2026 年 6 月 17 日にニューヨークで開催された AWS Summit を軸に、生成 AI 関連の発表が集中した一日でした。Amazon Bedrock AgentCore では知識拡充と継続学習の新機能、Web Search、フルマネージドナレッジベースが登場し、エンタープライズ RAG やエージェント構築の強化が図られました。Amazon Quick への自律エージェント追加、コードの脆弱性にマシンスピードで対応する AWS Continuum、スマホから使える Kiro for iOS、AWS DevOps Agent のリリース管理機能（プレビュー）なども発表されました。基盤サービスでは Graviton5 ベースの RDS M9g インスタンス、RDS for MySQL 5.7 の Extended Support を 2029 年 6 月まで延長、Glue Interactive Sessions の Spark Connect 対応、SageMaker Async Inference のインラインペイロード対応が公開。Bedrock AgentCore Python SDK の脆弱性 (CVE-2026-12530) も告知されました。
AWS Summit New York 2026: Bedrock AgentCore・マネージドナレッジベース・Web Search など生成 AI 発表
AI エージェント基盤: Amazon Quick 自律エージェント、Kiro for iOS、DevOps Agent リリース管理
セキュリティ: AWS Continuum とコード脆弱性対応、AgentCore Python SDK の CVE-2026-12530
データベース/分析: RDS M9g (Graviton5)、MySQL 5.7 Extended Support 延長、Glue Spark Connect
日本語ワークショップ/事例: OpenSearch 検索・Observability、日立グループ AI-DLC
AWS DevOps Agent now offers a release management capability in preview, reviewing code changes for release readiness and running autonomous release testing to help you ship code to production safely and with confidence. With this addition, AWS DevOps Agent now works across both delivery and operations. It accelerates and validates the deployment of code changes, then keeps your applications running optimally across AWS, multicloud, and on-prem environments, so your team ships faster, reduces MTTR, and achieves operational excellence.
With release readiness review, AWS DevOps Agent evaluates code changes for production safety during code generation by checking for drift from your internal standards, dependency impacts, and access controls. It maps cross-repository dependencies to surface breaking changes before commit and uses deterministic proofs to review that infrastructure changes do not drift from AWS Well-Architected best practices. With release testing, AWS DevOps Agent generates and runs test plans for web and API-based applications in customer-provisioned environments, catching regressions, UX issues, and integration failures a human reviewer may miss.
To get started with the preview, connect your code repositories and pipelines in your AWS DevOps Agent space. AWS DevOps Agent release management is available in the US East (N. Virginia) Region and at no additional cost during the preview period. For the list of AWS Regions where AWS DevOps Agent production operations is available, see the supported Regions table. For pricing of production operations features, which are generally available, see AWS DevOps Agent pricing.
Amazon Aurora MySQL-Compatible Edition and Amazon Relational Database Service (RDS) for MySQL now offer Amazon RDS Extended Support for MySQL 5.7 through June 30, 2029, from the previous end date of February 28, 2027. This applies to Aurora MySQL version 2 (with MySQL 5.7 compatibility) and RDS for MySQL version 5.7, giving customers additional time to plan and complete their upgrades to a supported major version while continuing to receive critical security patches and bug fixes.
RDS Extended Support delivers security patches for critical and high CVEs, bug fixes for critical operational issues, and access to AWS Support within the standard Aurora and RDS SLAs. There is no price increase with this extension, and customers using RDS Extended Support for MySQL 5.7 will continue to pay Year 3 pricing through June 30, 2029. For pricing details, see Aurora pricing and RDS for MySQL pricing.
We recommend upgrading to MySQL 8.0 or MySQL 8.4 compatible versions to benefit from the latest database features, performance improvements, and security enhancements. You can upgrade using Amazon RDS Blue/Green Deployments, in-place upgrade, or snapshot restore. To learn more, see the Aurora MySQL and RDS for MySQL user guides. This extension is available in all AWS Regions where Aurora MySQL and RDS for MySQL are available.
Amazon Aurora is designed for high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Amazon RDS for MySQL, PostgreSQL, and MariaDB make it simple to set up, operate, and scale open source deployments in the cloud. Visit the getting started pages for Aurora and RDS to begin.
AWS Graviton5-based M9g database (DB) instances are now generally available for Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB. Graviton5-based instances provide up to a 30% performance improvement and up to a 23% price/performance improvement for on-demand pricing over Graviton4-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload.
AWS Graviton5 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. M9g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, M9g DB instances offer up to 192 vCPU, up to 100Gbps enhanced networking bandwidth, and up to 72Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).
These instances are now available in the US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.
AWS HealthOmics now streams workflow engine logs to Amazon CloudWatch in real time, enabling customers to monitor workflow execution progress as it happens. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Real-time engine log streaming accelerates iterative workflow development and debugging by giving researchers, bioinformaticians, and workflow developers immediate access to execution details during a run. The streamed engine logs provide visibility into workflow orchestration events, task scheduling details, import/export activity, and full stack traces on errors — all routed into the engine log stream in real time. Customers can set up CloudWatch alarms on log patterns to detect anomalies early, build dashboards for ongoing monitoring, and integrate with existing observability tooling.
Real-time engine log streaming is now available for Nextflow, WDL, and CWL workflow runs in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Monitoring HealthOmics with CloudWatch Logs documentation.
AWS Glue Interactive Sessions now support Apache Spark Connect, using which you can now develop and run Apache Spark applications from your preferred environment, including managed notebooks in Amazon SageMaker Unified Studio, or your preferred notebook environments and IDEs like Jupyter, Visual Studio Code, while running them on AWS Glue's serverless infrastructure without managing clusters.
With Spark Connect, you submit Spark jobs to AWS Glue Interactive Sessions using a thin client architecture that decouples your client application from the Spark execution environment. This unlocks workflows like ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production, all from the tools you already use. Spark Connect also simplifies upgrades and improves stability by isolating client dependencies from the server-side Spark runtime. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management using the AWS Glue API, CLI, or SDK.
AWS Glue Interactive Sessions with Spark Connect is available in Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), South America (São Paulo), US East (Ohio, N. Virginia), and US West (Oregon).
To get started, connect to Glue Interactive Sessions using Spark Connect from notebooks in Amazon SageMaker Unified Studio, your favorite IDE with a Python interpreter, or the AWS API, SDK, and CLI. To learn more, visit the AWS Glue Interactive Sessions documentation.
Amazon Relational Database Service (Amazon RDS) for SQL Server now supports higher volume-level limits for General Purpose (gp3) storage. With this update, each gp3 volume can scale up to 64 TiB in size (4X the previous 16 TiB limit), up to 80,000 IOPS (5X the previous 16,000 IOPS limit), and up to 2,000 MiB/s throughput (2X the previous 1,000 MiB/s limit).
With these improvements, customers can now run larger Microsoft SQL Server databases on Amazon RDS. Workloads with demanding I/O requirements such as high-throughput OLTP systems and large-scale analytical workloads can take advantage of higher IOPS and throughput on a single volume with simplified storage management, and get better performance for mission-critical SQL Server workloads. Additionally, you can configure additional storage volumes to add up to three gp3 or io2 volumes per DB instance, increasing total capacity up to 256 TiB per instance. There is no change to pricing - customers pay for storage and any additional IOPS and throughput they provision beyond the baseline default.
For more information, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.
AWS announces availability of Ubuntu 24.04 LTS bundles for Amazon WorkSpaces Personal in the AWS China (Ningxia) Region, operated by NWCD. With this bundle, customers in China can launch Ubuntu WorkSpaces and take advantage of updated Linux packages, toolchains, and security improvements only available in Ubuntu 24.04.
The new Ubuntu 24.04 option brings access to the latest software ecosystem, an improved security posture, and an extended support lifecycle. This new bundle also provides a migration path for customers running older Amazon Linux 2 WorkSpaces who want to stay current with upstream packages and receive the longest available maintenance window from Canonical.
You can get started using the managed Ubuntu 24.04 WorkSpaces bundle by selecting it when creating a new Linux WorkSpace. This new bundle is available in the AWS China (Ningxia) Region. For pricing information, visit the Amazon WorkSpaces pricing page.
Amazon GameLift Servers now supports two significant container fleet improvements that enhance flexibility and inter-container communication for game server deployments. These new capabilities address common challenges faced by game developers using containerized architectures, providing greater control over container permissions and enabling seamless discovery of co-located containers on the same instance.
You can now customize Linux capabilities for containers in your container group definitions, giving you finer control beyond Docker's default capability set. This is particularly valuable for game servers requiring specialized capabilities such as NET_RAW for custom networking protocols or SYS_PTRACE for attaching debuggers and profiling tools. Additionally, game servers can now call the new ListContainersNetworkInfo() server SDK action to retrieve comprehensive network information, including container name, ID, local IP address, and container group type for all containers running on the same instance. This enables automatic service discovery and simplified communication between game servers and auxiliary services like metrics collectors, logging agents, or caching systems.
These improvements are available through the Amazon GameLift Servers console, AWS CLI, AWS SDK, and AWS CloudFormation. The ListContainersNetworkInfo() action is supported in server SDK 5.x for Go, C++, and C#, as well as in plugins for Unreal Engine and Unity. Both features are available in all AWS regions where Amazon GameLift Servers is supported, except China. To learn more, visit the Amazon GameLift Servers documentation.
AWS introduces Web Search on Amazon Bedrock AgentCore, a fully managed tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer's secured AWS environment. You can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure.
Amazon Bedrock's new Fully Managed Knowledge Bases simplifies building enterprise RAG pipelines by providing native data connectors Smart Parsing for automatic multi-format data preparation, and an Agentic Retriever for complex multi-step queries—all integrated with AgentCore Gateway so developers can focus on business outcomes rather than infrastructure management.
A recap of the top announcements from AWS's New York Summit 2026
2026 年 05 月に公開された AWS Black Belt オンラインセミナーの資料及び動画についてご案内させて頂きます。
コードの脆弱性に対応する AWS Continuum を発表します。テレメトリ収集とダッシュボード監視に頼る従来の運用モデルから、テレメトリ、コンテキスト、推論、アクションへと至る新しいアプローチへ移行します。フロンティアモデルを活用し、発見、優先順位付け、検証、緩和と修復という 4 つの継続的フェーズで、コードの脆弱性のライフサイクル全体にマシンスピードで対応する仕組みを紹介します。
大規模なデータ量を運用するうえで、運用面での重要な課題に直面します。Similarweb では Apache […]
Kiro のネイティブ iOS アプリが登場しました。スマートフォンから直接、クラウド上で動作する Kiro セッションの起動、監視、軌道修正、対話が可能になり、chat / spec / autonomous の 3 モードに対応します。差分はネイティブカードで読みやすく描画され、Web セッションと ID・設定・モデルがそのまま同期されます。ノート PC を開かずとも、移動中や待ち時間にエージェントへ作業を委譲し、後から PR として確認できます。
2026 年 6 月 16 日、Amazon Simple Storage Service (Amazon S […]
日立製作所・日立ハイテク・日立産業制御ソリューションズの3社・8チーム・52名が参加した「日立グループ合同 AI-DLC Unicorn Gym」の開催レポートです。AI駆動開発ライフサイクル（AI-DLC）を3日間で体験したワークショップの成果（開発工数70%以上削減を9割が体感、満足度4.67）に加え、日立グループへの本格展開を牽引するキーマンとの対談を通じて、品質保証との両立や「日立AI-DLC」構想、AI駆動開発ワーキンググループの立ち上げの計画までをお届けします。
日立製作所・日立ハイテク・日立産業制御ソリューションズの3社・8チーム・52名が参加した「日立グループ合同 AI-DLC Unicorn Gym」の開催レポートです。AI駆動開発ライフサイクル（AI-DLC）を3日間で体験したワークショップの成果（開発工数70%以上削減を9割が体感、満足度4.67）に加え、日立グループへの本格展開を牽引するキーマンとの対談を通じて、品質保証との両立や「日立AI-DLC」構想、AI駆動開発ワーキンググループの立ち上げの計画までをお届けします。
2026 年 6 月 17 日、ニューヨーク市で開催された AWS Summit では、AWS VP of A […]
2025 年 3 月に Amazon OpenSearch Service による検索ワークショップ（日本語版）のご紹介 という記事を公開し、OpenSearch の基本概念から AI を活用した検索までを学べる日本語ワークショップをご案内しました。 このたび、2 つの日本語版ワークショップが仲間入りいたしましたので、ご紹介いたします。 EC サイト検索ワークショップ：架空の EC サイトを題材に、検索機能を全文検索からセマンティック検索、マルチモーダル検索、エージェント検索へと段階的に育てていくワークショップです。また、ユーザーの行動ログを使った品質計測、機械学習による最適化を体験いただける実験的なラボも付属しています。 OpenSearch Observability Stack ワークショップ：OpenSearch を Observability のバックエンドとして使い、マイクロサービスの APM・ログ・メトリクスを横断しながら、Agentic AI も活用して障害の原因を調査するワークショップです。Agent Trace といった新しい OpenSearch の Observability 関連機能もお試しいただけます。
MetaQuestによるVR空間にて、HaritoraXでのモーショントラッキング技術とカメラによるMoveNetによる骨格推定を合わせて、パデルのフォーム分析。パデルトッププレイヤーとどのようにフォームが異なるのかを評価。AWS Summit 2026 Builders Fairのブースに出展。
What we believe We’ve been thinking deeply about enterprise security. The operating model that served us for the past decade (collect telemetry, store it, query it, build dashboards to watch it) is no longer keeping pace. We need to shift to the new world: telemetry, context, reasoning, and actions. An approach that produces outcomes. The […]
Bulletin ID: 2026-044-AWS
Scope: AWS
Content Type: Important (requires attention)
Publication Date: 06/17/2026 14:15 PM PDT
Description:
The AWS Bedrock AgentCore Python SDK (bedrock-agentcore) is an open-source SDK that enables developers to build, deploy, and manage agents on AWS Bedrock AgentCore. We identified CVE-2026-12530, an issue in the install_packages() method of the Code Interpreter client. The method applied an incomplete blocklist to sanitize package name arguments before constructing a 'pip install' shell command executed within the Code Interpreter sandbox. This allowed crafted package name arguments to bypass validation ‐ most critically, pip's '‐‐index‐url' flag, which could redirect package resolution to an third‐party‐controlled PyPI server, and the '-r' flag, which could read and expose arbitrary sandbox files.
Impacted versions: AWS Bedrock AgentCore Python SDK (bedrock-agentcore) versions >= 1.1.3 and < 1.6.1
Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.
Today we're introducing new capabilities on Amazon Bedrock AgentCore, the platform to build, connect, and optimize agents. In this post, we cover how these capabilities close each gap: connecting agents to organizational, web, and paid knowledge; helping teams find and fix what's going wrong in production; and enforcing controls that scale as agents grow more capable. Together, they help you build more capable agents faster, govern them with controls that scale, and improve them continuously.
Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in institutional knowledge that has never been written down. You want to trust the decisions made by your AI agents, but that can't happen until agents have context. Imagine what becomes possible when we give agents a safe way to access the context they need to deliver trusted decisions. This is why at the AWS Summit New York City, we’re announcing a series of innovations that deliver intelligence for your data and AI agents at scale.
Today, Quick gets even more powerful: new autonomous agents that work continuously on your behalf, an activity feed that helps you prioritize your most important work, and the ability to find insights across every data source your business runs on from a single question.
Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.