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Amazon Quick adds new tool settings and Model Context Protocol (MCP) sync support for connectors

Amazon Quick connectors let users leverage tools and services such as Outlook, Slack, Salesforce, Jira, and homegrown MCP servers directly into their workflows across chat, agents, apps, flows, and deep research. Today, Amazon Quick introduces new tool settings and MCP sync support that give admins and connector owners more control over how connectors are deployed and kept up to date.
Connector owners and admins can now selectively enable or disable individual tools within a connector to ensure only approved tools are available to end users. Additionally, new tool permission settings let connector owners decide which tools require consent before proceeding or give end users the flexibility to decide for themselves. Lastly, MCP sync keeps connectors current as external MCP servers add new tools, update descriptions, and evolve their capabilities, ensuring users always have the latest information to get their work done.
These features are available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick User Guide.
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Amazon Connect Customer expands automated performance evaluations to Malay

Amazon Connect Customer now automates evaluations of human and AI agents in Malay using generative AI. Managers define custom evaluation criteria in natural language and receive AI-generated evaluations with justifications in their preferred language. Performance evaluations also supports cross-language evaluation and can complete assessments in English, even when the conversation is in Malay. This enables multilingual contact centers to use a standardized evaluation framework across languages.
This feature is supported in 8 AWS regions including US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (London), Canada (Central), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Asia Pacific (Singapore). For information about Amazon Connect pricing, please visit our pricing page. To learn more, please visit our documentation and our webpage.
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Web Search on Amazon Bedrock is now available in AWS GovCloud (US-West)

The Web Search built-in server-side tool on Amazon Bedrock is now available in AWS GovCloud (US-West), helping bring grounded web results to compliance-sensitive government and public-sector workloads. Web Search helps supported OpenAI GPT models ground responses with information from the web. Responses include citations to the sources the model used so users can trace each claim back to its web origin. This can be especially valuable whenever an answer depends on information that changes over time or is more recent than a model’s training data, such as current events, recent releases or live pricing. Because the tool runs inside Amazon Bedrock, you don’t host a search index, manage crawlers, or write the tool-call loop yourself.
Web Search is designed to support the governance and data-handling standards AWS GovCloud (US) customers require. By default, it keeps your request data within the AWS boundary, serving results from a web index and cache maintained by Amazon. As an AWS-native capability governed by AWS Identity and Access Management (IAM), administrators can allow or deny it at the account or organization level and restrict it by Region, giving teams centralized control while keeping request data within the AWS boundary by default. To get started, add a tool of type web_search to the tools array in your OpenAI Responses API request using your existing OpenAI client library with an Amazon Bedrock API key. The model uses the tool only when it determines a request needs current information. At launch, Web Search in AWS GovCloud (US-West) supports GPT-5.4 , GPT-5.6 Terra and Luna models.
Web Search is available in AWS GovCloud (US-West), in addition to US East (N. Virginia), US East (Ohio), and US West (Oregon). To get started, see the Web Search technical blog. For implementation guidance, see the Web Search documentation. For pricing, see the Amazon Bedrock pricing page.
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AWS Backup now supports protecting more than 1,000 Amazon S3 buckets per account

AWS Backup now supports backup and restore of more than 1,000 Amazon S3 buckets per account, matching the Amazon S3 bucket quota configured for your account.
Previously, AWS Backup for Amazon S3 supported up to 1,000 buckets per account. With this launch, AWS Backup supports backing up all general purpose buckets in your account. Existing backup plans and configurations continue to work with no changes required if you are using default AWS Backup managed policies. For details on the required permissions for custom policies, see the AWS Backup documentation.
This capability is available in all AWS commercial and AWS GovCloud (US) Regions. To get started, visit the AWS Backup console.
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Amazon CloudWatch Database Insights now supports self-managed PostgreSQL

Today, AWS announces support for self-managed PostgreSQL databases in Amazon CloudWatch Database Insights. Using CloudWatch Database Insights, you can now monitor your self-managed PostgreSQL instances running on Amazon EC2, alongside Amazon RDS and Amazon Aurora, to give you a complete view of your database fleet in a single console. 
Amazon CloudWatch Database Insights is a database observability solution that provides a curated experience to help you monitor database fleet health, diagnose issues, and resolve performance bottlenecks. With support for self-managed databases, you use the Amazon CloudWatch agent to collect health and performance data from your self-managed PostgreSQL instances. Your database appears in the Database Insights fleet view with live performance data, including database load, wait event analysis, query-level statistics, and host metrics. Monitor and troubleshoot your self-managed PostgreSQL instances using the same familiar Database Insights console and workflows you already use for your AWS-managed databases.
Amazon CloudWatch Database Insights support for self-managed PostgreSQL is available in all AWS Commercial Regions. To get started, see Monitoring Self-Managed Databases in the Amazon CloudWatch User Guide. For pricing details, see the Amazon CloudWatch pricing page.
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Amazon Kinesis Data Streams now supports a dry run feature to validate API requests

Amazon Kinesis Data Streams now supports a dry run feature to check whether an API request would succeed without executing the operation. Customers can now set the new optional parameter ‘DryRun’ to true in their API requests to validate permissions before interacting with a stream in production.
Previously, customers had no safe way to test whether their application had the correct permissions to access a stream. They would often send a request engineered to fail after checking permissions, such as a PutRecord request with a payload deliberately larger than the maximum supported size. This approach was fragile as it depended on current service limits, and if those limits ever changed, the request could unexpectedly succeed, writing unintended records into the production stream and into any downstream consumers. Now, customers can simply set the parameter ‘DryRun’ to true to indicate they only want to validate the API request. If all checks complete successfully, the API returns a ‘DryRunOperationException’, confirming the request would have succeeded without the ‘DryRun’ parameter.
The dry run feature is available for five APIs (PutRecord, PutRecords, GetRecords, GetShardIterator, and SubscribeToShard) in all AWS Regions where Amazon Kinesis Data Streams is available. For more information about dry run, see Test your permissions and request inputs with dry run in the Amazon Kinesis Data Streams Developer Guide. To get started with dry run, see the Amazon Kinesis Data Streams API Reference.
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AWS Deadline Cloud now supports sharing job bundles

AWS Deadline Cloud now supports sharing job bundles, giving teams a simple way to distribute and reuse render job templates without manual file distribution. Deadline Cloud is a fully managed service that helps teams run compute-intensive workloads in the cloud for visual effects, animation, product design, simulation, and gaming. 
Job bundles define the jobs you submit to Deadline Cloud, including the job template, parameters, and asset references. Previously, sharing a bundle with teammates meant distributing files through shared drives or manual copying. Now, you can publish a bundle to your queue directly from the submitter or the command line, and it immediately becomes available to everyone with access to that queue. Shared bundles are packaged as portable archives and stored in the queue’s existing job attachments bucket in Amazon S3, so there is no additional infrastructure or configuration to set up. This makes it easy for pipeline teams to publish standard, ready-to-submit job templates that artists can pick up and use.
You can browse bundles shared on your queue, on your local filesystem, or from your job submission history. You can preview each bundle’s name, description, steps, and parameters before selecting it. New CLI commands let you manage shared bundles from the command line and integrate bundle sharing into pipeline scripts.
This feature is available in all AWS Regions where Deadline Cloud is available. To get started, visit the AWS Deadline Cloud documentation.
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Amazon Quick now lets you build custom apps with natural language -

Now generally available, Amazon Quick lets you build custom applications by simply describing them in natural language. Create project trackers, customer dashboards, and training portals in minutes instead of months. Whether you’re a product manager, finance lead, HR partner, or ops analyst, you can now turn ideas into fully functional apps without writing any code. Tell Quick what you need, and it builds a live, connected application with real-time data from your existing business systems.
Quick connects directly to the tools and data your business already uses, including Salesforce, Jira, Asana, ServiceNow, Microsoft 365, Google Workspace, databases, and data warehouses. Your apps stay current automatically as underlying data changes, and every connection respects your organization’s existing identity, authorization, and access control policies. Once built, you can publish and share apps instantly with specific users or your entire organization. Need to make a change like adding AI, updating the visual style, or connecting to another system? Just describe what you want and Quick handles it.
During preview, customers used Quick to replace spreadsheets and disconnected tools with purpose-built applications. New York Life built an e-learning portal for their Institutional Life team that consolidates onboarding, training, and compliance courses into a single experience. The Amazon Quick team tracks pipeline, customer requests, and adoption metrics in a weekly leadership review app that replaced manual data pulls from four different systems.
Building apps in Quick is available to Plus, Professional, and Enterprise customers starting September 1, 2026. To learn more, visit the Apps in Quick getting-started guide.
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Amazon CloudWatch now supports warm-up periods for alarms

Amazon CloudWatch now lets you configure a warm-up period for metric alarms and log alarms, delaying alarm evaluation for a set time after the alarm is created. This reduces noise from missing data while a new resource or service starts up and begins publishing metrics. For example, a team that provisions a new microservice and its alarms together through a CI/CD pipeline can attach a warm-up period so alarms do not page the on-call engineer while the service is still starting up and has not yet published metrics.

Previously, when you created an alarm before the underlying metric was reporting data, CloudWatch evaluated the alarm right away using its “treat missing data” setting. For resources that take time to begin emitting metrics, such as a newly deployed application or service, this could cause the alarm to transition state and run actions on missing data during startup, triggering unnecessary notifications. A warm-up period fixes this by giving you two ways to hold off evaluation during startup: wait a fixed duration you set before evaluation begins, or let CloudWatch start evaluating automatically as soon as the metric actually has enough data to fill the alarm’s evaluation window.

You set the warm-up period with the WarmUpConfiguration parameter when you create or update an alarm. Specify a warm-up duration from 1 to 2,880 minutes (2 days). By default, the alarm ends warm-up early and begins evaluating as soon as enough data fills its evaluation window. Additionally, you can optionally require the alarm to wait the full duration before evaluating.

Warm-up periods are available in all AWS Regions where Amazon CloudWatch is offered at no additional charge beyond standard CloudWatch alarm pricing.

To get started, see  Alarm warm-up periods  and  Create an alarm that uses a warm-up period  in the Amazon CloudWatch User Guide.
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Claude Fable 5.1, Anthropic's new frontier model is now available on AWS

Claude Fable 5.1 is generally available on AWS and brings Anthropic’s most capable frontier model to all customers. Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. A clear improvement over Claude Fable 5, Fable 5.1 is a step up in intelligence on the hardest reasoning tasks, providing better judgement on ambiguous work and fewer confident wrong answers. Claude Mythos 5.1, the same underlying model as Claude Fable 5.1 with its full cyber and bio capabilities retained for cybersecurity and biology research, is available with limited access.
Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its own, handling features across an entire codebase, code review, and performance work over sessions that run for hours. If it gets stuck, it says so instead of reporting success, and it is less likely to take shortcuts like disabling a failing test. It takes analysis from the first question to the finished document, and it is Anthropic’s strongest model yet for knowledge work.
Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. Enterprise Frontier Safeguards (EFS) built in partnership between AWS and Anthropic, will let eligible customers use Covered Models while keeping their data in a cloud environment they control. Read the launch blog to learn more.
Customers have two ways to access Claude Fable 5.1: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Amazon Bedrock documentation, regional availability, and Claude Platform on AWS documentation..
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