. Available in all commercial Regions at no additional cost.
• Upgrade Amazon EKS clusters with confidence using Kubernetes version rollbacks: Learn how Kubernetes version rollbacks for Amazon EKS let you reverse cluster upgrades within seven days. This new feature provides a safety net for upgrade failures, no cluster rebuilds required, turning Kubernetes version upgrades into a reversible, low-risk operation.
• Automate public TLS certificate issuance with ACME support in AWS Certificate Manager: AWS Certificate Manager now supports the ACME protocol, so you can automate the issuance and renewal of public TLS certificates using standard, widely adopted tooling.

Here are some launches and updates that caught my attention:

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

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

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

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

• Amazon Bedrock Agents (launched November 2023) is now Amazon Bedrock Agents Classic
• Amazon Cognito Sync
• Amazon Kendra
• Amazon Q Business
• AWS Directory Service – Simple AD
• AWS IoT Device Defender – Detect (feature will no longer be accessible to new customers starting August 31, 2026)
• AWS Mainframe Modernization – Self-Managed Experience
• AWS Management Console – myApplications
• AWS Resource Groups – Group Lifecycle Events
• AWS Service Catalog – Application Registry
• AWS Systems Manager – Application Manager
• Amazon SageMaker AI features: A2I, Clarify, Debugger, GeoSpatial, Ground Truth, Mechanical Turk, Model Monitor, Role Manager, and Studio Lab

Services entering Sunset:

• Amazon WorkSpaces – PCoIP
• Amazon WorkSpaces – Pool
• AWS Managed Services (AMS) Advanced
• AWS re:Post Private
• Amazon Sagemaker AI- Profiler

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

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

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

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

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

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

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

– Daniel Abib

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

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

• Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
• Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
• Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
• Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

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

• AWS Local Zone in Hanoi, Vietnam – This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
• AWS Blocks, an open-source TypeScript framework for application developers (preview) – AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
• Grok 4.3 from xAI in Amazon Bedrock – You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
• Amazon S3 annotations: attach rich, queryable context directly to your objects – Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling – Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs – AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities – Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
• AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
• AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
• Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

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

Price reductions 
AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

• Amazon S3 Vectors reduces query charges by up to 80% for large vector indexes – This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required.
• Amazon GameLift Servers introduces free network bandwidth – Amazon GameLift Servers provides network bandwidth in and out of AWS at no additional charge for all instance types from generation 6 and later, including On-Demand and Spot, with no commitment required. You now pay only for your Amazon GameLift Servers instance hours; all network bandwidth is free.
• AWS Marketplace reduces listing fee for professional services to 0.5% from 2.5% – This reduction makes it more cost-effective for consulting partners, systems integrators, managed services providers and independent software vendors to transact their services through AWS Marketplace, while retaining the procurement and billing benefits that come with it.

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

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

— Channy

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote.

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

New in agents customers create
We’re introducing new capabilities on Amazon Bedrock AgentCore: connecting AI agents to organizational, web, and paid knowledge, helping teams find and fix what’s going wrong in production, and enforcing controls that scale as agents grow more capable.

Together, these capabilities help you build more capable agents faster, govern those agents with controls that scale, and improve them continuously. To learn more, read our blog post covering all the new features.

• Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications – You can build enterprise RAG pipelines with the managed Knowledge Base on Bedrock. It provides native data connectors, Smart Parsing for automatic multi-format data preparation, and an Agentic Retriever for complex multi-step queries, all integrated with AgentCore Gateway so developers can focus on business outcomes rather than infrastructure management.
• Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge – You can use a fully managed web search tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’ secured AWS environment. You can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure.
• AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access – You can use a new Bot Control capability that enables content providers and publishers price, meter, and collect payment from AI bots and agents accessing their content and APIs. AWS WAF now lets you set a price for that access, accept payment through third-party providers, and grant scoped access directly at the edge.
• Amazon Bedrock AgentCore harness in now generally available – You can do building and running production-grade AI agents in minutes, without coding orchestration loops, by defining your agent’s model, tools, skills, and instructions in configuration, with Bedrock AgentCore harness.
• Coming soon: AWS Context – This is a new service that automatically maps the relationships across your existing data into a knowledge graph and provides agentic search so AI agents in the organization can access governed data relationships, business rules, and domain knowledge at runtime.

New in agents for securing

• Introducing AWS Continuum: Security at machine speed – AWS Continuum for code vulnerabilities, available in a gated preview, takes findings from across your environment, prioritizes by business impact, proves which are exploitable, and drives a fix through your own process.
• AWS Security Agent (now part of AWS Continuum) adds threat modeling, Kiro power and Claude Code plugin, and more – You can generate the new threat modeling (preview) to understand the full context of your application and identify threats with recommended mitigations using the STRIDE framework. You can also use pull request code scanning with remediation across major Git platforms, and IDE integrations via Kiro power, Claude Code plugin, and MCP, letting developers run security reviews and fix issues without context switching.

New in agents for building

• Introducing Kiro for iOS – Kiro introduces a native iOS app, available in a gated preview, built for real engineering work that gives developers a new surface to kick off, monitor, steer, and interact with their Kiro sessions directly from their phone. That means you can now start sessions, check back when they’re done, review diffs, and approve changes all while staying connected to your work with no laptop running.
• AWS DevOps Agent adds release management capabilities to assess code changes before production – You can use a new release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments.
• Proactively reduce tech debt autonomously with AWS Transform – continuous modernization – You can use continuous analysis (preview) to automatically scan your code repositories against configurable baselines and generates findings in hours, not weeks. Once you’ve identified and prioritized findings, you can configure autonomous remediations that generate pull requests for affected repositories automatically.

New in agents for works

With the launch of Amazon Quick’s new autonomous agents, you can create agents that work in the background with specific expertise, tone, and access to tools. You can create a finance agent to process orders as they come in, or a sales agent monitoring interactions across your CRM, emails, and Slacks to proactively draft follow-ups, flag risks, or recommend next steps.

We are also releasing a new activity feed that is tailored to how you work. It consolidates email, messaging, calendar, and tasks into a single prioritized view, learns which messages you always answer fast, which threads you skip, and what topics drive your week.

To learn more, look the demo of Amazon Quick – AI Assistant.

In addition to the keynote announcements, we have other important launches this week:

• Amazon S3 annotations: attach rich, queryable context directly to your objects – Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling – Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs – AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities – Strands is an open source toolkit for building production agents. You can use now better context management in the Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.

Updated on June 18, 2026 — Added new important launches on June 18.

Today, we’re announcing Amazon Bedrock Managed Knowledge Base, a new set of capabilities that enables developers to build enterprise-grade generative AI applications with their proprietary data in minutes. Organizations building agentic AI applications need secure, reliable, and up-to-date access to enterprise-wide data to deliver accurate, fast, and trusted outcomes. Managed Knowledge Base abstracts away the complexity of building and managing retrieval-augmented generation (RAG) pipelines, allowing developers to focus on business outcomes rather than infrastructure management.

Developers building knowledge bases for their agents face three key challenges today:

• Connecting to enterprise data: Enterprise knowledge lives across disparate systems with different content types, access control lists, and document formats. Building and maintaining custom connectors for each source adds complexity that slows down development.
• Optimizing RAG accuracy: Best practices for retrieval-augmented generation keep evolving. Developers need to experiment with different parsing strategies, chunking approaches, embedding models, and agentic retrieval behaviors to get accurate answers from their data.
• Managing infrastructure at scale: Organizations need to serve large knowledge bases with millions of documents, or manage thousands of smaller knowledge bases across teams. Both patterns require reliable infrastructure, security enforcement, and cost control.

These challenges require developers to repeatedly perform undifferentiated work instead of focusing on their applications.

Amazon Bedrock Managed Knowledge Base addresses these challenges by abstracting away the multiple infrastructure components developers traditionally have to assemble and maintain themselves (storage, retrieval, embeddings, re-ranking, and foundation model selection) into a single managed primitive. By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself. On top of this managed foundation, three core innovations further improve ease of use and accuracy:

• Native data connectors: Six pre-built ingestion connectors that natively pull enterprise data and permissions from SaaS applications, eliminating the overhead developers face in managing application-specific requirements. At launch, we support Amazon S3, SharePoint, Confluence, Web Crawler, Google Drive, and OneDrive.
• Smart Parsing: Different content types and sources require different approaches to achieve accurate retrieval. Smart Parsing handles this complexity automatically, selecting the right parsing strategy for each data type and connector to provide the highest accuracy for your agents.
• Agentic Retriever: Optimized for complex queries that require multiturn, multihop retrieval within a single knowledge base or across multiple knowledge bases. Agentic Retriever automatically infers end-user intent and draws relevant context from institutional knowledge spread across data sources and modalities.

With just a few lines of code, Amazon Bedrock Managed Knowledge Base automatically manages and scales the end-to-end RAG pipeline that powers your enterprise knowledge agents. For agent builders, it’s available as a pre-built target type in Amazon Bedrock AgentCore Gateway, reducing integration to a few lines of code, auto-generating role-based permissions, and providing observability and evaluation metrics in the AgentCore Observability dashboard.

Getting started with Amazon Bedrock Managed Knowledge Base
Creating a Managed Knowledge Base is straightforward. Navigate to the Amazon Bedrock AgentCore console or the Amazon Bedrock console, open the Knowledge Bases page, and choose Create Managed KB. The experience is the same in both consoles.

Picture 1 – Knowledge Bases list page in the Amazon Bedrock AgentCore console showing the Type column with different KB types and the Create Managed KB button

When creating a new Knowledge Bases, you can connect to your enterprise data sources by choosing from the list of supported connectors directly from a dropdown. AWS Identity and Access Management (IAM) roles are automatically created, and you can choose to edit these permissions if needed:

Picture 2 – Create Knowledge Base page showing the Data source dropdown expanded with all supported connectors: Amazon S3, Confluence, Custom, Google Drive, One Drive, SharePoint, and Web Crawler

An optimized set of defaults will be presented, allowing you to create your knowledge base in just a few clicks. Once the data is synced, you can integrate the knowledge base with your agent or provide it as a tool for your foundation model and start querying.

Smart Parsing for accurate data ingestion
One of the key challenges in building knowledge bases is preparing diverse data types for accurate retrieval. Once you point Managed Knowledge Base at your data sources, Smart Parsing automatically determines the optimal parsing strategy for each data type and connector, no extra configuration is required.

Smart Parsing combines multiple techniques:

• Connector-specific data models: Optimized handling for each data source. For example, the Web Crawler connector preserves HTML structure including embedded images and tables, ensuring rich content is not dropped during ingestion. SharePoint connectors maintain document hierarchy and relationships between files.
• Multimodal processing: Automatic detection and processing of different content types within documents. The system identifies bounding boxes in documents, then sends them to foundation models for data extraction, captioning, and scene description in video files.
• Optimized chunking: Smart Parsing leverages foundation models to understand document structure and extract meaningful content, ensuring that complex documents with mixed formats are properly indexed. Intelligent defaults balance retrieval accuracy with performance based on document type and content structure, while advanced users can customize chunking strategies when needed.

This automated approach eliminates weeks of experimentation typically required to achieve production-quality retrieval accuracy, while still preserving the flexibility to customize when needed.

Using Agentic Retriever for complex queries
After your data is ingested, you can start querying your knowledge base. Generative AI applications often struggle with complex user queries that require reasoning, recursive multi-step retrieval, and intermediate evaluations of results. Consider a user asking two related questions: “What is the cloud infrastructure budget for the ML platform team?” and “Does our expense policy allow prepaying annual commitments?” A single retrieval step might surface documents about the ML platform team but fail to connect the budget information with the expense policy needed to fully answer the question.

Picture 3 – Agentic Retriever decomposes complex user queries into a step-by-step plan, performing multi-hop retrieval across multiple knowledge bases and combining results to deliver accurate, grounded responses

Agentic Retriever solves this by creating a step-by-step query plan: 1. Which team owns the ML platform, and what is their cloud infrastructure budget? 2. What does the expense policy say about prepaying annual commitments? 3. Does the policy allow the ML platform team to prepay against this budget?

The system performs multi-hop retrieval and reasoning at each step, and once it has gathered sufficient relevant passages, it stops the search process and returns the top results. By abstracting away the complexity of building a separate multi-hop reasoning pipeline, this approach dramatically improves accuracy for complex queries while letting developers focus on their agentic search applications instead of orchestration logic.

You can try Agentic Retriever directly from the test panel of your knowledge base in the Amazon Bedrock AgentCore console. Select Agentic retrieval only as the retrieval type to let the system automatically plan and execute multi-step queries across your knowledge bases:

Picture 4 – Test Knowledge Base panel showing Agentic retrieval with answer generation selected as the retrieval type, with model selection and maximum agentic iterations options

Enabling MCP with Bedrock AgentCore
Amazon Bedrock Managed Knowledge Base seamlessly integrates with AgentCore Gateway as a native target type. This integration eliminates the need for manual integration and provides built-in observability, policy enforcement, and automatic permission management.

You can navigate to the Amazon Bedrock AgentCore console or SDK and create an AgentCore Gateway or select an existing one. When adding targets to your gateway, you will find Knowledge Base as a new pre-built target type alongside other options such as MCP server, Lambda ARN, REST API, and other integrations. Simply select your knowledge base ID to expose it through the gateway:

Picture 5 – Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Gateway exposes the standard Model Context Protocol (MCP), so the knowledge base tools are automatically discovered by clients from any MCP-compatible framework, including Strands Agents, LangChain, CrewAI, LlamaIndex, and LangGraph. No custom integration code is required.

Model choice and flexibility
Amazon Bedrock Managed Knowledge Base preserves the flexibility developers expect from Amazon Bedrock. Every foundation model available on Bedrock can power the generation step, and developers can select from different embedding and re-ranking models to optimize retrieval for their specific use case, enabling teams to fine-tune accuracy and cost-performance without changing infrastructure.

Unlike managed solutions that lock you into specific model providers, Amazon Bedrock Managed Knowledge Base separates the infrastructure management (connectors, parsing, storage, retrieval orchestration) from model selection. This means you can:

• Take advantage of the latest models: Adopt the latest embedding, re-ranking, and foundation models as they become available to improve accuracy, latency, and cost for your application without rebuilding your RAG pipeline.
• Optimize for price-performance: Choose smaller, faster models for simple queries and more capable models for complex reasoning tasks, all using the same knowledge base infrastructure.
• Use Bedrock embedding models: While Smart Parsing provides optimized defaults, you can configure Bedrock embedding models when your domain requires specialized semantic understanding.
• Maintain consistency with existing applications: If you’re already using Bedrock Knowledge Bases APIs (Retrieve, StartIngest, StopIngest, IngestKnowledgeBaseDocuments), Managed Knowledge Base uses the same APIs, so migration requires no code changes, just point to the new knowledge base ID.

This approach ensures you can spend time on your generative AI application without losing the ability to change models based on evolving requirements or new model capabilities.

Get started today
Amazon Bedrock Managed Knowledge Base is available today in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney, Tokyo), Europe (Dublin, Frankfurt, London), and AWS GovCloud (US-West) Regions. For Regional availability and future roadmap, visit AWS Capabilities by Region.

With Bedrock Managed Knowledge Base, you pay for what you use with no upfront commitments. Pricing is based on two dimensions: the size of indexed data stored and the number of retrievals performed (on-demand). For detailed pricing information, visit the Amazon Bedrock pricing page. Bedrock is also a part of the AWS Free Tier that new AWS customers can use to get started at no cost and explore key AWS services.

These capabilities work with any open source framework such as CrewAI, LangGraph, LlamaIndex, and Strands Agents, and with any foundation model. Bedrock services can be used together or independently, and you can get started using your favorite AI-assisted development environment with the AgentCore open source MCP server.

To learn more and get started quickly, visit the Bedrock Knowledge Bases Developer Guide.

Daniel Abib

 Updated on June 19, 2026 — Fixed correct screenshots to create a new Managed KB.

Today, we’re announcing the general availability of Web Search on Amazon Bedrock AgentCore, a fully managed tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’s secured AWS environment.

Web Search uses a built-in connector target on Bedrock AgentCore Gateway using the Model Context Protocol (MCP). Your agent sends a natural-language query, and Web Search returns most relevant snippets, source URLs, titles, and publication dates that the model can reason over to produce a grounded response.

It is built on Amazon’s search infrastructure, informed by years of experience powering agentic search experiences across Alexa+, Amazon Quick, and Kiro. It uses a multi-source grounding approach that combines Amazon’s web index with structured knowledge graph data. Beyond standard web results, this gives agents access to Amazon Knowledge Graph with verified facts, helping them retrieve more relevant and accurate responses than traditional web search alone.

With this launch, you can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure. Your AI agent looks at user question, retrieves the latest facts, and then takes any necessary action grounded in current developments beyond a model’s training data. You can also meet enterprise governance policies without sending user prompts and retrieval queries to external search API providers outside of AWS.

Web Search on Bedrock AgentCore in action
To get started, create the Bedrock AgentCore Gateway with Web Search tool target in the Bedrock AgentCore console. When the Gateway URL is created, you can interact with API call, Command Line Interface (CLI), or MCP Inspector.

To add Web Search tool target when creating the Gateway, choose MCP target as a target protocol and Connectors as a target type. You can select the Web Search tool as a preconfigured target to retrieve most relevant web search results including links, snippets, and metadata.

After creating your gateway, you can find the Web Search tool target on the detail page of your gateway. You can also add a new Web Search tool target to an existing gateway.

To interact with Web Search tool, use the sample invocation code in the View invocation code section. You can use code snippets through Python codes with API requests, MCP Python SDK, Strands MCP Client, and MCP Inspector.

For example, you can interact with the MCP Inspector, an interactive developer tool for testing and debugging MCP servers. When you connect to the MCP server through the Gateway resource URL, you will find a Web Search tool for each connector target on the Gateway. Enter input the web search query and choose Run Tool to get the results.

To learn more about how to use Web Search on Bedrock AgentCore, visit the Bedrock AgentCore Gateway documentation.

Customer voices
Some of our customers had early access to this new feature. This is what they shared with us:

Benchling helps scientists accelerate R&D, making it easy to centralize scientific data, collaborate across teams, and access insights. Nicholas Larus-Stone, Head of AI Agents at Benchling shared “Scientists using Benchling AI can now ask about a target they’re actively working on and get answers grounded in both their institutional data in Benchling and published literature. The result is more complete science, and hypothesis generation done right. Because we’re using the Web Search tool on Amazon Bedrock AgentCore, customers have a secure, governed environment to bring that high quality published data into their workflows without compromising how they manage their data.”

Gen Digital leads consumer and small business cyber safety, offering antivirus, antimalware, identity and privacy protection, virtual private networks, and cloud backup. Iskander Sanchez-Rola, Senior Director of AI & Innovation, Gen Digital shared “With the Web Search tool on Amazon Bedrock AgentCore, Norton Revamp helps professionals build their online reputation with current, grounded content ideas shaped by what’s actually happening in the world today. What we value most is that AWS uses its own search index and keep queries within our trusted AWS environment.”

To read more customer stories, visit the Amazon Bedrock Customers.

Now available
Web Search on Amazon Bedrock AgentCore is generally available today in the US East (N. Virginia) Region. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

You can get started with Web Search on Bedrock AgentCore with no upfront commitments. Pricing is simple and usage-based. You are charged based on the number of search queries your agents submit to the web search. Web Search is priced at $7 per 1,000 queries. New AWS customers also receive up to $200 in Free Tier credits. To learn more, visit the Amazon Bedrock AgentCore pricing page.

Try it in the Amazon Bedrock AgentCore console and send feedback to AWS re:Post for Amazon Bedrock AgentCore or through your usual AWS Support contacts.

— Channy

Updated on June 18, 2026 — Added a clear pricing statement for Web Search in Bedrock AgentCore.

This week, New York City is hosting AWS Summit, bringing together builders, customers, and AWS teams for a full day of announcements, demos, and technical sessions at the Javits Center. I wrote blog posts for some of the Summit launches, so I am excited to see them go live this week. I just won’t be watching from the Javits Center. I’ll be at a four-day music festival, following the launches on my phone while trying to figure out how to put up a tent. If you weren’t able to attend in person like me, the keynote livestream is available on June 17, with Dr. Swami Sivasubramanian, VP of Agentic AI, and Chet Kapoor, VP of Security Services and Observability, covering new capabilities across developer tools, AI infrastructure, and security.

Here’s what happened this week.

Headlines
How frontier teams are reinventing AI-native development – Swami published a detailed post this week drawing on data from experiments across hundreds of Amazon engineering teams. The findings are worth reading carefully if you are thinking about how to structure AI adoption on your own team.

A six-engineer team rebuilt the Amazon Bedrock inference engine in 76 days, a project originally scoped for 30 developers over 12 to 18 months. The median productivity gain across structured pilots with Amazon Stores teams was 4.5x in normalized deployment velocity, with some teams exceeding 10x. Perfect Order Experience went from a two-week feature cycle to shipping in an afternoon. WW Grocery cut design document creation from five days to a few hours.

The post distills these results into five practices for becoming a frontier team. First, invest in agent context: build steering files, coding standards, and structured repositories before writing production code. Second, expect an initial slowdown while workflows are restructured, and push through it. Third, maintain a steady backlog of well-scoped tasks so agents can run in parallel without constant supervision. Fourth, make intent explicit through structured specifications before code generation begins. Fifth, shift testing left so agents can self-correct before code reaches the pipeline.

The post closes with a note that commit velocity is only part of the picture, and that a follow-up will cover release management, operations, security operations, and EOL upgrades.

AWS FinOps Agent is now available in preview – AWS FinOps Agent is a new agent for FinOps practitioners and engineering teams that answers cost questions, surfaces optimization opportunities, investigates cost anomalies, and runs recurring FinOps workflows on a defined schedule. You can use it to query your AWS costs, generate cost reports for finance and engineering teams, and surface rightsizing, idle resource, and Savings Plans recommendations from AWS Cost Optimization Hub and AWS Compute Optimizer. The agent can open Jira tickets on your behalf based on those recommendations. When a cost anomaly is detected, FinOps Agent can automatically investigate the root cause and post findings to a Slack channel.

Last week’s launches
I’ll start with one I wrote this week, then cover the other launches that caught my attention:

• Amazon EC2 M9g and M9gd instances are now generally available – Powered by AWS Graviton5 processors and built on the sixth-generation AWS Nitro System, M9g instances deliver up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. Graviton5 is the first processor in the AWS fleet to support PCIe Gen6 and DDR5-8800 memory, and includes a 5x larger L3 cache compared to the previous generation. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon EBS bandwidth on average across sizes compared to M8g. This release also introduces the Nitro Isolation Engine, an enhancement to the Nitro System that uses formal verification to provide mathematically proven isolation between virtual machines — establishing Nitro as the first formally verified cloud hypervisor. M9gd instances add up to 11.4 TB of NVMe SSD local storage with 30% higher IOPS compared to M8gd. Both instance types support Instance Bandwidth Configuration (IBC) for adjusting bandwidth allocation between EBS and VPC networking by up to 25%.
• Anthropic Claude Fable 5 on Amazon Bedrock – Claude Fable 5 launched on Amazon Bedrock on June 9, bringing extended asynchronous task execution, advanced vision capabilities across diagrams, charts, and PDFs, and proactive self-verification. Access requires opting into data sharing via the Data Retention API before invoking the model; Anthropic requires 30-day retention of inputs and outputs for Mythos-class models. Important note on availability: On June 12, Anthropic asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 for all users to support compliance with a US Government export control directive. All other models, including Opus 4.8, are unaffected. Read the Anthropic statement for details. AWS will share further updates as they become available.
• Gemma 4 models are now available on Amazon Bedrock – The Gemma 4 family from Google DeepMind is now available on Amazon Bedrock across three variants: Gemma 4 31B (dense, 256K-token context window, suited for reasoning and coding workloads), Gemma 4 26B-A4B (mixture-of-experts architecture, targeting cost- and latency-sensitive workloads), and Gemma 4 E2B (smallest variant, designed for low-latency interactive use cases). All three support native function calling, structured output, reasoning, response streaming, multimodal input across text, image, video, and audio, and more than 35 languages.
• Amazon OpenSearch Service launches MCP Apps for agentic observability – Amazon OpenSearch Service now supports MCP Apps, enabling observability workflows inside compatible agentic IDEs including Claude Desktop and VS Code. An AI agent in your local environment can investigate incidents using logs, traces, metrics, and alerts stored in OpenSearch domains, collections, and Amazon Managed Service for Prometheus. Each MCP App tool call returns a dual response: a text summary for the agent to reason over and an interactive visualization rendered in the same conversation thread. Available MCP App tools cover log, metrics, and trace investigation; service performance; topology; dynamic visualizations; agent health; cluster health; and instrumentation scoring.

Other AWS news
Here are some additional posts and updates you may find useful:

• AWS CLI v1 enters maintenance mode – When CLI v1 enters maintenance mode, the botocore and s3transfer dependencies will be vendored directly into the CLI v1 codebase rather than installed as separate packages. This means upgrading CLI v1 will no longer update the standalone botocore or s3transfer packages, and installing those packages independently will have no effect on the versions used by CLI v1. Environments with both CLI v1 and boto3 installed will contain separate copies of these libraries. New CLI v1 releases will be limited to critical bug fixes and security issues. The recommended path is to migrate to AWS CLI v2.
• AWS Workload Credentials Provider is now available – AWS Workload Credentials Provider is a lightweight client-side provider that automates deployment of exported certificates from AWS Certificate Manager (ACM) and local caching of secrets from AWS Secrets Manager across AWS and non-AWS workloads. Previously, customers had to build custom EventBridge automation to detect certificate renewals and deploy updates, which becomes harder to maintain as public certificate lifetimes decrease per the CA/Browser Forum mandate. The provider handles this automatically, runs on Windows and Linux, and supports Apache and NGINX. For secrets, it maintains full backwards compatibility with the AWS Secrets Manager Agent.
• Kiro Pro Max is now available – Kiro has introduced a new Pro Max tier, adding higher usage limits, access to the latest frontier models, and additional agentic capabilities for development teams. Kiro Pro Max is designed for professional developers who need sustained, high-volume use across coding, specification generation, and agent-driven tasks.

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

• AWS Summits: AWS Summits are free in-person events covering cloud and AI. Coming up: New York City (June 17), Hong Kong (June 17), Shanghai (June 23-24), Japan (June 25), Washington, D.C. (June 30 – July 1), Taipei (July 15), and Bogotá (July 30).
• AWS Community Days: Community-led conferences planned and delivered by community leaders. Upcoming events include Montreal, Canada (June 20), Indianapolis, USA (June 24), Hangzhou, China (June 28), Bengaluru, India (July 11), and Yaoundé, Cameroon (July 25).

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

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

Today, we’re announcing the availability of Claude Fable 5 on Amazon Bedrock and Claude Platform on AWS. Claude Fable 5 makes Mythos-level capabilities available to customers, with strong safeguards designed to make it safe for broader use. Fable 5 is state-of-the-art on nearly all tested benchmarks and delivers exceptional performance in software engineering, knowledge work tasks, and vision – built for ambitious, long running work.

With Claude Fable 5 on Bedrock, you can build within your existing AWS environment and scale inference workloads. You can also use Claude Fable 5 through the Claude Platform on AWS, giving you Anthropic’s native platform experience.

According to Anthropic, Claude Fable 5 represents a step-change in what you can accomplish with AI models. Here is what makes this model different:

• Long-running, asynchronous execution: Claude Fable 5 handles complex tasks that previous models could not sustain, executing coding and knowledge work tasks for extended periods without intervention.
• Advanced vision capabilities: Claude Fable 5 understands diagrams, charts, and tables nested in files and PDFs. This opens up research and document-heavy work in finance, legal, analytics, architecture, and gaming. In coding, the model implements designs with high fidelity and uses vision to critique its output against goals.
• Proactive self-verification: The model updates its own skills based on learnings and develops its own harnesses and evaluations.

Claude Fable 5 includes safeguards that limit its performance in specific areas where misuse risk is elevated. Harmful prompts related to cybersecurity, biology, chemistry, and health fall back to receive a response from Opus 4.8 instead. Anthropic is able to expand access to nearly all of Claude Fable 5’s state-of-the-art capabilities by developing more powerful safeguards. The same model without these limits is Claude Mythos 5 and it will only be available to a small group of vetted customers.

Claude Fable 5 model in action
You can use Claude Fable 5 in both Amazon Bedrock and Claude Platform on AWS. This post covers guidance on how to access and use on Amazon Bedrock. For guidance on the Claude Platform on AWS, visit the documentation to learn more.

To get started with Amazon Bedrock, you can access the model programmatically now using the Anthropic Messages API to call the bedrock-runtime or bedrock-mantle endpoints through Anthropic SDK. You can also keep using the Invoke and Converse API on bedrock-runtime through the AWS Command Line Interface (AWS CLI) and AWS SDK.

Configure data retention setting
In order to access Claude Fable 5 model, you must opt into data sharing by using the Data Retention API and setting provider_data_share before you can invoke the models. There is no console user interface for this setting at launch.

This mode allows Amazon Bedrock to retain and share your inference data with model providers per their requirements. Anthropic requires 30-day inputs and outputs retention, as well as human review. To learn more, visit the Amazon Bedrock abuse detection.

Updated on September 2, 2026 – AWS introduces a new data retention mode, aws_review; provider_data_share remains as a legacy mode. Starting today, content sharing with model providers is not supported and AWS reviews content only for models whose provider requires human review. If your account or project is already set to provider_data_share, you do not need to take any action to continue using models that require aws_review, including Claude Fable 5 and higher models. Because provider_data_share is the more permissive setting, it continues to satisfy those models’ requirement.

Here is a sample script to set data retention for the bedrock-mantle engine.

curl -X PUT https://bedrock-mantle.us-east-1.api.aws/v1/data_retention \
  -H "x-api-key: <your-bedrock-api-key>" \ 
  -H "Content-Type: application/json" \
  -d '{ "mode": "provider_data_share" }'

If you want to use the bedrock-runtime engine, run this sample script.

curl -X PUT https://bedrock.us-east-1.amazonaws.com/data-retention \
  -H "Authorization: Bearer <your_bearer_token>" \
  -H "Content-Type: application/json" \
  -d '{ "mode": "provider_data_share" }'

Updated on Jun 10, 2026 — You can also use AWS SigV4 (Signature Version 4) to call the data retention API. Configure your AWS CLI or AWS SDK using environment variables.

export AWS_ACCESS_KEY_ID=your_access_key_id
export AWS_SECRET_ACCESS_KEY=your_secret_access_key
export AWS_SESSION_TOKEN=your_session_token

First, retrieve your current Bedrock data retention settings.

curl -s https://bedrock.us-east-1.amazonaws.com/data-retention \
  --aws-sigv4 "aws:amz:us-east-1:bedrock" \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  -H "x-amz-security-token: $AWS_SESSION_TOKEN"

This should return something like this: {"mode":"inherit","updatedAt":null} and update the data retention settings.

curl -s -X PUT https://bedrock.us-east-1.amazonaws.com/data-retention \
  --aws-sigv4 "aws:amz:us-east-1:bedrock" \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  -H "x-amz-security-token: $AWS_SESSION_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"provider_data_share"}'

If everything worked as expected, you should receive a response like: {"mode":"provider_data_share","updatedAt":"2026-06-10T16:51:39.331Z"}.

The latest AWS CLI supports configuring the data retention setting. Set your bearer API key as an environment variable after you generate a API key in the Bedrock console.

export AWS_BEARER_TOKEN_BEDROCK=bedrock-api-key-XXXXXXXXXX

aws bedrock put-account-data-retention \ 
  --mode provider_data_share

To learn more, visit the Data Retention API on the Amazon Bedrock User Guide.

How to use the Claude Fable 5 model
Let’s start with Anthropic SDK for Python using the Messages API on bedrock-mantle endpoint. Install Anthropic SDK.

pip install anthropic

Here is a sample Python code to call Claude Fable 5 model:

import anthropic

client = anthropic.Anthropic(
    base_url="https://bedrock-mantle.us-east-1.api.aws/anthropic",
    api_key= <your-bedrock-api-key>
)

message = client.messages.create( 
     model="anthropic.claude-fable-5", 
	 max_tokens=4096, 
	 messages=[ 
	     { "role": "user", 
		   "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions", 
		 }, 
	 ], 
)

print(message.content[0].text)

To learn more, check out Anthropic Messages API code examples and notebook examples for multiple use cases and a variety of programming languages.

You can use Claude Fable 5 in the Bedrock console. Choose Claude Fable 5 in the Playground and test it.

You can also use Claude Fable 5 with the Invoke API and Converse API on bedrock-runtime endpoint. Here’s an example to call Converse API for a unified multi-model experience using the AWS SDK for Python (Boto3):

import boto3 
bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1") 
response = bedrock_runtime.converse( 
    modelId="global.anthropic.claude-fable-5", 
    messages=[ 
        { 
            "role": "user", 
            "content": [ 
                { 
                    "text": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions." 
                } 
            ] 
        } 
    ], 
    inferenceConfig={ 
        "maxTokens": 4096 
    } 
) 
print(response["output"]["message"]["content"][0]["text"]) 

To learn more, visit code examples that show how to use Amazon Bedrock Runtime with AWS SDKs.

Things to know
Let me share some important technical details that I think you’ll find useful.

• Model access: Claude Fable 5 access is gradually expanding for all AWS accounts. If your account doesn’t have access yet, it will be enabled soon depending on your Bedrock usage. If you want to get access to this model quickly, contact your usual AWS Support.
• Pricing: When a harmful prompt is routed to Opus 4.8 instead of Fable 5, you pay only Opus prices. If a request is blocked mid-conversation, initial tokens are charged at Fable rates and subsequent tokens at Opus rates. To learn more, visit the Amazon Bedrock pricing page.
• Data retention: For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary.
• Claude Mythos 5 on Bedrock (Limited Preview): You can also use Anthropic’s most capable model for cybersecurity and life sciences, including vulnerability discovery, drug design, and biodefense screening. Access is currently limited due to the dual-use nature of these domains. To learn more, visit the model card documentation.

Now available
Anthropic’s Claude Fable 5 model is available today on Amazon Bedrock in the US East (N. Virginia) and Europe (Stockholm) Regions; check the full list of Regions for future updates. Claude Fable 5 is also available on the Claude Platform on AWS in North America, South America, Europe, and Asia Pacific.

Give Claude Fable 5 a try with the Amazon Bedrock APIs, in the Claude Platform on AWS, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

Editors Note: Updated on September 2, 2026 – AWS introduces a new data retention mode, aws_review; provider_data_share remains as a legacy mode. Starting today, content sharing with model providers is not supported and AWS reviews content only for models whose provider requires human review. If your account or project is already set to provider_data_share, you do not need to take any action to continue using models that require aws_review, including Claude Fable 5 and Claude Fable 5.1. Because provider_data_share is the more permissive setting, it continues to satisfy those models’ requirement.

Editors Note: Updated on July 1, 2026 – Access to Claude Fable 5 and Claude Mythos 5 on AWS has been restored

Editors Note: Updated on June, 12, 2026 – Claude Fable 5 and Claude Mythos 5 on Amazon Bedrock access unavailable. To support compliance with the US Government export control directive, Anthropic has asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 for all users. All other models, including Opus4.8, are not affected and you can continue using them in full confidence. Please view the Anthropic statement for further details.

Editors Note: Updated on June 10, 2026 – Added how to configure data retention setting through AWS SigV4 and AWS CLI.

Editors Note: Updated on June 9, 2026 – 1) Updated the console screenshot. You can use the console on bedrock-runtime engine. The console support on bedrock-mantle is coming soon. 2) Fixed the right model id in the sample code, 3) Fixed correct provider_data_share parameter, 4) Add a data retention setting script for bedrock-runtime engine.

This week, the AWS IoT Device SDK for Swift reached general availability. As a member of the Swift Server Workgroup (SSWG), this one caught my attention. The SDK brings production-ready MQTT 5 connectivity, Device Shadow, Jobs, and fleet provisioning to Swift developers on macOS, iOS, tvOS, and Linux.

I’m curious to see what you will build with it. Swift on the server has matured over the past few years, and now it reaches IoT devices too. This connects to a broader trend of running Swift at the edge. WendyOS, for example, is an open-source operating system for physical AI that offers first-class Swift support for deploying apps to NVIDIA Jetson and Raspberry Pi hardware. Between server-side Swift, IoT, and edge computing, the language is showing up in places that would have surprised most people a few years ago.

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

Headlines
Amazon RDS for SQL Server supports Bring Your Own Media – Customers who migrate SQL Server applications from on-premises environments can now reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program on Amazon RDS. BYOM is integrated with AWS License Manager for tracking license usage and compliance. Read more.

Amazon Cognito now supports multi-Region replication – You can now synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region in near real-time. In the event of a disruption in the primary Region, signed-in users continue accessing their applications without re-authenticating, and registered users can sign in with their existing credentials. Multi-Region replication is available as an add-on for user pools in Essentials or Plus feature tiers across 16 Regions. Read more.

GPT-5.5, GPT-5.4, and Codex from OpenAI are now generally available on Amazon Bedrock – You can now use GPT-5.5 and GPT-5.4 in production workloads on Amazon Bedrock and build with Codex for AI-powered software development, with the same security, governance, and operational controls you already use across AWS. GPT-5.5 is the most capable model from OpenAI, excelling at agentic coding, data analysis, and multi-step autonomous tasks. Codex is available through the Codex App, the Codex CLI, and IDE integrations with Visual Studio Code, JetBrains, and Xcode. Pricing matches OpenAI first-party rates, and usage counts toward existing AWS commitments. Read more.

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

• Amazon Bedrock adds CloudWatch metrics for OpenAI- and Anthropic-compatible APIs – You can now monitor inference traffic to the bedrock-mantle endpoint with CloudWatch metrics, including inference counts, input and output token totals, and client error counts at account, project, model, and project-and-model granularity.
• Amazon Bedrock launches a redesigned console optimized for OpenAI- and Anthropic-compatible APIs – A refreshed console workflow with a model catalog, side-by-side comparison, project-based organization, and project-aware documentation with pre-filled code snippets.
• Amazon Bedrock AgentCore Identity now supports bring-your-own secrets with AWS Secrets Manager – Customers can now reference existing AWS Secrets Manager secret ARNs in AgentCore Identity Credential Providers, enabling full ownership of secrets governance including custom CMKs, tagging strategies, and automatic rotation.
• AWS Step Functions adds AgentCore-powered agentic reasoning step – You can now add AI agent reasoning steps to your Step Functions workflows through an integration with the managed harness in Amazon Bedrock AgentCore. Run multiple agents in parallel or sequence, add human approval, and trace every agent decision.
• Amazon EKS and Amazon EKS Distro now support Kubernetes version 1.36 – Kubernetes 1.36 promotes User Namespaces to GA, introduces Mutating Admission Policies, In-Place Pod-Level Resources Vertical Scaling, and Resource Health Status reporting. Available in all Regions where EKS is available.
• Amazon ECS Managed Instances now supports AWS Trainium and AWS Inferentia – You can now create an ECS Managed Instances capacity provider with Inferentia2, Trainium1, and Trainium2 instance types and have Amazon ECS automatically allocate all accelerator resources to your workload.
• Amazon Quick now supports VPC connectivity for MCP connections – Enterprise customers can now connect privately hosted Model Context Protocol (MCP) servers to Amazon Quick through VPC, enabling secure access to proprietary applications and internal tools without exposing them to the internet.
• AWS Cost and Usage Report 2.0 now supports Athena and Redshift integration – CUR 2.0 exports are automatically delivered in the optimal format for your chosen query engine, with supporting infrastructure templates, table definitions, and data loading instructions.
• Amazon Location Service announces public transit and intermodal routing – The Routes API now supports two new travel modes, Transit and Intermodal, to plan journeys combining public transportation with walking, driving, taxi, and rental segments across 13 Regions.

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

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

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

Today, we’re announcing a new console experience in Amazon Bedrock for you to experiment, iterate, and scale with the latest AI models on Amazon Bedrock’s next-generation inference engine built for high performance, reliability, and security. This console has a refreshed workflow optimized for bedrock-mantle endpoint, which supports the latest GPT, Claude, and open-weight models with the OpenAI Responses API, OpenAI Chat Completions API, and the Anthropic Messages API.

The new console experience makes it simple to find the right model and move quickly from evaluation to production.

• New model card: You can browse the full model catalog, compare them side by side on capabilities, modality support, context window, and applicable service quotas in a single view, removing the need to stitch together documentation, and limit calculators.
• Project-based work: You can make a project to run evaluations and review usage insights in one streamlined workflow that mirrors the lifecycle of building a generative AI application.
• Live documentation: You can use project-aware live documentation: code samples, SDK snippets, and API references are automatically prefilled with your project variables. You can copy a snippet straight from the console into your application and run it without modification.

How to get started
You can try a new experience by choosing Try the new Bedrock Console from within the Amazon Bedrock console, or by using the new console link directly.

You can find a project-based dashboard to show inference requests and error by range of recent dates, recently used models, and the project list. You can create a project, assign models, configure API keys, and start making inference requests in minutes.

A new model catalog shows the latest GPT, Claude, and open-weight models that are supported on the bedrock-mantle engine. You can see the details of features, tokens, pricing, input/output, pricing information, and Regional availability. You can also compare up to 3 models in a single view.

When you choose the project dashboard, you can see the models used in the project, the distribution of your token usage such as total token usage, token usage per minute, inference requests per minute, and tokens per inference request. This can inform your model selection, prompt optimization, and workload consistency decisions.

You can select up to 3 models to start evaluating to compare responses side by side with the same prompt.

To build your application in the project, choose Getting started. You can migrate existing code, build a new app with the Anthropic or OpenAI SDK, or connect an AI coding assistant to Bedrock.

Choose the API & SDK, your SDK (either Anthropic or OpenAI), your preferred programming language, and your authentication method. It shows your environment code to run these in your terminal for a quick test, or save to a .env file for your application. You can also send your first request with sample code snippets to verify your setup.

When you choose Clients, you can select the AI coding agent source such as Claude Code, Cline, Codex, Cursor, or OpenCode that you want to connect to the bedrock-mantle engine. It provides instructions on how to install the AI agent, use your AWS IAM credentials or use a Bedrock API key, set environment variables, and route requests from each AI agent through Bedrock.

To learn about Anthropic- and OpenAI-compatible APIs, choose Live API docs. You can choose Anthropic API Protocol for access to Claude model features like the Messages API or OpenAI API Protocol for access to features like Responses API.

For example, when you choose OpenAI Response API, it retrieves a model response with the given model ID. These API references are automatically prefilled with the project’s selected model ID, Region, bedrock-mantle endpoint URL, and API key reference, and they update in place as you change models or settings.

You can also choose the existing Bedrock console to manage fully-managed features such as Agents, Knowledge Bases, Guardrails, fine-tuning, or the InvokeModel and Converse APIs to run on the bedrock-runtime endpoint.

Now available
The new console experience is available in all AWS Regions where the bedrock-mantle endpoint is offered: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Jakarta, Mumbai, Sydney, Tokyo), Europe (Frankfurt, Ireland, London, Milan, Stockholm), and South America (São Paulo). Check the full list of Regions for future updates.

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

— Channy

Updated on July 13, 2026 – Fixed the first console image to reflect the latest UI.

As we previewed in What’s Next with AWS 2026, we’re announcing the availability of OpenAI GPT-5.5, GPT-5.4 models, and Codex on Amazon Bedrock, giving you access to frontier models and a coding agent for software development.

According to OpenAI, GPT-5.5 and GPT-5.4 models are excellent for coding, reasoning, agentic workflows, and complex professional work. You can use GPT-5.5 for the hardest customer workloads and GPT-5.4 for the best price-performance. You can call them through Responses API on Amazon Bedrock’s next-generation inference engine built for high performance, reliability, and security.

Codex is the OpenAI coding agent for AI-powered software development. According to OpenAI, more than 4 million developers use Codex every week to write, refactor, debug, test, and validate code across large codebases. With GPT-5.5 powering inference, Codex introduces a new class of intelligence optimized for complex, long-horizon developer workflows. You can use the Codex App, the Codex CLI, and IDE integrations with Visual Studio Code, JetBrains, and Xcode, with all model inference routed through the Responses API on Amazon Bedrock.

For customers with data residency requirements, all processing stays within the Bedrock Region you select. You pay per token with no seat licenses and no per-developer commitments.

GPT-5.5 and GPT-5.4 models on Bedrock in action
You can access the model programmatically using the OpenAI Responses API to call the bedrock-mantle endpoints through the OpenAI SDK, command-line tools such as curl.

Let’s start with OpenAI SDK for Python. Install OpenAI SDK.

pip install -U openai

Set the environment variables for authentication.

export OPENAI_BASE_URL="https://bedrock-mantle.us-east-2.api.aws/openai/v1"
export OPENAI_API_KEY="<BEDROCK_API_KEY>"
export BEDROCK_OPENAI_MODEL_ID="openai.gpt-5.5"

Here is a sample Python code to call GPT-5.5 model on Bedrock:

import os
from openai import OpenAI
 
client = OpenAI(
    base_url=os.environ["OPENAI_BASE_URL"],
    api_key=os.environ["OPENAI_API_KEY"],
)
 
response = client.responses.create(
    model=os.environ["BEDROCK_OPENAI_MODEL_ID"],
    input=[
        {
            "role": "developer",
            "content": "You are a software engineer with excellent AWS cloud knowledge. Be concise and practical.",
        },
        {
            "role": "user",
            "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions.",
        },
    ],
    reasoning={"effort": "medium"},
    text={"verbosity": "low"},
)
 
print(response.output_text)

You can call directly the model endpoint using curl.

curl "$OPENAI_BASE_URL/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "openai.gpt-5.5",
    "input": [
      {
        "role": "developer",
        "content": "You are a software engineer with excellent AWS cloud knowledge."
      },
      {
        "role": "user",
        "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
      }
    ],
    "reasoning": {"effort": "medium"},
    "text": {"verbosity": "low"}
  }'

You can use the Responses API when you want to use model-managed multi-turn state, need hosted tools, function tools, or richer tool orchestration, and run background or long-running work. To learn more, visit the OpenAI Cookbook Responses examples and getting started guide.

Using OpenAI Codex with GPT-5.5 on Amazon Bedrock
You can download Codex CLI, Codex App or Codex VS Code extension and get started with the Bedrock for model inference. Codex supports two Bedrock authentication pathways: Amazon Bedrock API key or AWS SDK credential chain. If you set AWS_BEARER_TOKEN_BEDROCK, Codex uses it first; otherwise Codex falls back to AWS SDK credential chain.

Set AWS_BEARER_TOKEN_BEDROCK in the environment that Codex will read:

export AWS_BEARER_TOKEN_BEDROCK=<your-bedrock-api-key>

Then, configure your preferred Region and set the model ID to openai.gpt-5.5 in ~/.codex/config.toml, which is required for Bedrock API-key authentication. You can also choose openai.gpt-5.4, openai.gpt-oss-120b, or openai.gpt-oss-20b. For the desktop app or VS Code extension, put any environment variables the app needs in ~/.codex/.env.

model = "openai.gpt-5.5"
model_provider = "amazon-bedrock"
[model_providers.amazon-bedrock.aws]
region = "us-east-2"

Restart the desktop app or VS Code extension after changing ~/.codex/config.toml or ~/.codex/.env. In Codex CLI, you should see a /status tab that looks like this:

In Codex App, you can use GPT-5.5 model through Amazon Bedrock inference.

To learn more about how to configure Codex to use OpenAI models on Amazon Bedrock, visit Use Codex with Amazon Bedrock.

Things to know
Let me share some important technical details that I think you’ll find useful.

• Model latency: OpenAI model information positions GPT-5.5 as fast and GPT-5.4 as medium speed, but customer-perceived latency depends on reasoning effort, output length, tool calls, background mode, Region, quotas, throttling, prompt size, and cache hits. Start GPT-5.5 at medium effort. Start GPT-5.4 with effort set explicitly rather than relying on its none default.
• Scaling and capacity: Bedrock’s new inference engine is designed to rapidly provision and serve capacity across many different models. When accepting requests, we prioritize keeping steady state workloads running, and ramp usage and capacity rapidly in response to changes in demand. During periods of high demand, requests are queued, rather than rejected.

Now available
OpenAI GPT models and Codex on Amazon Bedrock are available today: GPT-5.5 model in the US East (Ohio) Region, GPT-5.4 model in the US East (Ohio) and US West (Oregon) Regions. Check the full list of Regions for future updates. To learn more, visit the OpenAI on Amazon Bedrock page and the Amazon Bedrock pricing page.

Give GPT-5.5, GPT-5.4 models, and Codex on Amazon Bedrock a try today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

Updated on June, 1, 2026 – The GPT 5.5 and 5.4 models now support the Responses API only on Amazon Bedrock, and console support is coming soon.

Updated on June, 3, 2026 – Amazon Bedrock now supports GPT‑5.4 from OpenAI in AWS GovCloud (US-West) Region.

Updated on June, 7, 2026 – You can use the GPT 5.5 and 5.4 models in the new Amazon Bedrock console.

In my last Week in Review post, I shared what I’d been hearing from customers in the AI-Driven Development Lifecycle (AI-DLC) workshops I’ve been delivering. Last week I was back at it, this time in Denver for a two-day AI-DLC workshop, where I helped facilitate 17 teams to deliver nearly 20 separate use cases in just two days. The pace of acceleration that AI-DLC unlocks—especially when paired with tools like Claude Code on Amazon Bedrock—is fundamentally changing how businesses operate. Traditional roles within software development teams are collapsing into smaller, AI-augmented squads, and the paradigm shift is beginning to take place right in front of us. To learn more about how to utilize various AI tools, visit the GitHub repository of AI-DLC workflow.

This shift is also reshaping how AWS account teams (solutions architects, customer solutions managers, and technical account managers) collaborate with customers. It’s becoming less about handing off advisory design documents and more about building alongside them in real time. It’s a genuinely exciting moment to be in the middle of the change, and this week’s headline launch — Anthropic’s most capable model yet, now on AWS, is going to push that pace even further.

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

Headlines
Claude Opus 4.8 on AWS – Anthropic’s most capable generally available model is now accessible through both Amazon Bedrock and the Claude Platform on AWS. Opus 4.8 is built for agentic coding, knowledge work, and extended autonomous task execution — it sustains longer autonomous sessions with deeper reasoning, recovers from errors, and synthesizes information across lengthy documents. For coding workloads, it reads codebases like an engineer, plans before it edits, and holds context across long sessions. On Amazon Bedrock, you get AWS-managed features like Guardrails, Knowledge Bases, and data residency; on the Claude Platform on AWS, you get Anthropic’s native APIs unified with AWS billing. To learn more, visit the deep-dive blog post.

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

• Introducing the next generation of AWS Resilience Hub – A reimagined Resilience Hub gives SREs and developers a unified framework to define resilience standards, evaluate applications against them, and demonstrate compliance across an entire portfolio. It introduces modular resilience policies (covering service-level objectives (SLOs), multi-AZ/Region DR, and data recovery), business-oriented application modeling, generative AI-powered assessments aligned with the Well-Architected and Resilience Analysis Frameworks, and automatic dependency discovery via DNS query log analysis. Integration with AWS Organizations enables organization-wide resilience management from a single delegated administrator account.
• Introducing the next generation of Amazon OpenSearch Serverless for building agentic AI applications – Amazon OpenSearch Serverless is now a fully managed search and vector engine purpose-built for agentic AI applications. It scales from zero to thousands of requests per second—roughly 20x faster than the prior generation—delivers up to 60% cost savings versus peak-provisioned clusters, and adds GPU acceleration plus new SEARCH and VECTORSEARCH collection types. Native integrations with Vercel, Kiro, Claude Code, and Cursor through OpenSearch Agent Skills make it straightforward to plug into your agent stack.
• New assessment capabilities in AWS Transform – AWS Transform expands with new tools to help you build migration business cases and evaluate TCO before moving workloads to AWS. You can ingest data from RVTools exports, CMDB data, the AWS Transform discovery tool, and third-party discovery tools, then run what-if scenarios across region, utilization, and service mapping for EC2, FSx, S3, SQL Server on EC2, and virtual desktops. The release also adds Agentic Readiness Analysis (ARA) and Modernization Analysis (MODA), which scan code repositories in 5 to 30 minutes per repo to surface severity-tagged findings with file-level evidence and AWS-mapped remediation guidance.
• Amazon Aurora MySQL with Kiro Powers — Aurora MySQL now integrates with Kiro Powers, drawing from a curated repository of pre-packaged MCP servers, steering files, and hooks validated by Kiro partners. Developers can execute both data plane tasks (queries, schema management) and control plane tasks (cluster management) in natural language, with dynamic guidance for Aurora MySQL Serverless scaling, RDS-to-Aurora migration, and replication setup. The companion Database Blog post explains how the agent produces the API calls, SQL, and configuration for you to review and run — available via one-click install from the Kiro IDE or webpage.
• Amazon WorkSpaces Applications now supports Windows Desktop OS – You can now bring your own Windows Desktop licenses to Amazon WorkSpaces Applications and stream full Windows desktops and applications from AWS-hosted dedicated hardware. BYOL eliminates OS fees (you pay only for compute and streaming infrastructure), supports eligible Microsoft 365 Apps for enterprise, and gives users a matching experience between local and remote environments — same workflows, shortcuts, and navigation in both.

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

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

• Meet our newest AWS Heroes — May 2026 – An AWS Builder Center post introducing the latest cohort of AWS Heroes recognized for their contributions to the global AWS community.
• AI-native, full-stack web apps with Vercel and AWS databases – Database blog post on the new Vercel and AWS Databases integration, which lets you provision Aurora PostgreSQL, DynamoDB, or Aurora DSQL directly from the Vercel dashboard or via v0. The post also highlights the H0 hackathon with $160,000 in prizes for building full-stack apps on this stack.
• Introducing US-based, US citizen 24/7 technical support for AWS GovCloud (US) customers: Your mission never sleeps. Neither do we – Public Sector blog post announcing that all AWS GovCloud (US) technical support cases are now automatically routed to US-based, US citizen engineers around the clock, no opt-in required, for government agencies, contractors, nonprofits, and academic institutions running mission-critical workloads.

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

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

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

-Micah

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

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

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

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

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

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

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

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

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

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

— Channy