Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore

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.

— seb

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Amazon DynamoDB now supports real-time vector search at any scale

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

— Esra

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AWS Weekly Roundup: Price reduction of GPT models in Bedrock, CloudWatch managed collectors for Prometheus metrics, and more (August 3, 2026)

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.

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

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AWS Weekly Roundup: Local Zone in Athens, Claude Opus 5 on AWS, Lambda durable execution for .NET, and more (July 27, 2026)

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, and Amazon ECS.

AWS Local Zones place AWS infrastructure much closer to large population and industry hubs, enabling applications that require single-digit millisecond latency, such as real-time gaming, media production, and financial services, to run where end users actually are. For builders in Greece, you can now run latency-sensitive workloads locally while connecting seamlessly to the nearest AWS Region for services that don’t require low latency, giving you the flexibility to architect hybrid, latency-optimized applications 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!

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AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)

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

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SP-API Updates: Listings Items API adds multi-marketplace support and July listing attribute & enumeration updates

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Amazon SQS turns 20: Two decades of reliable messaging at scale

On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3. We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies. If one service called another directly and that service was slow or unavailable, failures cascaded through the entire system. Message queuing solved this by letting services communicate asynchronously: a producer could drop a message into a queue and move on, while a consumer picked it up when ready. This approach kept individual service failures from affecting the rest of the system.

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. The scale, performance, and operational controls around it look very different now though.

Jeff Barr covered the first 15 years of SQS milestones in his 15th anniversary post, from the original 8 KB message limit in 2006 through FIFO queues, server-side encryption, and Lambda integration. Over the last five years, we have continued to scale SQS, added stronger security defaults, and introduced new capabilities that address increasingly complex workload patterns.

Key milestones between 2021 and 2026
High throughput mode for FIFO queues (2021): In May 2021, we launched general availability of high throughput mode for FIFO queues, supporting up to 3,000 transactions per second (TPS) per API action, a tenfold increase over the previous limit. We continued raising this ceiling over the following two years: to 6,000 TPS in October 2022, to 9,000 TPS in August 2023, and to 18,000 TPS in October 2023, before reaching 70,000 TPS per API action in select Regions by November 2023.

Server-side encryption with SSE-SQS (2021): In November 2021, we introduced server-side encryption with Amazon SQS-managed encryption keys (SSE-SQS), giving customers an encryption option that required no key management. In October 2022, we made SSE-SQS the default for all newly created queues, so customers no longer needed to explicitly enable it.

Dead-letter queue redrive enhancements (2021): We progressively expanded how customers recover unconsumed messages from dead-letter queues. In December 2021, we added DLQ redrive to source queue directly in the SQS console. In June 2023, we extended this capability to the AWS SDK and CLI through new APIs, including StartMessageMoveTask, CancelMessageMoveTask, and ListMessageMoveTasks. In November 2023, we added redrive support for FIFO queues.

Attribute-based access control, ABAC (2022): In November 2022, we introduced ABAC, giving customers the ability to configure access permissions based on queue tags rather than maintaining static policies as resources scaled.

JSON protocol support (2023): In November 2023, we added support for the JSON protocol in the AWS SDK, reducing end-to-end message processing latency by up to 23% for a 5 KB payload and lowering client-side CPU and memory usage.

Amazon EventBridge Pipes console integration (2023): We added the ability to connect a queue directly to EventBridge Pipes from the SQS console, routing messages to a broad range of AWS service targets without writing custom integration code.

Extended Client Library for Python (2024): We brought the Extended Client Library, previously available for Java, to Python developers, allowing messages up to 2 GB to be sent through SQS by storing the payload in Amazon S3 and passing a reference through the queue.

FIFO in-flight message limit increase (2024): We increased the in-flight message limit for FIFO queues from 20,000 to 120,000 messages, so consumers can process significantly more messages concurrently without being constrained by the previous ceiling.

Fair queues for multi-tenant workloads (2025): We introduced fair queues to mitigate the noisy neighbor problem in multi-tenant standard queues. By including a message group ID when sending messages, customers can prevent a single tenant from delaying message delivery for others, without any changes required on the consumer side.

1 MiB maximum message payload size (2025): We increased the maximum message payload from 256 KiB to 1 MiB for both standard and FIFO queues, helping customers send larger messages without offloading data to external storage. AWS Lambda event source mapping for SQS was updated in parallel to support the new payload size.

The constant underneath the change
Despite two decades of feature additions, the fundamental use case for SQS has not shifted. Customers use it to decouple services, buffer bursts of traffic, and build systems that stay resilient when individual components fail. That same pattern now extends to AI workloads. Customers use SQS queues to buffer requests to large language models, manage inference throughput, and coordinate communication between autonomous AI agents operating as independent services. For an example of this architecture in practice, read Creating asynchronous AI agents with Amazon Bedrock.

To learn more about Amazon SQS, visit the Amazon SQS product page, review the developer guide, or explore recent updates on the AWS Blogs.

— Esra

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AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering everything shipped over the past year: AWS Capabilities by Region (1,500+ services across 37 Regions), Spaces for community-created groups, workshops with category and complexity filters, badges and streaks, article series, view counts, saved items, student status, availability notifications, sign-in with GitHub and Amazon, and sandbox environments.

Jeff Barr published a retrospective summarizing Builder Center’s first year. Since launch, 5,548 authors have published 6,448 articles with more than 10.4 million page views combined. Builders have earned 99,226 badges since the badge system launched in March 2026. Community members have submitted 565 wishes, 10 of which have shipped with another 20 on the near-term roadmap.

The top community article Building an AWS Study Buddy with MCP + Strands Agents SDK by Dineshraj Dhanapathy reached 50,000+ views. Chris Miller’s Migrating an EOL Linux Server to AWS in 8 Hours with Kiro followed at 45,000+, and Yash Aggarwal’s AIdeas: NeuroVoice – Multimodal AI for Early Screening of Neurological Diseases article reached 38,000+.

The week’s headline addition is Sandbox Environments by Rick Suttles. Sandboxes give you a free, pre-provisioned AWS account to complete a workshop exercise. Each environment is active for 8 hours, after which the account and all its resources are automatically de-provisioned. You can have one active sandbox at a time and request one per week. No personal AWS account, credit card, or manual cleanup required.

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

• AWS Security Hub introduces Network Scanning – Security Hub introduced Network Scanning, a capability that identifies resources in your environment that are reachable from the public internet. Network Scanning probes your resources from the internet to detect actual reachability, complementing the existing network reachability findings in Security Hub that identify configurations that could make a resource reachable. It discovers public IP addresses, virtual machines, and load balancers across your AWS and Azure environments, identifies reachable ports, and determines what services are running behind them. Each reachable port generates a Security Hub finding with evidence of the port and service discovered. Security Hub Exposures then automatically correlates these findings with other findings and resource configurations to determine broader risk. Existing customers can enable Network Scanning in individual accounts and Regions, or across an organization through a configuration policy. For new customers, Network Scanning is on by default. It is included with Security Hub Essentials at no additional cost.
• Security Hub also extends unified security management to Microsoft Azure – Security Hub now monitors Microsoft Azure resources, providing unified posture management, vulnerability management, and security response across both clouds. It automatically discovers Azure VMs, container images, Function Apps, and identities, and evaluates them for misconfigurations, internet exposure, and software vulnerabilities. AWS and Azure findings appear in the same prioritized view with the same formats and automation workflows.
• Amazon SageMaker Studio integrates with Hugging Face for one-click model deployment and customization – You can now go from discovering a model on Hugging Face to working with it in SageMaker Studio in a single click. Select any supported model on Hugging Face and choose “Customize on SageMaker AI” or “Deploy on SageMaker AI” to land directly on the corresponding workflow page with the model pre-loaded. New customers receive a Studio environment created in seconds with pre-configured permissions for serverless model customization (including fine-tuning with custom reward functions for reinforcement learning), model evaluation, and deployment to SageMaker or Bedrock endpoints. Verified customers receive default GPU access to G5, G6, and G4dn instances without requesting quota increases, and quota utilization is visible directly inside the Studio environment.
• Amazon EKS Auto Mode and Amazon ECS Managed Instances reduce GPU management fees by up to 60% – Beginning July 1, 2026, EKS Auto Mode and ECS Managed Instances reduce management fees for accelerated instance types: G-series fees are down 35%, and P-series and AWS Trainium fees are down 60%. The reductions apply automatically to existing clusters and require no action from customers. Both services include capabilities built for accelerated workloads. EKS Auto Mode provides automatic parallel image pulling on GPU instances with local NVMe storage and accelerator-aware node repair. ECS Managed Instances provides GPU metrics through Amazon CloudWatch Container Insights and automatic health monitoring for GPU hardware failures.
• Amazon Aurora DSQL change data capture (CDC) is now generally available – Aurora DSQL CDC streams the results of insert, update, and delete operations as change events to Amazon Kinesis Data Streams. You can use it to synchronize data across microservices, trigger Lambda functions, or deliver changes to S3, Redshift, and OpenSearch Service through Amazon Data Firehose. CDC streaming is designed to have zero impact on database workload performance and requires no infrastructure to manage.

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:

• Building secure AI agents at scale: Introducing Loom for AWS – Loom is an open-source enterprise platform for building agents with AWS Strands Agents and deploying them on Amazon Bedrock AgentCore Runtime. It provides a unified management UI and backend API with identity provider integration, scope-based authorization, multi-persona navigation, and full lifecycle management for agents, memory, MCP servers, and agent-to-agent integrations. Loom enforces automated resource tagging for cost attribution, implements RBAC and ABAC for multi-tenant security, uses paved-path blueprints for agent deployments, manages identity propagation through delegated actor chains, integrates with AWS Agent Registry for discovery and governance, and supports human-in-the-loop review before sensitive actions. The project is available in AWS Labs on GitHub.
• Introducing Claude apps gateway for AWS – The Claude apps gateway is a self-hosted control plane that gives organizations centralized control over access, cost, and policy for Claude Code and Claude Desktop. It connects to any OIDC-compliant identity provider, enforces managed settings on every request, routes inference to Amazon Bedrock or Claude Platform on AWS, and supports per-user and per-group spend caps. The gateway runs as a stateless container in your private network, backed by a PostgreSQL database for short-lived sign-in state. No long-lived secrets are stored on developer machines. Deploy it through Amazon Bedrock to keep data within the AWS security boundary, or through Claude Platform on AWS for the native Claude platform experience.
• Introducing OAuth support for AWS MCP Server – You can now connect agents to the AWS MCP Server using browser-based OAuth with the same credentials you use for the AWS Console or CLI. The new sign-in path supports IAM federation, AWS IAM Identity Center, and root or IAM users. AWS Sign-In issues short-lived access tokens and refresh tokens, with automatic token management so developers stay authenticated across restarts. For headless use cases, a non-interactive flow lets applications with existing AWS credentials obtain OAuth access tokens through the create-oauth2-token-with-iam API. New governance controls include OAuth-specific IAM condition keys, token introspection and revocation, dynamic client registration, and CloudTrail audit elements.

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, think big, and make new connections. Coming up: Taipei (July 15), Bogotá (July 30), Jakarta (August 6), Ciudad de México (August 12), Johannesburg (August 19), and Zurich (September 2).
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include Yaoundé, Cameroon (July 25), Ahmedabad, India (July 25), Belo Horizonte, Brazil (August 22), Ottawa, Canada (August 22), Tulsa, USA (August 22), and Toronto, Canada (August 29).

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

Wishing everyone a restful and enjoyable summer. Whether you’re building, learning, or recharging, I hope you find time for all three. I’ll be heading to Scandinavia for a few weeks to trade the heat for some cooler weather and longer evenings. Come back next week for more news!

— Esra

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SP-API Updates: Invoice by Amazon Expansion for VCS Lite Sellers and New Cancellation XML for Brazil Store

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