Amazon CloudWatch now offers CloudWatch Omni, an AI-powered observability experience for the applications and AI agents you run together. You reach Omni through a dedicated URL for your organization and sign in with the identities you already manage, so working in Omni does not require access to the AWS Management Console. Omni is built on OpenTelemetry: the telemetry you already send to CloudWatch appears in Omni with nothing to reconfigure, and any other workload you instrument with OpenTelemetry sends its telemetry to an OpenTelemetry Protocol (OTLP) endpoint.

CloudWatch Omni offers both agent observability and application observability in a single experience. In our companion post, we introduced the agent observability capabilities of Omni for generative AI and agentic workloads. In this post, we present the application observability experience.

Engineering teams spend a significant portion of their observability time maintaining dashboards, tuning thresholds, and switching between tools to piece together what happened during an incident. When an issue crosses team boundaries, context gets lost in Slack threads and screenshots rather than flowing naturally to the next engineer. CloudWatch Omni changes this by organizing observability around your applications rather than individual signals, and bringing your whole team into the same workspace.

What CloudWatch Omni brings
CloudWatch Omni addresses three problems that engineering teams told us they face today.

One collaborative experience for your whole team. Every engineer accesses CloudWatch Omni through a single URL with enterprise SSO (via IAM Identity Center, supporting Okta, EntraID, and other providers). No AWS Console access is required. SREs, developers, database engineers, and managers share the same data and investigation context. When an investigation escalates, the next person joins the same session with full context already in front of them.

The system adapts as your applications evolve. CloudWatch Omni discovers your services, maps dependencies, and adjusts alarms automatically. Instead of manually curating dashboards and tuning thresholds, you declare what matters (availability targets, latency budgets, error rate thresholds) and Omni adapts as your system changes. When you deploy new services, Omni updates the application topology automatically.

AI-powered investigation with Amazon DevOps Agent. Amazon DevOps Agent participates alongside your team in investigation sessions, correlating signals and suggesting next steps. The agent works from the same telemetry your engineers see, so its suggestions are grounded in the actual state of your application. It identifies correlated events across services, traces root cause paths through your dependency graph, and maintains investigation history for post-incident review.

How an investigation works
When something breaks, CloudWatch Omni opens an investigation session pre-loaded with context. Here is a typical incident workflow:

An alarm fires on elevated error rates in your checkout service. Omni opens a session showing the service topology, correlated signals (a deployment 10 minutes earlier, increased latency from a downstream payment API), and DevOps Agent’s initial analysis.

Your on-call SRE confirms the deployment correlation, pulls in the trace view to identify failing endpoints, and checks if the payment API latency correlates with a capacity limit.

The SRE escalates to the payments team. The payments engineer joins the same session and sees everything found so far, plus DevOps Agent’s correlation with a configuration change in the payment provider’s API gateway. They identify the root cause and roll back.

The entire investigation history is captured automatically. No separate incident report needed.

Walkthrough: setting up your first Space
To set up CloudWatch Omni for your team, open the CloudWatch console and click “Try CloudWatch Omni.”

Next, connect your identity provider through IAM Identity Center (supporting Okta, Azure AD, and other SAML 2.0 providers). Once connected, your team members access Omni directly at your dedicated URL without needing AWS Console credentials.

Create a Space for your team. A Space groups the applications your team owns and the telemetry associated with them.

Once created, Omni discovers your services automatically and maps the dependencies between them. You see your application topology immediately.

You can ask CloudWatch Omni any question about your applications in plain English, and Omni will analyze your telemetry data and surface insights.

You can also set up service health alerts, configure what matters to your team, and trigger an AWS DevOps agent investigation to identify the root cause and develop a mitigation plan.

Application-centric organization
CloudWatch Omni organizes telemetry by application rather than by infrastructure component. The system automatically discovers services from the telemetry data and AWS Config resource discovery, maps dependencies, and lets you see your application as a connected system rather than a collection of isolated resources.

Each team gets a Space that contains the applications they own. A Space points at existing CloudWatch data (logs, metrics, traces, and alarms) with no additional data movement required. Dynamic views replace the maintenance burden of static dashboards, providing ongoing visibility into SLOs and application health.

Getting started
Getting started takes minutes and doesn’t require reconfiguration of your existing CloudWatch setup.

If you’re an existing CloudWatch customer: Click “Try CloudWatch Omni” in the CloudWatch console. All your existing telemetry (logs, metrics, traces, and alarms) is immediately available. Workloads are discovered automatically, and you can start an investigation or browse your application topology right away.

For organization-wide deployment: An administrator configures a domain, connects your identity provider via IAM Identity Center, defines Spaces for teams and environments, and invites users. Each Space points at existing CloudWatch data with no additional data movement required.

For applications in other environments: CloudWatch Omni provides connectors that make it easy to bring in telemetry from additional environments. All ingested telemetry appears alongside your AWS data in the same Spaces and investigation sessions.

For generative AI and agentic workloads: The same CloudWatch Omni experience delivers purpose-built observability for AI agents, including trace exploration, evaluation frameworks, and real-time monitoring. In our companion post, we introduced the agent observability capabilities of Omni; for that walkthrough, see Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads.

Things to know

• CloudWatch Omni extends CloudWatch. Existing alarms, dashboards, APIs, and console workflows continue unchanged.
• Access is through a dedicated web application with enterprise SSO. Engineers don’t need AWS Console access to use it.
• Once you setup, DevOps Agent is enabled by default in every Omni investigation session.

Pricing and availability
Amazon CloudWatch Omni is now available. Existing CloudWatch customers can try it directly from the CloudWatch console. For pricing details, visit the Amazon CloudWatch pricing page.

To get started, visit Amazon CloudWatch Omni or click “Try CloudWatch Omni” in the Amazon CloudWatch console.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

9/24/2026 – Editor’s note: Azure AD has been renamed to Microsoft Entra ID.

— Daniel Abib

Today, Amazon CloudWatch introduces CloudWatch Omni, a unified observability experience for application and AI workloads that is app-centric, AI-powered, built on open standards, and delivered off-console. CloudWatch Omni is a purpose-built observability, evaluation, and experimentation solution for AI agents. It helps teams design, evaluate, and operate AI agents across any model provider, framework, or runtime, with an eval-driven workflow, support for the tools you already use, and observability delivered where you work: directly in your IDE and through a standalone web experience, separate from the AWS Management Console.

Organizations deploying agentic AI systems face observability challenges that traditional monitoring can’t address. Agent behavior is non-deterministic: a prompt change can degrade response quality even when standard metrics show no errors. Teams spend hours manually reviewing logs across multiple systems, unable to pinpoint what changed or why. Existing tools force teams to choose between siloed generative AI monitoring or fragmented solutions requiring constant context-switching between their coding environment and browser-based dashboards.

CloudWatch Omni captures every trace and includes built-in evaluators for correctness, coherence, retrieval quality, and tool selection, among others. You can compare prompt versions side by side in the playground, build test datasets from production traffic, run experiments across different configurations, and detect regressions automatically.

Two surfaces for development and operations
CloudWatch Omni delivers observability through two complementary surfaces. Developers get a native extension inside VS Code and Kiro (the currently supported IDEs), where traces appear as you run your agent with a playground and evaluators a click away. Operators get a standalone web experience, separate from the AWS Management Console to monitor the fleet, accessible through SSO with no AWS console needed. Both share the same data: the trace a developer debugs is the trace an operator investigates.

The Cloud Login feature connects your local IDE environment to your AWS account, enabling you to send telemetry data to Amazon CloudWatch for persistent storage, share traces with your team, and access production dashboards. This connection is optional. You can use CloudWatch Omni entirely locally during development, then connect to the cloud when you are ready to monitor agents in production.

Getting started
CloudWatch Omni offers two ways to get started: through the IDE extension (for VS Code and Kiro) or directly through the cloud experience, where you can start sending telemetry data to CloudWatch without installing any IDE extension. In this walkthrough, I install the extension, create an agent, run it, and explore the traces and evaluation tools from my IDE.

After installing the CloudWatch Omni extension from the VS Code Marketplace, the CloudWatch Omni icon appears in the Activity Bar. From the welcome screen, I selected Get started with Sample Project to load a pre-configured agent with sample trace data or use shortcut to Command Palette using Command + Shift + P (on macOS) or Ctrl + Shift + P (on Windows/Linux) and select Omni: Create a new Project

The sample project comes with an agent implementation and example datasets. Part of the getting-started experience is adding OpenTelemetry instrumentation, and CloudWatch Omni guides you through each step. You can also create a new agent from scratch. CloudWatch Omni walks you through the process using an interactive chat where you define the agent’s purpose, select a model provider, and configure tools. All data is stored locally by default. You can optionally connect to AWS to send data to Amazon CloudWatch.

After verifying the configuration, I started the local dev server and sent a question to the agent. What makes this different from a typical chatbot interface is what happens next: selecting View Trace shows exactly how the agent processed the request.

CloudWatch Omni integrates with AI code assistants such as Kiro, Claude Code, and Codex to streamline the setup process. These assistants can configure the Dev Server, install dependencies, and set up instrumentation on your behalf, so you can go from installation to running your first traced agent session in minutes without manual configuration.

Traces are essential for understanding AI agent behavior. Unlike traditional request-response systems, agents make multiple decisions per invocation: choosing tools, composing prompts, and chaining sub-calls. Without full trace visibility, diagnosing why an agent produced an incorrect answer or took an unexpected path becomes guesswork. CloudWatch Omni records every step in a structured timeline so you can pinpoint exactly where behavior diverged.

The Trace Explorer shows a detailed breakdown of every step the agent took (LLM calls, tool invocations, and reasoning steps) in a structured, hierarchical timeline. I could drill into any span to inspect inputs, outputs, token usage, and latency.

The Trace Explorer also supports Compare mode, which places two traces side by side to see how different prompts or configurations affect behavior. Compare mode is especially helpful when debugging regressions. And with Ask Assistant, an AI agent analyzes your traces to surface patterns and anomalies, answering questions like “Why did the agent call this tool twice?”

Evaluation is what turns observability into actionable quality improvement for generative AI. Traditional metrics like latency and error rate cannot tell you whether an agent’s response was helpful, coherent, or factually correct. Evaluators score each response against quality dimensions, letting you measure what users actually experience and catch regressions that standard monitoring misses entirely.

CloudWatch Omni includes 17 built-in evaluators for metrics like coherence, helpfulness, faithfulness, and routing correctness. I selected traces from the Trace Explorer, chose evaluators, and ran an evaluation, getting per-example scores and aggregate metrics without building any custom evaluation framework.

From there, I used the Playground to test different system prompts side by side, comparing multiple model and prompt configurations in real time to see how each variation affects output quality before committing changes. With the Experiments view, I could run the same dataset against two agent variants and compare their evaluation scores, latency, and token usage side by side to pick the best-performing configuration.

Figure 7. Comparing evaluations across agent variants in the Omni Experiments console

With Prompt Management, you can version and track prompt configurations over time, making it easy to roll back when a new version underperforms.

CloudWatch Omni also provides a Session Explorer to review full conversation histories and understand how agents handle multi-turn interactions, along with an Agent Topology view that visualizes the architecture of your agent system, including sub-agents, tools, and their interconnections. You can drill into any node to inspect performance and identify bottlenecks.

CloudWatch Omni also offers a dedicated web experience accessible from any browser without an IDE. Teams can access all capabilities collaboratively, including application monitoring, analytics, agent observability, and AI-powered investigations.

I curated traces into golden datasets for structured experimentation. The Experiment function runs the agent against a dataset and automatically scores results, creating benchmarks for regression testing whenever prompts or agent logic change.

If you already have an agent built with a supported framework, CloudWatch Omni provides two paths to add instrumentation: Auto-instrument with Kiro, which detects your framework and configures tracing automatically, or manual instrumentation with ready-to-use code snippets for Python and TypeScript. For detailed instrumentation guides, see the CloudWatch Omni documentation.

Supported frameworks and open standards
The walkthrough above uses the sample project, but CloudWatch Omni works with the agent frameworks teams are already using: LangChain, LangGraph, CrewAI, OpenAI SDK, Strands, Vercel AI SDK, and more, in both Python and TypeScript. It also provides native observability for agents built with Amazon Bedrock AgentCore, and uses AgentCore’s evaluation capabilities to assess agent quality directly within the Omni workflow.

Instrumentation uses open standards (OpenInference and ADOT), whether your agents run on Lambda, ECS, EKS, or other clouds. For evaluation, Omni integrates with third-party evaluators including AutoEval and DeepEval alongside built-in datasets, a playground, and batch experiments. No re-platforming required.

CloudWatch Omni brings agent observability and application observability together in a single experience. For the application observability experience, read the companion post Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications.

Pricing and availability
Amazon CloudWatch Omni is now generally available. The IDE extension is free to use. You don’t need an AWS account to get started. You only need AWS credentials for Amazon Bedrock models, or API keys for other providers like OpenAI or Anthropic. Get started today by installing the extension from the VS Code Marketplace.

To explore all capabilities and get started quickly, visit CloudWatch on AWS Builder Center.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

Happy building!

— Daniel Abib

Today, Amazon EC2 R9g and R9gd instances are generally available, powered by AWS Graviton5 processors. R9g instances are memory-optimized and deliver up to 25% better compute performance compared to Graviton4-based R8g instances, powered by the most energy efficient processor AWS has ever built.

R9g instances are ideal for memory-intensive workloads including databases, in-memory caches (Valkey, Redis, MemCached), real-time big data analytics, Linux-based workloads including containerized and micro-service-based applications (e.g. Kubernetes, Docker, EKS, ECS), as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.

R9gd instances include local NVMe-based SSD block-level storage, ideal for memory-intensive workloads requiring fast, low-latency local storage such as open-source databases, distributed real-time big data analytics, large in-memory databases, and large caching workloads.

If you’re running workloads on R8g instances today, R9g gives you more performance per vCPU with faster memory, higher network and Amazon EBS bandwidth, and a larger L3 cache, all while using less energy.

What makes R9g different
Graviton5 processors bring several hardware improvements over Graviton4:

• Up to 25% higher compute performance per vCPU
• DDR5 8800 MT/s memory (up from 5600 MT/s in Graviton4), the fastest memory available in the cloud
• 5x larger L3 cache for better data locality
• Up to 2x higher network and EBS bandwidth for the largest instance sizes (up to 100 Gbps network, up to 72 Gbps EBS on the 48xlarge)
• Up to 3x higher packet-processing performance

R9g and R9gd instances support Instance Bandwidth Configuration (IBC), which lets you adjust the allocation of bandwidth between Amazon EBS and Amazon VPC networking by 25%. This helps optimize performance for workloads with specific bandwidth requirements such as databases and caching.

All R9g and R9gd instances run on the AWS Nitro System, which offloads virtualization, storage, and networking to dedicated hardware. This gives your applications near-bare-metal performance while maintaining strong security isolation between instances.

R9g and R9gd instances feature the Nitro Isolation Engine (NIE), the same enhancement to the Nitro System introduced with C9g and M9g instances earlier this year, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. Nitro Isolation Engine is a purpose-built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs. Nitro Isolation Engine leverages formal verification, a technique to mathematically demonstrate that the hardware or software behaves as intended, and not just in specific test cases. This intensive verification technique establishes Nitro as the first formally verified cloud hypervisor, pioneering a new standard for mathematically proven cloud security. To learn more about the Nitro Isolation Engine, visit the blog post. For details on the formal verification results, including scope and assumptions, see the technical white paper.

EC2 R9g and R9gd instance specifications
R9g and R9gd instances are each available in 11 sizes, from medium to metal-48xl. The following tables show the full specifications for each size.

Instance size	vCPUs	Memory (GiB)	Instance Storage	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
r9g.medium	1	8	EBS-Only	Up to 15	Up to 12
r9g.large	2	16	EBS-Only	Up to 15	Up to 12
r9g.xlarge	4	32	EBS-Only	Up to 15	Up to 12
r9g.2xlarge	8	64	EBS-Only	Up to 17	Up to 12
r9g.4xlarge	16	128	EBS-Only	Up to 17	Up to 12
r9g.8xlarge	32	256	EBS-Only	17	12
r9g.12xlarge	48	384	EBS-Only	25	18
r9g.16xlarge	64	512	EBS-Only	34	24
r9g.24xlarge	96	768	EBS-Only	50	36
r9g.48xlarge	192	1536	EBS-Only	100	72
r9g.metal‑48xl	192	1536	EBS-Only	100	72

R9gd instances offer the same compute and networking performance as R9g, with the addition of local NVMe-based SSD storage for workloads that need fast, low-latency scratch space or temporary caches.

Instance size	vCPUs	Memory (GiB)	Instance Storage (NVMe SSD)	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
r9gd.medium	1	8	1 x 59 GB	Up to 15	Up to 12
r9gd.large	2	16	1 x 118 GB	Up to 15	Up to 12
r9gd.xlarge	4	32	1 x 237 GB	Up to 15	Up to 12
r9gd.2xlarge	8	64	1 x 474 GB	Up to 17	Up to 12
r9gd.4xlarge	16	128	1 x 950 GB	Up to 17	Up to 12
r9gd.8xlarge	32	256	1 x 1900 GB	17	12
r9gd.12xlarge	48	384	3 x 950 GB	25	18
r9gd.16xlarge	64	512	1 x 3800 GB	34	24
r9gd.24xlarge	96	768	3 x 1900 GB	50	36
r9gd.48xlarge	192	1536	3 x 3800 GB	100	72
r9gd.metal‑48xl	192	1536	3 x 3800 GB	100	72

Getting started
You can launch R9g and R9gd instances from the Amazon EC2 console using any supported Arm-based AMI. R9g instances support Amazon Linux 2023, Amazon Linux 2, Ubuntu 22.04+, RHEL 8.4+, SUSE Linux Enterprise Server 15 SP3+, Debian 12+, and other major Linux distributions.

If you’re migrating from R8g, no code changes are required for most applications. Select the equivalent R9g instance size and your application runs with better performance. For containerized workloads, R9g works with Amazon EKS, Amazon ECS, and standard Kubernetes deployments. Multi-arch container images built for Arm64 run without changes.

Several resources help you get started: the AWS Graviton Getting Started Guide covers how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track cost savings. AWS Transform automates code transformations for migrating Java applications from x86 to Graviton. To learn more, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

Pricing and availability
Amazon EC2 R9g and R9gd instances are available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions.

R9g and R9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For detailed pricing, visit the Amazon EC2 pricing page.

Ready to get started? Launch R9g instances from the Amazon EC2 console. For more details, visit the Amazon EC2 R9g instances page.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post for Amazon EC2 or reach out through your usual AWS Support contacts.

— Daniel Abib

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation and the MIT license, and over time AWS plans to combine its speed at everyday queries with the enterprise scale of services like Amazon S3, Amazon Redshift, and Amazon Athena.

Co-founded by Hannes Mühleisen and Mark Raasveldt, DuckDB runs locally or on Amazon S3, which makes it remarkably fast for the everyday queries (a terabyte or less) that make up the bulk of real-world analytics. It also happens to pair beautifully with AI agents, which “poke” and experiment their way through data much like humans do. The co-founders will continue leading its technical direction while AWS combines DuckDB’s speed with analytics services like Amazon EMR, AWS Glue, and Amazon SageMaker. For the bigger picture on why this matters, Andy Warfield, Vice President and Distinguished Engineer shared his thoughts on the post DuckDB and the changing physics of analytics on All Things Distributed.

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

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

• Amazon ECS now automatically detects and recovers container instances that lose agent connectivity – Amazon ECS now continuously monitors agent connectivity to the control plane and surfaces a new AGENT_CONNECTIVITY health event across AWS Fargate, Amazon ECS Managed Instances, and Amazon ECS on EC2. On Fargate and Managed Instances, ECS handles recovery automatically, draining tasks, launching replacements, and deregistering the impaired instance. On EC2, you can wire the event into your own workflow. Available at no additional cost in all AWS Commercial and AWS GovCloud (US) Regions.
• AWS Lambda introduces public preview runtimes, starting with Node.js 26 and Python 3.15 – You can now test upcoming Lambda runtimes before they reach general availability. Preview runtimes use the same identifier as the eventual GA version, so your functions graduate automatically with no action required. Third-party tools and deployment frameworks can also validate compatibility ahead of GA. Not meant for production yet (breaking changes are possible), but a great way to get ahead of your next upgrade. Available in all AWS commercial, AWS GovCloud (US), and China Regions.
• AWS IoT Core adds a native InfluxDB rule action – You can now route time-series data from your IoT devices straight into InfluxDB (Amazon Timestream-managed or self-hosted) without writing custom code or standing up an intermediate service. IoT Core formats data into InfluxDB’s line protocol and supports device-side and server-side batching. Available in all AWS Regions where Amazon Timestream for InfluxDB is offered.
• Amazon GameLift Servers now includes enhanced DDoS protection – Your game servers now get automatic protection against network and transport layer (layers 3 and 4) DDoS attacks – UDP reflection, SYN floods, and similar vectors – with nothing to enable or opt into. Built on top of AWS Shield Standard with gaming-optimized traffic shaping, it turns on the moment your servers start running (Server SDK 5) at no extra cost. It’s available in all supported GameLift Servers Regions except China (Beijing) and China (Ningxia).
• Amazon SageMaker HyperPod expands support for Ray – You can now run Ray workloads on SageMaker HyperPod with built-in observability, resilient training, and accelerated inference. Create and manage Ray clusters from Amazon SageMaker Studio, attach JupyterLab or your local IDE so a multi-node cluster behaves like a local dev environment, and get auto-provisioned Grafana dashboards. Node auto recovery, hung job detection, and tiered checkpointing keep large training runs healthy, while Ray Serve adds a tiered KV cache for inference. Your existing open source Ray code runs unchanged. Available for HyperPod clusters orchestrated by Amazon EKS.

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:

• Happy 20th birthday, Amazon EC2! – Amazon EC2 turns 20. Channy Yun looks back at how EC2 grew from a single m1.small instance type in one Region to more than 1,200 instance types across 39 Regions, along with the custom silicon journey from the first Graviton to Graviton5 and Trainium3. A fun and worthwhile read on the service that still underpins so much of AWS – including Amazon ECS, Amazon EKS, AWS Lambda, Amazon SageMaker, and Amazon Bedrock.
• Agentic Resource Discovery (ARD): an open specification for agent discovery – As organizations scale up agents, tools, and MCP servers, those resources end up scattered across clouds, on-premises infrastructure, and SaaS platforms – each with its own registry and metadata. ARD is a new open specification (Apache 2.0) that defines a common way to describe and discover agentic resources, so publishers “describe once” and consumers “discover everywhere” – think DNS, but for agents. AWS contributed feedback but doesn’t own the spec, and it complements the AWS Agent Registry by letting you federate across catalogs without migrating.
• Get started with the Agent Toolkit for AWS in the AWS CLI – A single AWS CLI command (aws configure agent-toolkit) now equips AI coding agents like Kiro, Claude Code, Codex, and Cursor with curated, up-to-date AWS knowledge and a secure connection to thousands of AWS APIs through the AWS MCP Server. If you build with an AI coding assistant, this helps it choose the right services, use modern APIs, and follow security best practices – so it gets AWS code right more often the first time.

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

• AWS Summits – Free in-person events where builders come together to learn, connect, and explore the latest in cloud and AI. Upcoming stops include Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). Can’t attend in person? You can stream sessions through the Global Livestream and On-Demand Hub. I’ll be presenting two sessions on generative AI and Amazon Bedrock at the São Paulo Summit – if you’re there, come say hello.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. Upcoming events include JAWS SONIC 2026 in Tokyo (September 5) and Warsaw, Poland (September 8).

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 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!

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!

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, delivering high performance GPU acceleration for AI inference, graphics, and data analytics workloads.

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. G7 instances also deliver faster performance for GPU-accelerated analytics on Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). G7 instances are well suited for a broad range of GPU-enabled workloads including AI inference, graphics rendering, video transcoding and analytics, spatial computing, virtual desktop infrastructure (VDI), and data analytics.

Here are improvements of G7 instances compared to previous generation:

• Faster GPU memory: NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs offer 1.33 times the GPU memory capacity and 2.45 times the GPU memory bandwidth compared to G6 instances. With 32 GB of GPU memory per GPU, 5th Gen Tensor Cores, and 4th Gen RT Cores, G7 instances deliver enhanced AI inference and graphics performance.
• High performance networking and storage: G7 instances come with 700 Gbps of EFA-enabled networking throughput (7x compared to G6) enabling the low-latency, high-bandwidth connectivity that AI inference, graphics-intensive applications, and GPU-accelerated data analytics workloads need to perform at their best. G7 instances support up to 7.6 TB local NVMe SSD storage, enabling you to keep large models and datasets close to compute, reduce data transfer overhead, and improve throughput.
• Advanced video encoding and decoding engines: Ninth-generation NVENC and sixth-generation NVDEC engines support 4:2:2 encoding and decoding for high-resolution video workflows, delivering 1.5x concurrent video streams compared to previous-generation G6 instances.

EC2 G7 instance specifications
G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with up to 256 GB of total GPU memory (32 GB of memory per GPU) and custom Intel Xeon Scalable processors. They also are available in 7 sizes and support up to 192 vCPUs, up to 700 Gbps of network bandwidth, up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.

Here are the specs:

Instance name	GPUs	GPU memory (GB)	vCPUs	Memory (GiB)	Storage	EBS bandwidth (Gbps)	Network bandwidth (Gbps)
g7.2xlarge	1	32	8	32	1 x 600	Up to 8	Up to 60
g7.4xlarge	1	32	16	64	1 x 600	8	Up to 100
g7.8xlarge	1	32	32	128	1 x 950	16	Up to 100
g7.12xlarge	2	64	48	192	1 x 1900	20	175
g7.24xlarge	4	128	96	384	1 x 3800	40	350
g7.48xlarge	8	256	192	768	2 x 3800	80	700
g7.metal*	8	256	192	768	2 x 3800	80	700

* Coming soon

G7 instances support NVIDIA GPUDirect P2P for multi-GPU sizes, NVIDIA GPUDirect RDMA with EFA, and GPUDirect RDMA with EFA for Amazon FSx for Lustre, enabling low-latency GPU-to-GPU communication for multi-GPU and multi-node workloads.

To get started with G7 instances, you can use the AWS Deep Learning AMIs (DLAMI) or NVIDIA Workstation AMIs with prepackaged GPU drivers for your AI inference and graphics workloads. To use G7 instances with Amazon EKS, build EKS AMIs with NVIDIA driver version R595 with EKS-provided automation. G7 instances support multiple operating systems including Amazon Linux, Ubuntu, RHEL, and Windows Server, with comprehensive NVIDIA driver integration providing compatibility with industry-standard graphics libraries including DirectX, Vulkan, and OpenGL.

Get started today
You can start using Amazon EC2 G7 instances today in two AWS regions: US East (Ohio) and US West (Oregon). To check future Regional expansion plans, look up the instance type in the CloudFormation resources tab on the AWS Capabilities by Region page.

G7 instances are offered through multiple purchasing options, including On-Demand, Savings Plans, and Spot Instances. Dedicated Instances are also supported for the 12xlarge, 24xlarge, and 48xlarge sizes. For detailed pricing, visit the Amazon EC2 Pricing page.

Ready to get started? Launch G7 instances from the Amazon EC2 console. For more details, head over to the Amazon EC2 G7 instances page. We’d love to hear your feedback. Share it on AWS re:Post for EC2 or reach out through your usual AWS Support contacts.

– Daniel Abib

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.

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:

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.

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:

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:

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 a new metadata capability for Amazon Simple Storage Service (Amazon S3) called annotations, enabling you to attach rich, large-scale business context directly to your objects. You can store up to 1,000 named annotations per object, each up to 1 MB in size, totaling up to 1 GB per object, in flexible formats like JSON, XML, YAML, or plain text. You can modify or delete an annotation at any time, without re-writing your objects, making it easy to keep your object context current.

Organizations are building AI agents and autonomous workflows that need to find, understand, and act on data without human intervention. To support these agentic workflows, you need metadata that can evolve alongside the data, scale to petabytes of objects, and remain queryable without expensive retrieval.

With S3 annotations, you can store context such as AI-generated transcripts, content ratings, or technical specifications directly alongside your objects. Your context moves automatically with the object during copy, replication, and cross-region transfers, and S3 removes it when you delete the object. When you enable S3 Metadata, annotations automatically flow into fully managed annotation tables that you can query with Amazon Athena and other analytics engines.

Common use cases
Annotations solve complex metadata challenges across industries:

• Media & Entertainment: Track transcripts, content moderation results, subtitle files, and licensing metadata as separate annotations on video assets, eliminating the need to synchronize metadata across multiple media asset management systems.
• Financial Services: Attach AI-generated investment summaries and sentiment analysis to research documents, enabling autonomous research agents to discover relevant datasets through natural-language queries without maintaining separate metadata databases.
• Life Sciences: Annotate clinical trial data with regulatory status, patient cohort details, and approval chains, making compliance audits faster while keeping full context accessible for archived data in Amazon S3 Glacier storage classes without retrieval charges.

How annotations address metadata challenges
Amazon S3 already supports several ways to describe your objects. System-defined metadata captures properties like size and storage class. Object tags support operational tasks like access control and lifecycle management. User-defined metadata lets you add small amounts of custom information at upload time.

While these capabilities work well for their intended purposes, they have limitations when you need to attach much richer context without building and maintaining separate metadata systems. Annotations address these needs by providing metadata capabilities at a fundamentally different scale and flexibility, offering mutable, queryable context per object compared to 10 immutable tags or 2 KB of headers.

Capability	Max size	Mutable?	Best for
System-defined metadata	Fixed	No	Object properties (size, storage class, creation time)
User-defined metadata	2 KB	No (set at upload)	Small custom key-value pairs
Object tags	10 tags, 128/256 characters per key/value	Yes	Access control, lifecycle rules, cost allocation
Annotations	1 GB (1,000 × 1 MB)	Yes	Rich business context (JSON, XML, YAML, plain text)

Today, metadata describing S3 objects often lives in separate databases or sidecar files, requiring complex synchronization workflows that can exceed data storage costs. When you enable S3 Metadata annotation tables, this context becomes queryable at scale through Amazon Athena. AI agents can discover your data through natural language with the S3 Tables MCP server, which provides a standardized interface for AI models to query your annotations. You can query annotations for objects in any storage class, without restoring the objects or paying retrieval charges.

Getting started with annotations
To start using annotations, make sure your AWS Identity and Access Management (IAM) policy or bucket policy grants permissions for the s3:PutObjectAnnotation and s3:GetObjectAnnotation actions. You can then add annotations to any existing or new S3 object using the PutObjectAnnotation API.

For example, a media company can attach technical specifications and AI-produced summaries to a video asset using the AWS Command Line Interface (AWS CLI):

# Create a JSON file with technical metadata
cat > mediainfo.json << 'EOF'
{"codec":"H.265","resolution":"3840x2160","audio_tracks":8,"frame_rate":29.97}
EOF

# Attach it as an annotation
aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  --annotation-payload ./mediainfo.json

# Attach a plain-text AI-generated summary as a separate annotation
echo "A 90-minute nature documentary covering wildlife migration patterns across three continents, featuring aerial footage and underwater sequences. Languages: English, Spanish, Portuguese." > ai_summary.txt

aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name ai_summary \
  --annotation-payload ./ai_summary.txt

These commands attach two separate annotations to the same video object. The mediainfo annotation stores structured technical specifications as JSON, while the ai_summary annotation stores a text description. Each annotation is identified by a unique name, and you can read and modify each one independently. With unique names for each annotation, you can use different annotations to support multiple concurrent enrichment workflows, for example, one team adding technical metadata while another team adds content classifications, without interfering with each other.

Retrieve a specific annotation using the GetObjectAnnotation API:

aws s3api get-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  ./mediainfo-output.json

To see all annotations attached to an object, use the ListObjectAnnotations API:

aws s3api list-object-annotations \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4

When you no longer need a specific annotation, remove it using the DeleteObjectAnnotation API:

aws s3api delete-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo

You can update an existing annotation at any time by calling PutObjectAnnotation again with the same annotation name. For large objects uploaded using multipart upload, attach annotations after completing the multipart upload using the PutObjectAnnotation API.

Querying annotations at scale with S3 Metadata tables
Attaching annotations to individual objects is useful, but the real power comes when you query across all your annotations at scale. When you enable S3 Metadata annotation tables on your bucket, S3 automatically indexes your annotations into a fully managed Apache Iceberg table, called an annotation table. You can query annotation tables with Amazon Athena or any Iceberg-compatible engine.

To enable annotation tables, use the S3 console or the CreateBucketMetadataConfiguration API. The following example creates a new metadata configuration with annotation tables enabled while keeping journal tables for change tracking and disabling the live inventory table:

{
  "JournalTableConfiguration": {
    "RecordExpiration": { "Expiration": "DISABLED" }
  },
  "InventoryTableConfiguration": { "ConfigurationState": "DISABLED" },
  "AnnotationTableConfiguration": {
    "ConfigurationState": "ENABLED",
    "Role": "arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"
  }
}

This configuration tells S3 to automatically capture all your annotations in a queryable table. Once applied, any annotation you attach to objects in this bucket will appear in the table within approximately one hour.

If the bucket already has a metadata configuration, use the UpdateBucketMetadataAnnotationTableConfiguration API:

aws s3api update-bucket-metadata-annotation-table-configuration \
  --bucket my-media-bucket \
  --annotation-table-configuration '{"ConfigurationState":"ENABLED","Role":"arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"}'

Once enabled, your annotations automatically flow into the annotation table. Journal tables update in near real time, while annotation tables refresh within an hour. Unlike traditional metadata tables that require predefined schemas, annotation tables automatically adapt to any JSON, XML, or YAML structure you write. Each annotation becomes a row in the table with its content stored in a text_value column, letting you query across all annotations without schema migrations.

If you enable annotation tables on a bucket that already has annotated objects, S3 automatically backfills existing annotations into the table. The backfill process runs in the background and can take several hours to days depending on the number of objects.

For example, to find all video assets with more than 8 audio tracks across your entire bucket using Amazon Athena:

SELECT DISTINCT bucket, object_key
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."annotation"
WHERE name = 'mediainfo'
AND CAST(json_extract_scalar(text_value, '$.audio_tracks') AS INTEGER) > 8

This query scans the annotation table for all annotations named mediainfo, extracts the audio_tracks field from the JSON content, and returns objects where the count exceeds 8.

Or to find all objects that received new annotations in the last 24 hours through the journal table:

SELECT bucket, key, version_id, record_timestamp, annotation.name
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."journal"
WHERE record_timestamp >= (current_date - interval '1' day)
AND annotation.name IS NOT NULL
AND record_type IN ('CREATE_ANNOTATION', 'DELETE_ANNOTATION')

This query uses the journal table to track annotation changes in near real time, which is ideal for building event-driven workflows that respond to new or deleted annotations.

You can also use natural language to search objects by their annotations using agents in Amazon SageMaker Unified Studio or any IDE with the S3 Tables MCP server. For example, asking “find all PG-rated movies with Spanish subtitles from 2023” returns results in seconds instead of the hours it would take querying multiple disconnected systems.

Get started today
You can start using Amazon S3 annotations today in all AWS Regions, including the AWS China Regions. Annotation tables are available in all AWS Regions where S3 Metadata is available.

Whether you’re building AI agents that need to discover data autonomously, managing petabytes of media assets with complex metadata, or tracking compliance context for archived datasets, annotations give you the scale and flexibility to attach rich metadata directly to your objects without managing separate systems.

Annotation storage is always billed at S3 Standard rates, even if the parent object is in S3 Glacier or another storage class. For full pricing details, visit the Amazon S3 pricing page.

To learn more and get started, visit the Amazon S3 Metadata overview page and the Amazon S3 documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

Daniel Abib