Amazon Bedrock now enables server-side tool execution through Amazon Bedrock AgentCore Gateway integration with the Responses API. Customers can connect their AgentCore Gateway tools to Amazon Bedrock models, enabling server-side tool execution without client-side orchestration.

With this launch, customers can specify an AgentCore Gateway ARN as a tool connector in Responses API requests. Amazon Bedrock automatically discovers available tools from the gateway, presents them to the model during inference, and executes tool calls server-side when the model selects them, all within a single API call. This eliminates the need for customers to build and maintain client-side tool orchestration loops, reducing application complexity and latency for agentic workflows. Customers retain full control over tool access through their existing AgentCore Gateway configurations and AWS IAM permissions.

Server-side tool execution with AgentCore Gateway supports all models available through the Amazon Bedrock Responses API. Customers define tools using the MCP server connector type with their gateway ARN, and Amazon Bedrock handles tool discovery, model-driven tool selection, execution, and result injection automatically. Multiple tool calls within a single conversation turn are supported, and tool results are streamed back to the client in real time.

This capability is generally available in all AWS Regions where both Amazon Bedrock’s Responses API and Amazon Bedrock AgentCore Gateway are available. To get started, visit the Amazon Bedrock documentation or the Amazon Bedrock console. For more information about Amazon Bedrock AgentCore Gateway, see the AgentCore documentation.

 

​Amazon Bedrock now enables server-side tool execution through Amazon Bedrock AgentCore Gateway integration with the Responses API. Customers can connect their AgentCore Gateway tools to Amazon Bedrock models, enabling server-side tool execution without client-side orchestration.
With this launch, customers can specify an AgentCore Gateway ARN as a tool connector in Responses API requests. Amazon Bedrock automatically discovers available tools from the gateway, presents them to the model during inference, and executes tool calls server-side when the model selects them, all within a single API call. This eliminates the need for customers to build and maintain client-side tool orchestration loops, reducing application complexity and latency for agentic workflows. Customers retain full control over tool access through their existing AgentCore Gateway configurations and AWS IAM permissions.
Server-side tool execution with AgentCore Gateway supports all models available through the Amazon Bedrock Responses API. Customers define tools using the MCP server connector type with their gateway ARN, and Amazon Bedrock handles tool discovery, model-driven tool selection, execution, and result injection automatically. Multiple tool calls within a single conversation turn are supported, and tool results are streamed back to the client in real time.
This capability is generally available in all AWS Regions where both Amazon Bedrock’s Responses API and Amazon Bedrock AgentCore Gateway are available. To get started, visit the Amazon Bedrock documentation or the Amazon Bedrock console. For more information about Amazon Bedrock AgentCore Gateway, see the AgentCore documentation.  

AWS Compute Optimizer makes it easier to identify snapshots that are created when snapshotting and deleting unattached Amazon Elastic Block Store (EBS) volumes by automatically applying an AWS-generated tag during creation. This enhancement improves visibility and tracking of EBS snapshots created through Compute Optimizer Automation.

When Compute Optimizer creates a snapshot before deleting an unattached EBS volume—whether initiated through manual actions or automation rules—the snapshot now receives the tag aws:compute-optimizer:automation-event-id with a tag value that links the snapshot to the unique identifier of the automation event that created it. This allows you to easily identify, track, and manage snapshots created through the automated optimization process, helping you maintain better governance over your backup resources and understand the source of snapshots in your environment.

This is available in all AWS Regions where AWS Compute Optimizer Automation is available. To get started with automated optimization, go to the AWS Compute Optimizer console or visit the user guide documentation.

 

​AWS Compute Optimizer makes it easier to identify snapshots that are created when snapshotting and deleting unattached Amazon Elastic Block Store (EBS) volumes by automatically applying an AWS-generated tag during creation. This enhancement improves visibility and tracking of EBS snapshots created through Compute Optimizer Automation.
When Compute Optimizer creates a snapshot before deleting an unattached EBS volume—whether initiated through manual actions or automation rules—the snapshot now receives the tag aws:compute-optimizer:automation-event-id with a tag value that links the snapshot to the unique identifier of the automation event that created it. This allows you to easily identify, track, and manage snapshots created through the automated optimization process, helping you maintain better governance over your backup resources and understand the source of snapshots in your environment.
This is available in all AWS Regions where AWS Compute Optimizer Automation is available. To get started with automated optimization, go to the AWS Compute Optimizer console or visit the user guide documentation.  

Today, AWS announces AWS Observability as a Kiro power, enabling developers and operators to investigate infrastructure and application health issues faster with AI agent-assisted workflows in Kiro. Kiro Powers is a repository of curated and pre-packaged Model Context Protocol (MCP) servers, steering files, and hooks validated by Kiro partners to accelerate specialized software development and deployment use cases.

The AWS Observability power packages four specialized MCP servers with targeted observability guidance: the CloudWatch MCP server for observability data; the Application Signals MCP server for application performance monitoring; the CloudTrail MCP server for security analysis and compliance; and the AWS Documentation MCP server for contextual reference access. This unified platform gives Kiro agents instant context for comprehensive workflows including alarm response, anomaly detection, distributed tracing, SLO compliance monitoring, and security investigation. Additionally, the power includes automated gap analysis that helps you identify and fix missing instrumentation.

With the AWS Observability power, developers can now accelerate troubleshooting their distributed applications and infrastructure in minutes, directly in their IDE. The power addresses two critical needs: reducing mean time to resolution (MTTR) for active incidents and proactively improving your observability stack. For faster incident response, when investigating an active alarm, the power dynamically loads relevant guidance and operational signals so AI agents receive only the context needed for the specific troubleshooting task at hand. For stack improvement, the automated gap analysis examines your code to identify missing instrumentation patterns—such as unlogged errors, missing correlation IDs, or absent distributed tracing—and provides actionable recommendations. The power includes eight comprehensive steering guides covering incident response, alerting, performance monitoring, security auditing, and gap analysis.

The AWS Observability power is available for one-click installation within Kiro IDE and Kiro powers webpage in all AWS Regions, with each underlying MCP server functional based on regional support of the corresponding AWS service. To learn more about AWS observability MCP servers, visit our documentation. 

 

​Today, AWS announces AWS Observability as a Kiro power, enabling developers and operators to investigate infrastructure and application health issues faster with AI agent-assisted workflows in Kiro. Kiro Powers is a repository of curated and pre-packaged Model Context Protocol (MCP) servers, steering files, and hooks validated by Kiro partners to accelerate specialized software development and deployment use cases. The AWS Observability power packages four specialized MCP servers with targeted observability guidance: the CloudWatch MCP server for observability data; the Application Signals MCP server for application performance monitoring; the CloudTrail MCP server for security analysis and compliance; and the AWS Documentation MCP server for contextual reference access. This unified platform gives Kiro agents instant context for comprehensive workflows including alarm response, anomaly detection, distributed tracing, SLO compliance monitoring, and security investigation. Additionally, the power includes automated gap analysis that helps you identify and fix missing instrumentation. With the AWS Observability power, developers can now accelerate troubleshooting their distributed applications and infrastructure in minutes, directly in their IDE. The power addresses two critical needs: reducing mean time to resolution (MTTR) for active incidents and proactively improving your observability stack. For faster incident response, when investigating an active alarm, the power dynamically loads relevant guidance and operational signals so AI agents receive only the context needed for the specific troubleshooting task at hand. For stack improvement, the automated gap analysis examines your code to identify missing instrumentation patterns—such as unlogged errors, missing correlation IDs, or absent distributed tracing—and provides actionable recommendations. The power includes eight comprehensive steering guides covering incident response, alerting, performance monitoring, security auditing, and gap analysis. The AWS Observability power is available for one-click installation within Kiro IDE and Kiro powers webpage in all AWS Regions, with each underlying MCP server functional based on regional support of the corresponding AWS service. To learn more about AWS observability MCP servers, visit our documentation.   

AWS is announcing Amazon EC2 I7ie instances are now available in AWS Africa (Cape Town) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances.

I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS).

To learn more, visit the I7ie instances page.

 

​AWS is announcing Amazon EC2 I7ie instances are now available in AWS Africa (Cape Town) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.  

Amazon RDS Snapshot Export to S3 is now available in AWS GovCloud (US) regions, enabling you to export snapshot data in Apache Parquet format for analytics, data retention, and machine learning use cases.

Snapshot export to S3 supports all DB snapshot types (manual, automated system, and AWS Backup snapshots) and runs directly on the snapshot without impacting database performance. The exported data in Apache Parquet format can be analyzed using other AWS services such as Amazon Athena, Amazon SageMaker, or Amazon Redshift Spectrum, or with big data processing frameworks such as Apache Spark.

You can create a snapshot export with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. Snapshot Export to S3 is supported for Amazon Aurora PostgreSQL – Compatible Edition and Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for MariaDB snapshots. For more information, including instructions on getting started, read Aurora documentation or Amazon RDS documentation.

 

​Amazon RDS Snapshot Export to S3 is now available in AWS GovCloud (US) regions, enabling you to export snapshot data in Apache Parquet format for analytics, data retention, and machine learning use cases. Snapshot export to S3 supports all DB snapshot types (manual, automated system, and AWS Backup snapshots) and runs directly on the snapshot without impacting database performance. The exported data in Apache Parquet format can be analyzed using other AWS services such as Amazon Athena, Amazon SageMaker, or Amazon Redshift Spectrum, or with big data processing frameworks such as Apache Spark. You can create a snapshot export with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. Snapshot Export to S3 is supported for Amazon Aurora PostgreSQL – Compatible Edition and Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for MariaDB snapshots. For more information, including instructions on getting started, read Aurora documentation or Amazon RDS documentation.  

Today, AWS Deadline Cloud announces support for grouping tasks into chunks to efficiently execute multiple tasks together. AWS Deadline Cloud is a fully managed service that simplifies render management for computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design.

When your job has short tasks, or tasks that need to run in an environment with a long startup time, chunking them together for execution reduces the time and cost for completing the job. When creating a job, you can now manually specify a chunk size for the number tasks to group together for execution, or alternately specify a target run time for the execution of a chunk of tasks. The target run time will be used to dynamically change the number of tasks grouped together as the job completes to improve execution efficiency and achieve the target run time.

Running tasks together in chunks is now available in all AWS Regions where AWS Deadline Cloud is supported. To get started, visit the Deadline Cloud developer guide.

 

​Today, AWS Deadline Cloud announces support for grouping tasks into chunks to efficiently execute multiple tasks together. AWS Deadline Cloud is a fully managed service that simplifies render management for computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design. When your job has short tasks, or tasks that need to run in an environment with a long startup time, chunking them together for execution reduces the time and cost for completing the job. When creating a job, you can now manually specify a chunk size for the number tasks to group together for execution, or alternately specify a target run time for the execution of a chunk of tasks. The target run time will be used to dynamically change the number of tasks grouped together as the job completes to improve execution efficiency and achieve the target run time. Running tasks together in chunks is now available in all AWS Regions where AWS Deadline Cloud is supported. To get started, visit the Deadline Cloud developer guide.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i and C8i-flex instances are available in the Asia Pacific (Malaysia) and South America (Sao Paulo) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. These C8i and C8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i and C7i-flex instances, with even higher gains for specific workloads. The C8i and C8i-flex are up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i and C7i-flex.

C8i-flex are the easiest way to get price performance benefits for a majority of compute intensive workloads like web and application servers, databases, caches, Apache Kafka, Elasticsearch, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources.

C8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. C8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications.

To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i and C8i-flex instances visit the AWS News blog.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i and C8i-flex instances are available in the Asia Pacific (Malaysia) and South America (Sao Paulo) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. These C8i and C8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i and C7i-flex instances, with even higher gains for specific workloads. The C8i and C8i-flex are up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i and C7i-flex. C8i-flex are the easiest way to get price performance benefits for a majority of compute intensive workloads like web and application servers, databases, caches, Apache Kafka, Elasticsearch, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. C8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. C8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i and C8i-flex instances visit the AWS News blog.  

Starting today, the general-purpose Amazon EC2 R7a instances are now available in AWS Asia Pacific (Hyderabad) Region. R7a instances, powered by 4th Gen AMD EPYC processors (code-named Genoa) with a maximum frequency of 3.7 GHz, deliver up to 50% higher performance compared to R6a instances.

These instances can be purchased as Savings Plans, Reserved, On-Demand, and Spot instances. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the R7a instances page.

 

​Starting today, the general-purpose Amazon EC2 R7a instances are now available in AWS Asia Pacific (Hyderabad) Region. R7a instances, powered by 4th Gen AMD EPYC processors (code-named Genoa) with a maximum frequency of 3.7 GHz, deliver up to 50% higher performance compared to R6a instances. These instances can be purchased as Savings Plans, Reserved, On-Demand, and Spot instances. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the R7a instances page.  

AWS Elemental Inference, a fully managed Artificial Intelligence (AI) service that enables broadcasters and streamers to automatically generate vertical content and highlight clips for mobile and social platforms in real time, is now generally available. The service applies AI capabilities to live and on-demand video in parallel with encoding and helps companies and creators to reach audiences in any format without requiring AI expertise or dedicated production teams.

With Elemental Inference you can process video once and optimize it everywhere—creating main broadcasts while simultaneously generating vertical versions for TikTok, Instagram Reels, YouTube Shorts, Snapchat, and other mobile platforms in parallel with live video. For example, sports broadcasters can automatically generate vertical highlight clips during live games and distribute them to social platforms in real-time, capturing viral moments as they happen rather than hours later. 

The service launches with two AI features: vertical video cropping that transforms live and on-demand landscape broadcasts into mobile-optimized formats, and advanced metadata analysis that identifies key moments to generate highlight clips from live content. Using an agentic AI application that requires no prompts or human-in-the-loop intervention, broadcasters can scale content production without adding manual workflows or production staff—the system automatically adapts content for each platform. In beta testing, large media companies achieved 34% or more savings on AI-powered live video workflows compared to using multiple point solutions.

AWS Elemental Inference is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), and Europe (Ireland).

For more information, visit the AWS News Blog or explore the AWS Elemental Inference documentation.

 

​AWS Elemental Inference, a fully managed Artificial Intelligence (AI) service that enables broadcasters and streamers to automatically generate vertical content and highlight clips for mobile and social platforms in real time, is now generally available. The service applies AI capabilities to live and on-demand video in parallel with encoding and helps companies and creators to reach audiences in any format without requiring AI expertise or dedicated production teams.
With Elemental Inference you can process video once and optimize it everywhere—creating main broadcasts while simultaneously generating vertical versions for TikTok, Instagram Reels, YouTube Shorts, Snapchat, and other mobile platforms in parallel with live video. For example, sports broadcasters can automatically generate vertical highlight clips during live games and distribute them to social platforms in real-time, capturing viral moments as they happen rather than hours later. 
The service launches with two AI features: vertical video cropping that transforms live and on-demand landscape broadcasts into mobile-optimized formats, and advanced metadata analysis that identifies key moments to generate highlight clips from live content. Using an agentic AI application that requires no prompts or human-in-the-loop intervention, broadcasters can scale content production without adding manual workflows or production staff—the system automatically adapts content for each platform. In beta testing, large media companies achieved 34% or more savings on AI-powered live video workflows compared to using multiple point solutions.
AWS Elemental Inference is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), and Europe (Ireland).
For more information, visit the AWS News Blog or explore the AWS Elemental Inference documentation.  

Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Frankfurt) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.

M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.

M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.

To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.

 

​Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Frankfurt) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances. M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.