Amazon DynamoDB Accelerator (DAX) now supports AWS PrivateLink, enabling you to securely access DAX management APIs such as CreateCluster, DescribeClusters, and DeleteCluster over private IP addresses within your virtual private cloud (VPC). DAX clusters already run inside your VPC, and all data plane operations like GetItem and Query are handled privately within the VPC. With this launch, you can now perform cluster management operations privately, without connecting to the public regional endpoint.
With AWS PrivateLink, you can simplify private network connectivity between virtual private clouds (VPCs), DAX, and your on-premises data centers using interface VPC endpoints and private IP addresses. It helps you meet compliance regulations and eliminates the need to use public IP addresses, configure firewall rules, or configure an Internet gateway to access DAX from your on-premises data centers.
AWS PrivateLink for DAX is available in all Regions where DAX is available today. For information about DAX Regional availability, see the “Service endpoints” section in Amazon DynamoDB endpoints and quotas. There is an additional cost to use the feature. Please see AWS PrivateLink pricing for more details. To get started with DAX and PrivateLink, see AWS PrivateLink for DAX.
Amazon DynamoDB Accelerator (DAX) now supports AWS PrivateLink, enabling you to securely access DAX management APIs such as CreateCluster, DescribeClusters, and DeleteCluster over private IP addresses within your virtual private cloud (VPC). DAX clusters already run inside your VPC, and all data plane operations like GetItem and Query are handled privately within the VPC. With this launch, you can now perform cluster management operations privately, without connecting to the public regional endpoint. With AWS PrivateLink, you can simplify private network connectivity between virtual private clouds (VPCs), DAX, and your on-premises data centers using interface VPC endpoints and private IP addresses. It helps you meet compliance regulations and eliminates the need to use public IP addresses, configure firewall rules, or configure an Internet gateway to access DAX from your on-premises data centers. AWS PrivateLink for DAX is available in all Regions where DAX is available today. For information about DAX Regional availability, see the “Service endpoints” section in Amazon DynamoDB endpoints and quotas. There is an additional cost to use the feature. Please see AWS PrivateLink pricing for more details. To get started with DAX and PrivateLink, see AWS PrivateLink for DAX.
Amazon Aurora DSQL now supports Federal Information Processing Standards (FIPS) 140-3 compliant endpoints, helping companies contracting with the US federal governments meet the FIPS security requirement to encrypt sensitive data in supported Regions. With this launch, you can use Aurora DSQL for workloads that require a FIPS 140-3 validated cryptographic module when sending requests over public or VPC endpoints.
Aurora DSQL is the fastest serverless, distributed SQL database with single- and multi-Region clusters providing active-active high availability and strong consistency. Aurora DSQL enables you to build applications with virtually unlimited scalability, the highest availability, and zero infrastructure management.
Aurora DSQL FIPS compliant endpoints are now available in the following regions: US East (N. Virginia), US East (Ohio), and US West (Oregon). To learn more about FIPS 140-3 at AWS, visit FIPS 140-3 Compliance.
Amazon Aurora DSQL now supports Federal Information Processing Standards (FIPS) 140-3 compliant endpoints, helping companies contracting with the US federal governments meet the FIPS security requirement to encrypt sensitive data in supported Regions. With this launch, you can use Aurora DSQL for workloads that require a FIPS 140-3 validated cryptographic module when sending requests over public or VPC endpoints. Aurora DSQL is the fastest serverless, distributed SQL database with single- and multi-Region clusters providing active-active high availability and strong consistency. Aurora DSQL enables you to build applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. Aurora DSQL FIPS compliant endpoints are now available in the following regions: US East (N. Virginia), US East (Ohio), and US West (Oregon). To learn more about FIPS 140-3 at AWS, visit FIPS 140-3 Compliance.
Today, AWS announces the general availability of the Model Context Protocol (MCP) Proxy for AWS, a client-side proxy that enables MCP clients to connect to remote, AWS-hosted MCP servers using AWS SigV4 authentication. The Proxy supports popular agentic AI development tools like Amazon Q Developer CLI, Kiro, Cursor, and popular agent frameworks like Strands Agents. Customers can connect to remote MCP servers with AWS credentials using the Proxy to automatically handle MCP protocol communications via SigV4. The Proxy also helps customers to connect to MCP servers built on Amazon Bedrock AgentCore Gateway or Runtime using SigV4 authentication.
This release allows developers and agents to extend development workflows to include AWS service interactions from AWS MCP server tools. For example, you can use AWS MCP servers to work with resources like AWS S3 buckets or Amazon RDS tables through existing MCP servers with SigV4. The MCP Proxy for AWS includes safety controls such as read-only mode to prevent unintended changes, configurable retry logic for reliability, and logging for troubleshooting. Customers can install the Proxy from source, through Python package managers, or by using a container making it simple to configure with their preferred MCP-supported development tool.
The MCP Proxy for AWS is open-source and available now. Visit the AWS GitHub repositoryto view the installation and configuration options and start connecting with remote AWS MCP Servers today.
Today, AWS announces the general availability of the Model Context Protocol (MCP) Proxy for AWS, a client-side proxy that enables MCP clients to connect to remote, AWS-hosted MCP servers using AWS SigV4 authentication. The Proxy supports popular agentic AI development tools like Amazon Q Developer CLI, Kiro, Cursor, and popular agent frameworks like Strands Agents. Customers can connect to remote MCP servers with AWS credentials using the Proxy to automatically handle MCP protocol communications via SigV4. The Proxy also helps customers to connect to MCP servers built on Amazon Bedrock AgentCore Gateway or Runtime using SigV4 authentication. This release allows developers and agents to extend development workflows to include AWS service interactions from AWS MCP server tools. For example, you can use AWS MCP servers to work with resources like AWS S3 buckets or Amazon RDS tables through existing MCP servers with SigV4. The MCP Proxy for AWS includes safety controls such as read-only mode to prevent unintended changes, configurable retry logic for reliability, and logging for troubleshooting. Customers can install the Proxy from source, through Python package managers, or by using a container making it simple to configure with their preferred MCP-supported development tool. The MCP Proxy for AWS is open-source and available now. Visit the AWS GitHub repository to view the installation and configuration options and start connecting with remote AWS MCP Servers today.
AWS PrivateLink now supports native cross-region connectivity to AWS services. Until now, Interface VPC endpoints only supported connectivity to AWS services in the same Region. This launch enables customers to connect to select AWS services hosted in other Regions of the same AWS partition over Interface endpoints.
As a service consumer, you can access Amazon S3, Route53, Elastic Container Registry (ECR) and other services, privately without the need to setup cross-region peering or exposing your data over the public internet. These services can be accessed through Interface endpoints at a private IP address in your VPC, enabling simpler and more secure inter-region connectivity. This feature helps you build globally distributed private networks that comply with data residency requirements, while accessing supported AWS Services through PrivateLink
To learn about pricing for this feature, please see the AWS PrivateLink pricing page. For a complete list of supported AWS services and Regions, please refer to our documentation [link TBD]. To learn more, visit AWS PrivateLink in the Amazon VPC Developer Guide.
AWS PrivateLink now supports native cross-region connectivity to AWS services. Until now, Interface VPC endpoints only supported connectivity to AWS services in the same Region. This launch enables customers to connect to select AWS services hosted in other Regions of the same AWS partition over Interface endpoints. As a service consumer, you can access Amazon S3, Route53, Elastic Container Registry (ECR) and other services, privately without the need to setup cross-region peering or exposing your data over the public internet. These services can be accessed through Interface endpoints at a private IP address in your VPC, enabling simpler and more secure inter-region connectivity. This feature helps you build globally distributed private networks that comply with data residency requirements, while accessing supported AWS Services through PrivateLink To learn about pricing for this feature, please see the AWS PrivateLink pricing page. For a complete list of supported AWS services and Regions, please refer to our documentation [link TBD]. To learn more, visit AWS PrivateLink in the Amazon VPC Developer Guide.
Amazon Connect now supports scheduling of individual agents, giving you more flexibility in scheduling your workforce. For example, when onboarding 100 new agents to a business unit with schedules already published for next two months, you can create schedules for only those new agents and automatically merge them with existing schedules. This eliminates the need for workarounds such as manually copying schedules from existing agents to new agents or regenerating schedules for entire business unit, thus improving manager productivity and operational efficiency.
This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To learn more about Amazon Connect agent scheduling, click here.
Amazon Connect now supports scheduling of individual agents, giving you more flexibility in scheduling your workforce. For example, when onboarding 100 new agents to a business unit with schedules already published for next two months, you can create schedules for only those new agents and automatically merge them with existing schedules. This eliminates the need for workarounds such as manually copying schedules from existing agents to new agents or regenerating schedules for entire business unit, thus improving manager productivity and operational efficiency. This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To learn more about Amazon Connect agent scheduling, click here.
AWS Marketplace now offers flexible pricing models, simplified authentication, and streamlined deployment for AI agents and tools. The new capabilities include contract-based and usage-based pricing for Amazon Bedrock AgentCore Runtime containers, and simplified OAuth credential management through Quick Launch for API-based AI agents and tools. Customers can also use supported remote MCP servers procured through AWS Marketplace as MCP targets on AgentCore Gateway, making it easier for them to connect to AI agents and tools from AWS Partners at scale. The improvements reduce deployment complexity while offering pricing models that better align with diverse customer needs.
For Partners, the new capabilities for AI agents and tools streamline management and provide additional pricing options through AWS Marketplace. Partners can now manage all their AI agents and tools listings from one page in the AWS Marketplace Management Portal, reducing the complexity of managing multiple listings across different interfaces. With usage-based and contract-based pricing options for AgentCore Runtime compatible products, Partners have more flexibility to implement pricing strategies that align with their business models and customers’ needs.
Customers can learn more in the buyer guideand start exploring AI agent solutions in AWS Marketplace on the solutions page. For partners interested in implementing the capabilities, visit the seller guide and complete the workshop.
AWS Marketplace now offers flexible pricing models, simplified authentication, and streamlined deployment for AI agents and tools. The new capabilities include contract-based and usage-based pricing for Amazon Bedrock AgentCore Runtime containers, and simplified OAuth credential management through Quick Launch for API-based AI agents and tools. Customers can also use supported remote MCP servers procured through AWS Marketplace as MCP targets on AgentCore Gateway, making it easier for them to connect to AI agents and tools from AWS Partners at scale. The improvements reduce deployment complexity while offering pricing models that better align with diverse customer needs. For Partners, the new capabilities for AI agents and tools streamline management and provide additional pricing options through AWS Marketplace. Partners can now manage all their AI agents and tools listings from one page in the AWS Marketplace Management Portal, reducing the complexity of managing multiple listings across different interfaces. With usage-based and contract-based pricing options for AgentCore Runtime compatible products, Partners have more flexibility to implement pricing strategies that align with their business models and customers’ needs. Customers can learn more in the buyer guide and start exploring AI agent solutions in AWS Marketplace on the solutions page. For partners interested in implementing the capabilities, visit the seller guide and complete the workshop.
Por: Jared Spataro, CMO de IA en el trabajo de Microsoft
En estos días, sería difícil encontrar a alguien que necesite convencerse de que la IA cambia el futuro de los negocios y la sociedad en general. Para los líderes empresariales, se ha convertido en una cuestión de qué tan rápido pueden integrarla en el núcleo de su organización. Por muy rápido que lo hagan, su competencia trata de hacerlo más rápido.
La proporción de empleados estadounidenses que utilizan herramientas de IA en el trabajo, ya sea de manera ocasional, con frecuencia o a diario, ya se ha duplicado en dos años. Eso es un gran aumento de adopción. Y las organizaciones más progresistas, las Frontier Firms (Empresas Frontera), van más allá de la experimentación para establecer el ritmo y las reglas del juego para la era de la IA.
3 nuevos patrones de trabajo para la era de la IA
Las Empresas Frontera están dirigidas por humanos y operadas por agentes: compran inteligencia como electricidad, la ponen a trabajar como un empleado y la capitalizan como intereses. Estas organizaciones cambian todas las suposiciones, al reconstruir el trabajo desde cero para la colaboración entre humanos e IA. Y ya hemos comenzado a ver surgir nuevos patrones de trabajo que maximizan el valor de esa colaboración.
Hoy en día, pueden ver estos patrones con más claridad en el desarrollo de software, pero pronto los veremos aparecer en industrias, funciones y empresas de todos los tamaños. Las empresas establecidas ya los aplican a aquellas funciones con el mayor número de procesos definidos de manera clara y medidos como ventas, servicio y finanzas. Y han comenzado a aparecer en áreas donde la IA tiene fortalezas distintivas, como el marketing y la creación de contenido.
Cómo cobran vida los patrones
Echemos un vistazo a cada patrón y usemos el desarrollo de software para comprender cómo se ven en la práctica.
1. Humano + asistente de IA El patrón: Un individuo se empareja con un asistente de IA para eliminar la monotonía y acelerar la productividad.
En el desarrollo de software: El trabajo comienza con la IA. El asistente de IA sugiere código y pruebas, por lo que los desarrolladores dedican menos tiempo al trabajo pesado, lo que los libera para concentrarse en el diseño, el valor para el cliente y la calidad, el trabajo que realmente hace avanzar los productos.
2. Equipos humano-agente El patrón: Los agentes se unen a los equipos como trabajadores digitales para pasos o flujos de trabajo específicos, y colaborar con las personas para escalar el impacto.
En el desarrollo de software: la IA se inserta en los flujos de trabajo existentes. Las tareas (probar código nuevo, revisar cada actualización, verificar el cumplimiento) ya no consumen días durante un sprint. Los agentes dan el primer paso, redactan resúmenes, marcan problemas y sugieren soluciones. Los desarrolladores toman las decisiones finales, pero el equipo se mueve más rápido y con menos fricción.
3. Dirigido por humanos y operado por agentes El patrón: la plantilla óptima de Empresas Frontera: rediseñar los flujos de trabajo para que los agentes los ejecuten de principio a fin. Los humanos establecen metas, barandillas e intervienen solo cuando es necesario.
En el desarrollo de software: imaginen una canalización de lanzamiento que se ejecuta en gran medida en piloto automático. El desarrollo comienza con objetivos expresados en un lenguaje sencillo que los agentes convierten en borradores y prototipos. Esto permite a los equipos pequeños pasar de la idea a la demostración en días, lo que acelera el ciclo de retroalimentación. Los agentes crean, prueban, implementan y monitorean en canalizaciones bien definidas con barreras definidas por humanos; los desarrolladores se enfocan en qué construir a continuación e intervienen cuando surgen casos extremos.
No es una progresión lineal, es una frontera irregular
Estos patrones no aparecen en una progresión lineal o universal en toda la organización. Muchas empresas ven los tres a la vez, en diferentes rincones del negocio. Un equipo de marketing puede apoyarse en asistentes para redactar campañas, ingeniería ejecuta pruebas impulsadas por agentes, experimentos financieros con informes totalmente automatizados.
Pero no todos los patrones son adecuados para todos los flujos de trabajo, y la rapidez con la que se puede mover cada función depende del tiempo, el presupuesto y la capacidad.
La verdadera conclusión es que el desarrollo de software no solo agrega IA a las viejas rutinas, sino que rediseña su trabajo en torno a ella. A nivel de productividad personal, eso significa comenzar cada tarea con IA. A nivel de proceso, significa mapear flujos de trabajo, decidir dónde puede conectarse la IA y luego refinar o incluso reconstruir esos procesos de extremo a extremo. Un solo equipo de producto podría usar IA para redactar código por la mañana, colaborar con los agentes para probarlo esa tarde y enviar un lanzamiento a través de una canalización automatizada por agentes por la noche.
No solo coexisten, sino que se combinan, cada patrón refuerza a los demás para acelerar la escala y el impacto.
Los nuevos patrones que vemos en Empresas Frontera, y de manera más amplia en el desarrollo de software, pronto se extenderán a todo el trabajo. La elección para los líderes ahora es subirse a la ola o dejarse llevar.
Para obtener más información sobre la IA y el futuro del trabajo, suscríbanse a este boletín.
AWS announces USB redirection support for WorkSpaces running Amazon DCV protocol, enabling users to access locally connected USB devices from their virtual desktop environments. With this feature, customers can now connect a wide range of USB peripherals to their virtual desktops, including credit card readers, 3D mice, and other specialized devices.
USB redirection addresses the need for direct access to USB devices that require specialized drivers or lack dedicated protocols. This capability is currently limited to WorkSpaces Personal with Windows desktops accessed from Windows client devices. Performance and device compatibility may vary, so testing with your specific USB peripherals is recommended before adding them to the allowlist.
This feature is available in all AWS Regions where Amazon WorkSpaces is offered.
For more information about USB redirection in Amazon WorkSpaces, see USB Redirection for DCV in the Amazon WorkSpaces Administration Guide, or visit the Amazon WorkSpaces page to learn more about virtual desktop solutions from AWS.
AWS announces USB redirection support for WorkSpaces running Amazon DCV protocol, enabling users to access locally connected USB devices from their virtual desktop environments. With this feature, customers can now connect a wide range of USB peripherals to their virtual desktops, including credit card readers, 3D mice, and other specialized devices. USB redirection addresses the need for direct access to USB devices that require specialized drivers or lack dedicated protocols. This capability is currently limited to WorkSpaces Personal with Windows desktops accessed from Windows client devices. Performance and device compatibility may vary, so testing with your specific USB peripherals is recommended before adding them to the allowlist. This feature is available in all AWS Regions where Amazon WorkSpaces is offered. For more information about USB redirection in Amazon WorkSpaces, see USB Redirection for DCV in the Amazon WorkSpaces Administration Guide, or visit the Amazon WorkSpaces page to learn more about virtual desktop solutions from AWS.
Amazon announces the expansion of the TwelveLabs’ Pegasus 1.2 video understanding model to the US East (Ohio), US West (N. California), and Europe (Frankfurt) AWS Regions. This expansion makes it easier for customers to build and scale generative AI applications that can understand and interact with video content at an enterprise level.
Pegasus 1.2 is a powerful video-first language model that can generate text based on the visual, audio, and textual content within videos. Specifically designed for long-form video, it excels at video-to-text generation and temporal understanding. With Pegasus 1.2’s availability in these additional regions, you can now build video-intelligence applications closer to your data and end users in key geographic locations, reducing latency and simplifying your architecture.
With today’s expansion, Pegasus 1.2 is now available in Amazon Bedrock across 7 regions: US East (N. Virginia), US West (Oregon), US East (Ohio), US West (N. California), Europe (Ireland), Europe (Frankfurt), and Asia Pacific (Seoul). To get started with Pegasus 1.2, visit the Amazon Bedrock console. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation.
Amazon announces the expansion of the TwelveLabs’ Pegasus 1.2 video understanding model to the US East (Ohio), US West (N. California), and Europe (Frankfurt) AWS Regions. This expansion makes it easier for customers to build and scale generative AI applications that can understand and interact with video content at an enterprise level. Pegasus 1.2 is a powerful video-first language model that can generate text based on the visual, audio, and textual content within videos. Specifically designed for long-form video, it excels at video-to-text generation and temporal understanding. With Pegasus 1.2’s availability in these additional regions, you can now build video-intelligence applications closer to your data and end users in key geographic locations, reducing latency and simplifying your architecture. With today’s expansion, Pegasus 1.2 is now available in Amazon Bedrock across 7 regions: US East (N. Virginia), US West (Oregon), US East (Ohio), US West (N. California), Europe (Ireland), Europe (Frankfurt), and Asia Pacific (Seoul). To get started with Pegasus 1.2, visit the Amazon Bedrock console. To learn more, read the blog, product page, Amazon Bedrock pricing, and documentation.
Starting today, Split Cost Allocation Data for Amazon EKS now allows you to import up to 50 Kubernetes custom labels per pod as cost allocation tags. You can attribute costs of your Amazon EKS cluster at the pod level using custom attributes, such as cost center, application, business unit, and environment in AWS Cost and Usage Report (CUR).
With this new capability, you can better align your cost allocation with specific business requirements and organizational structure driven by your cloud financial management needs. This enables granular cost visibility of your EKS clusters running multiple application containers using shared EC2 instances, allowing you to allocate the shared costs of your EKS cluster. For new split cost allocation data customers, you can enable this feature in the AWS Billing and Cost Management console. For existing customers, EKS will automatically import the labels, but you must activate them as cost allocation tags. After activation, Kubernetes custom labels are available in your CUR within 24 hours. You can use the Containers Cost Allocation dashboard to visualize the costs in Amazon QuickSight and the CUR query library to query the costs using Amazon Athena.
This feature is available in all AWS Regions where Split Cost Allocation Data for Amazon EKS is available. To get started, visit Understanding Split Cost Allocation Data.
Starting today, Split Cost Allocation Data for Amazon EKS now allows you to import up to 50 Kubernetes custom labels per pod as cost allocation tags. You can attribute costs of your Amazon EKS cluster at the pod level using custom attributes, such as cost center, application, business unit, and environment in AWS Cost and Usage Report (CUR). With this new capability, you can better align your cost allocation with specific business requirements and organizational structure driven by your cloud financial management needs. This enables granular cost visibility of your EKS clusters running multiple application containers using shared EC2 instances, allowing you to allocate the shared costs of your EKS cluster. For new split cost allocation data customers, you can enable this feature in the AWS Billing and Cost Management console. For existing customers, EKS will automatically import the labels, but you must activate them as cost allocation tags. After activation, Kubernetes custom labels are available in your CUR within 24 hours. You can use the Containers Cost Allocation dashboard to visualize the costs in Amazon QuickSight and the CUR query library to query the costs using Amazon Athena. This feature is available in all AWS Regions where Split Cost Allocation Data for Amazon EKS is available. To get started, visit Understanding Split Cost Allocation Data.
