Amazon CloudWatch alarms that use Prometheus Query Language (PromQL) can now trigger Amazon EC2 and Application Auto Scaling step and simple scaling policies. You can scale your EC2 Auto Scaling groups on CloudWatch OpenTelemetry metrics using the same kind of PromQL queries you use for dashboards and alerting.

In this release, PromQL alarms also support EC2 Auto Scaling simple and step scaling policies. Target tracking is not currently a supported policy type for PromQL alarms. Teams moving to OpenTelemetry can scale capacity on the same metrics as the rest of their monitoring, instead of keeping separate classic metric alarms for scaling. For step scaling, how far the value is past the threshold in your PromQL query decides which step applies. The query must return a single series and compare it to a threshold.

You can add EC2 Auto Scaling and Application Auto Scaling actions in the CloudWatch console when you create or edit an alarm, or with the PutMetricAlarm API, AWS CLI, AWS SDKs, or AWS CloudFormation. You can also use the PutMetricAlarm API, AWS CLI, AWS SDKs, or AWS CloudFormation.

This feature is available in all AWS Regions where CloudWatch PromQL alarms are available. Standard CloudWatch alarm pricing applies. To get started, see  Using PromQL in alarms  and  Alarm actions  in the Amazon CloudWatch User Guide.

 

​Amazon CloudWatch alarms that use Prometheus Query Language (PromQL) can now trigger Amazon EC2 and Application Auto Scaling step and simple scaling policies. You can scale your EC2 Auto Scaling groups on CloudWatch OpenTelemetry metrics using the same kind of PromQL queries you use for dashboards and alerting.

In this release, PromQL alarms also support EC2 Auto Scaling simple and step scaling policies. Target tracking is not currently a supported policy type for PromQL alarms. Teams moving to OpenTelemetry can scale capacity on the same metrics as the rest of their monitoring, instead of keeping separate classic metric alarms for scaling. For step scaling, how far the value is past the threshold in your PromQL query decides which step applies. The query must return a single series and compare it to a threshold.

You can add EC2 Auto Scaling and Application Auto Scaling actions in the CloudWatch console when you create or edit an alarm, or with the PutMetricAlarm API, AWS CLI, AWS SDKs, or AWS CloudFormation. You can also use the PutMetricAlarm API, AWS CLI, AWS SDKs, or AWS CloudFormation.
This feature is available in all AWS Regions where CloudWatch PromQL alarms are available. Standard CloudWatch alarm pricing applies. To get started, see  Using PromQL in alarms  and  Alarm actions  in the Amazon CloudWatch User Guide.  

Today, AWS announces the general availability of the Web Search API on Amazon Bedrock AgentCore, which lets you search the web with a single API call. Web Search on AgentCore has been available as an MCP tool through AgentCore Gateway. The new InvokeWebSearch API gives applications, custom agent loops, and data pipelines search results with no resources to create.

The API returns ranked results from the Amazon-operated web index behind Web Search on AgentCore. Each result includes a text extract and, when available, the page title, URL, and publication time. You can filter each request by domain and publication time, for example to find recent product announcements or check a claim against trusted domains.

You call the API with the AWS SDKs or the AWS Command Line Interface (AWS CLI) and authorize calls with AWS Identity and Access Management (IAM). You can call it from your virtual private cloud (VPC) through AWS PrivateLink and log requests as AWS CloudTrail data events. Web Search API queries are billed at $7 per 1,000 queries, the same rate as Web Search through AgentCore Gateway, with no gateway charges.

The Web Search API is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Ireland). To learn more, see Search the web with the InvokeWebSearch API in the AgentCore Developer Guide, the Web Search  page, and AgentCore pricing .

 

​Today, AWS announces the general availability of the Web Search API on Amazon Bedrock AgentCore, which lets you search the web with a single API call. Web Search on AgentCore has been available as an MCP tool through AgentCore Gateway. The new InvokeWebSearch API gives applications, custom agent loops, and data pipelines search results with no resources to create.
The API returns ranked results from the Amazon-operated web index behind Web Search on AgentCore. Each result includes a text extract and, when available, the page title, URL, and publication time. You can filter each request by domain and publication time, for example to find recent product announcements or check a claim against trusted domains.
You call the API with the AWS SDKs or the AWS Command Line Interface (AWS CLI) and authorize calls with AWS Identity and Access Management (IAM). You can call it from your virtual private cloud (VPC) through AWS PrivateLink and log requests as AWS CloudTrail data events. Web Search API queries are billed at $7 per 1,000 queries, the same rate as Web Search through AgentCore Gateway, with no gateway charges.
The Web Search API is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Ireland). To learn more, see Search the web with the InvokeWebSearch API in the AgentCore Developer Guide, the Web Search  page, and AgentCore pricing .  

Amazon CloudWatch Logs now supports account-level retention policies, so you can use a single policy to set how long log data is kept across all or subset of log groups in an AWS account. Account-level policies allow platform and security teams to bring each log group in line with their retention standards.

With today’s launch, you can create policies that cover all log groups in the account or apply to a specific set of log groups with names that start with a prefix, such as /aws/lambda. You can additionally choose whether it applies to existing log groups or only new ones. When more than one policy matches a log group, the longest retention period applies. For example, a central platform team can set a 90-day baseline for the account and a 1-year policy for log groups that start with /security to meet audit requirements, without tracking individual log group names.

Account-level retention policies are available in all commercial AWS Regions except Middle East (Bahrain), and Middle East (UAE) at no additional cost . You can create and manage up to 20 policies per account and region using the AWS Management Console, AWS CLI, and AWS SDKs. Learn more in the  CloudWatch Logs documentation, and the  API reference .

 

​Amazon CloudWatch Logs now supports account-level retention policies, so you can use a single policy to set how long log data is kept across all or subset of log groups in an AWS account. Account-level policies allow platform and security teams to bring each log group in line with their retention standards.
With today’s launch, you can create policies that cover all log groups in the account or apply to a specific set of log groups with names that start with a prefix, such as /aws/lambda. You can additionally choose whether it applies to existing log groups or only new ones. When more than one policy matches a log group, the longest retention period applies. For example, a central platform team can set a 90-day baseline for the account and a 1-year policy for log groups that start with /security to meet audit requirements, without tracking individual log group names.
Account-level retention policies are available in all commercial AWS Regions except Middle East (Bahrain), and Middle East (UAE) at no additional cost . You can create and manage up to 20 policies per account and region using the AWS Management Console, AWS CLI, and AWS SDKs. Learn more in the  CloudWatch Logs documentation, and the  API reference .  

AWS Glue now supports 100 new native connectors that simplify access to data in popular SaaS and REST API-based applications. Previously, bringing data from SaaS applications into AWS required writing custom code. Now, teams can create a connection once in the AWS Glue Data Catalog and reuse it across ETL jobs with the same permissions model they already use for AWS Glue.

The new connectors span generative AI, finance, payments, marketing, product analytics, human resources, engineering, IT operations, and collaboration tools. AI teams can bring usage and cost data from model providers such as Anthropic to track generative AI adoption and spend. Finance teams can consolidate transactions from applications such as Adyen, Square, and Xero for faster reporting. Product and marketing teams can combine billing, usage, and campaign data from tools such as Chargebee, Amplitude, and Klaviyo to understand customer lifetime value. HR teams can unify workforce data from systems such as BambooHR and Gusto for headcount planning. Engineering teams can join incident and deployment data from tools such as PagerDuty, Sentry, Jenkins, and Vercel to track reliability. You can load the integrated data into Amazon S3 or Amazon Redshift for analytics and reporting.

The 100 new AWS Glue connectors are available in all AWS Commercial Regions on Glue versions 5.0 and above.

To learn more, see the complete list of available connectors and learn about connecting to individual connectors in the AWS Glue documentation.

 

​AWS Glue now supports 100 new native connectors that simplify access to data in popular SaaS and REST API-based applications. Previously, bringing data from SaaS applications into AWS required writing custom code. Now, teams can create a connection once in the AWS Glue Data Catalog and reuse it across ETL jobs with the same permissions model they already use for AWS Glue.
The new connectors span generative AI, finance, payments, marketing, product analytics, human resources, engineering, IT operations, and collaboration tools. AI teams can bring usage and cost data from model providers such as Anthropic to track generative AI adoption and spend. Finance teams can consolidate transactions from applications such as Adyen, Square, and Xero for faster reporting. Product and marketing teams can combine billing, usage, and campaign data from tools such as Chargebee, Amplitude, and Klaviyo to understand customer lifetime value. HR teams can unify workforce data from systems such as BambooHR and Gusto for headcount planning. Engineering teams can join incident and deployment data from tools such as PagerDuty, Sentry, Jenkins, and Vercel to track reliability. You can load the integrated data into Amazon S3 or Amazon Redshift for analytics and reporting.
The 100 new AWS Glue connectors are available in all AWS Commercial Regions on Glue versions 5.0 and above.
To learn more, see the complete list of available connectors and learn about connecting to individual connectors in the AWS Glue documentation.  

Amazon Quick, the AI workspace built only for work, is now AWS Quick, a name that reflects the enterprise security and governance built into Quick from the start as an AWS service. The new name now appears across the Quick website, the web, desktop, and mobile apps, and AWS documentation. Existing customers don’t need to take any action, and Quick works the same way it did before.

Amazon Quick Sight, the business intelligence capability in Quick, is now AWS Quick Sight. Quick Sight customers can keep using their dashboards, datasets, and analyses just as they do today, with no changes to their data, settings, or pricing.

With Quick, teams use AI agents to research, analyze, and act across the data and applications they already use, regardless of vendor. Quick understands your role, your team, and how your work connects, and anyone can build agents, apps, and automations in plain language that become shared assets for the whole team. Quick is HIPAA eligible and holds compliance certifications including FedRAMP, SOC 2, and ISO 27001. Your data stays in your environment and never trains someone else’s models.

AWS Quick is available in many AWS Regions. For more information, see the AWS Region table.

To learn more, see AWS Quick.

 

​Amazon Quick, the AI workspace built only for work, is now AWS Quick, a name that reflects the enterprise security and governance built into Quick from the start as an AWS service. The new name now appears across the Quick website, the web, desktop, and mobile apps, and AWS documentation. Existing customers don’t need to take any action, and Quick works the same way it did before.
Amazon Quick Sight, the business intelligence capability in Quick, is now AWS Quick Sight. Quick Sight customers can keep using their dashboards, datasets, and analyses just as they do today, with no changes to their data, settings, or pricing.
With Quick, teams use AI agents to research, analyze, and act across the data and applications they already use, regardless of vendor. Quick understands your role, your team, and how your work connects, and anyone can build agents, apps, and automations in plain language that become shared assets for the whole team. Quick is HIPAA eligible and holds compliance certifications including FedRAMP, SOC 2, and ISO 27001. Your data stays in your environment and never trains someone else’s models.
AWS Quick is available in many AWS Regions. For more information, see the AWS Region table.
To learn more, see AWS Quick.  

AWS Security Token Service (AWS STS) now lets your workloads use their AWS Identity and Access Management (IAM) identity in one partition to get temporary credentials for an IAM role in a different partition, without storing long-term credentials, which can help improve your security posture. Partitions are groups of AWS Regions, such as the commercial and AWS GovCloud (US) Regions, and each partition has an independent instance of IAM, creating a hard boundary between them. Previously, organizations that run workloads across partitions commonly relied on long-term credentials, such as IAM user access keys, that don’t expire automatically.

With this launch, your workload requests a short-lived token from AWS STS in its own partition and exchanges it for temporary AWS credentials in the other partition, using the existing GetWebIdentityToken and AssumeRoleWithWebIdentity APIs. You control which identities can access each role, apply governance across your organization, and audit token use in AWS CloudTrail in both partitions. This launch helps with scenarios such as consolidated security and compliance reporting, and centralized operations tooling. If your organization already operates a public key infrastructure (PKI), IAM Roles Anywhere is another option for cross-partition access.

Cross-partition federation is available at no additional charge in all commercial AWS Regions, the AWS GovCloud (US) Regions, the China Regions, and the AWS European Sovereign Cloud (Germany) Region. To learn more, see Federating AWS identities across AWS partitions in the IAM User Guide and the blog post Simplify cross-partition access with AWS IAM outbound identity federation.

 

​AWS Security Token Service (AWS STS) now lets your workloads use their AWS Identity and Access Management (IAM) identity in one partition to get temporary credentials for an IAM role in a different partition, without storing long-term credentials, which can help improve your security posture. Partitions are groups of AWS Regions, such as the commercial and AWS GovCloud (US) Regions, and each partition has an independent instance of IAM, creating a hard boundary between them. Previously, organizations that run workloads across partitions commonly relied on long-term credentials, such as IAM user access keys, that don’t expire automatically.
With this launch, your workload requests a short-lived token from AWS STS in its own partition and exchanges it for temporary AWS credentials in the other partition, using the existing GetWebIdentityToken and AssumeRoleWithWebIdentity APIs. You control which identities can access each role, apply governance across your organization, and audit token use in AWS CloudTrail in both partitions. This launch helps with scenarios such as consolidated security and compliance reporting, and centralized operations tooling. If your organization already operates a public key infrastructure (PKI), IAM Roles Anywhere is another option for cross-partition access.
Cross-partition federation is available at no additional charge in all commercial AWS Regions, the AWS GovCloud (US) Regions, the China Regions, and the AWS European Sovereign Cloud (Germany) Region. To learn more, see Federating AWS identities across AWS partitions in the IAM User Guide and the blog post Simplify cross-partition access with AWS IAM outbound identity federation.  

Amazon Connect Customer now provides evaluation form templates so managers don’t have to build forms from scratch, reducing the time to deploy automated evaluations of human and AI agent performance. These templates cover typical customer interaction scenarios: customer service, inbound sales, and self-service. Each template assesses human and AI agent behaviors. For example, the customer service template includes questions such as «Did the agent show empathy toward the customer’s issue?» Templates also include questions that assess business outcomes such as customer satisfaction and attrition risk, so managers can see how agent behaviors relate to outcomes. Managers can use the templates as-is or customize them before deploying.

This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo). To learn more, visit our documentation and our webpage. For Amazon Connect Customer pricing, visit our pricing page.

 

​Amazon Connect Customer now provides evaluation form templates so managers don’t have to build forms from scratch, reducing the time to deploy automated evaluations of human and AI agent performance. These templates cover typical customer interaction scenarios: customer service, inbound sales, and self-service. Each template assesses human and AI agent behaviors. For example, the customer service template includes questions such as «Did the agent show empathy toward the customer’s issue?» Templates also include questions that assess business outcomes such as customer satisfaction and attrition risk, so managers can see how agent behaviors relate to outcomes. Managers can use the templates as-is or customize them before deploying.
This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo). To learn more, visit our documentation and our webpage. For Amazon Connect Customer pricing, visit our pricing page.  

Today, AWS is introducing an open-source solution with a local web UI that helps customers turn data lake records into graphs for analysis with Amazon Neptune Analytics. By projecting selected records and relationships, customers can investigate connections across tables and run graph algorithms without building their own ETL pipelines.

For example, a customer could project accounts and transactions into a graph to explore how accounts are connected or identify communities that may warrant investigation. Doing this today often means writing queries to prepare node and relationship data, creating a Neptune Analytics graph, and managing the import. Customers may also need to build a selective projection so they do not load an entire dataset for a narrower question.

The new UI brings those steps into single workflow. To run graph analytics over iceberg tables, customers select an Athena catalog and database, define a property graph using SQL/PGQ, and preview rows before import. The solution runs the projection queries through Athena, stages the selected data in Amazon S3, and imports it into Neptune Analytics. The UI helps customers manage the resulting graph. They can open the loaded graph in Graph Explorer to query and visualize relationships. [1]

The UI runs locally and it is available in all regions where Amazon Neptune Analytics is available. To get started, see the setup instructions.

 

​Today, AWS is introducing an open-source solution with a local web UI that helps customers turn data lake records into graphs for analysis with Amazon Neptune Analytics. By projecting selected records and relationships, customers can investigate connections across tables and run graph algorithms without building their own ETL pipelines.
For example, a customer could project accounts and transactions into a graph to explore how accounts are connected or identify communities that may warrant investigation. Doing this today often means writing queries to prepare node and relationship data, creating a Neptune Analytics graph, and managing the import. Customers may also need to build a selective projection so they do not load an entire dataset for a narrower question.
The new UI brings those steps into single workflow. To run graph analytics over iceberg tables, customers select an Athena catalog and database, define a property graph using SQL/PGQ, and preview rows before import. The solution runs the projection queries through Athena, stages the selected data in Amazon S3, and imports it into Neptune Analytics. The UI helps customers manage the resulting graph. They can open the loaded graph in Graph Explorer to query and visualize relationships. [1]
The UI runs locally and it is available in all regions where Amazon Neptune Analytics is available. To get started, see the setup instructions.  

Claude Fable 5.2 is now available on AWS GovCloud (US) and brings Anthropic’s most capable generally available model for technical work to regulated customers. Fable 5.2 sets a new standard on the hardest projects in coding, math, and science, and Anthropic recommends it for fields like software engineering, scientific research, and finance, where judgment, iteration, and accuracy are critical. Claude Mythos 5.2, the same underlying model as Claude Fable 5.2, with its full cyber and bio capabilities retained for cybersecurity and biology research, is available through Anthropic’s verification programs.

Claude Fable 5.2 can work for hours and up to days unattended, driving autonomous work and taking on bigger, more challenging projects. It takes on large, loosely defined engineering problems across multiple repositories and monorepos, tests its own work as it goes, and is more willing to question inherited conclusions, so it finds root causes previous Claude models miss. In Anthropic’s testing, it coordinated hundreds of subagents as a lead agent and flagged results that don’t reproduce. Beyond coding, it develops new analytical methods for scientific research and brings frontier reasoning to quantitative finance.

Anthropic has designated Fable 5.2 and Mythos 5.2 as covered models, which require data retention for safety monitoring. Until Enterprise Frontier Safeguards is available, eligible customers can use Fable 5.2 with zero data retention. Visit the Amazon Bedrock abuse detection page to learn more about details regarding data retention for these models.

To learn more, see the Amazon Bedrock documentation, and regional availability.

 

​Claude Fable 5.2 is now available on AWS GovCloud (US) and brings Anthropic’s most capable generally available model for technical work to regulated customers. Fable 5.2 sets a new standard on the hardest projects in coding, math, and science, and Anthropic recommends it for fields like software engineering, scientific research, and finance, where judgment, iteration, and accuracy are critical. Claude Mythos 5.2, the same underlying model as Claude Fable 5.2, with its full cyber and bio capabilities retained for cybersecurity and biology research, is available through Anthropic’s verification programs.
Claude Fable 5.2 can work for hours and up to days unattended, driving autonomous work and taking on bigger, more challenging projects. It takes on large, loosely defined engineering problems across multiple repositories and monorepos, tests its own work as it goes, and is more willing to question inherited conclusions, so it finds root causes previous Claude models miss. In Anthropic’s testing, it coordinated hundreds of subagents as a lead agent and flagged results that don’t reproduce. Beyond coding, it develops new analytical methods for scientific research and brings frontier reasoning to quantitative finance.
Anthropic has designated Fable 5.2 and Mythos 5.2 as covered models, which require data retention for safety monitoring. Until Enterprise Frontier Safeguards is available, eligible customers can use Fable 5.2 with zero data retention. Visit the Amazon Bedrock abuse detection page to learn more about details regarding data retention for these models.
To learn more, see the Amazon Bedrock documentation, and regional availability.  

Amazon Bedrock, the platform for building generative AI applications and agents at production scale, now offers Claude Mythos 5.2 to eligible customers in AWS GovCloud (US). Claude Mythos 5.2 is Anthropic’s most advanced AI model to date for use cases across cybersecurity, software coding, and complex reasoning tasks. The model can identify sophisticated security vulnerabilities in software and demonstrate exploitability. It can also comprehend large codebases and deliver actionable findings with less manual guidance than previous AI models. This enables security teams to accelerate defensive cybersecurity work, find and fix security vulnerabilities in the world’s most critical software, and address them before threats emerge. Claude Mythos 5.2 is the first of Anthropic’s AI models with powerful cybersecurity capabilities to be available in AWS GovCloud (US).

Claude Mythos 5.2 is available to eligible customers in AWS GovCloud (US) Regions through Amazon Bedrock. Access is limited to allow-listed organizations; to get started, visit Anthropic’s Cyber Verification Program. 

 

​Amazon Bedrock, the platform for building generative AI applications and agents at production scale, now offers Claude Mythos 5.2 to eligible customers in AWS GovCloud (US). Claude Mythos 5.2 is Anthropic’s most advanced AI model to date for use cases across cybersecurity, software coding, and complex reasoning tasks. The model can identify sophisticated security vulnerabilities in software and demonstrate exploitability. It can also comprehend large codebases and deliver actionable findings with less manual guidance than previous AI models. This enables security teams to accelerate defensive cybersecurity work, find and fix security vulnerabilities in the world’s most critical software, and address them before threats emerge. Claude Mythos 5.2 is the first of Anthropic’s AI models with powerful cybersecurity capabilities to be available in AWS GovCloud (US).
Claude Mythos 5.2 is available to eligible customers in AWS GovCloud (US) Regions through Amazon Bedrock. Access is limited to allow-listed organizations; to get started, visit Anthropic’s Cyber Verification Program.