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.
Today, AWS Clean Rooms announces support for advanced configurations to improve the performance of Spark SQL queries. This launch enables you to customize Spark properties and compute sizes for SQL queries at runtime, offering increased flexibility to meet your performance, scale, and cost requirements.
With AWS Clean Rooms, you can configure Spark properties—such as shuffle partition settings for parallel processing and autoBroadcastJoinThreshold for optimizing join operations—to help you better control the behavior and tuning of SQL queries in a Clean Rooms collaboration. Additionally, you can choose to cache an existing table’s data containing results from a SQL query or create and cache a new table, which help improve the performance and reduce costs for complex queries using large datasets. For example, an advertiser running lift analysis on their advertising campaigns can specify a custom number of workers for an instance type and configure Spark properties—without editing their SQL query—to optimize costs.
With AWS Clean Rooms, customers can create a secure data clean room in minutes and collaborate with any company on AWS or Snowflake to generate unique insights about advertising campaigns, investment decisions, and research and development. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
Today, AWS Clean Rooms announces support for advanced configurations to improve the performance of Spark SQL queries. This launch enables you to customize Spark properties and compute sizes for SQL queries at runtime, offering increased flexibility to meet your performance, scale, and cost requirements. With AWS Clean Rooms, you can configure Spark properties—such as shuffle partition settings for parallel processing and autoBroadcastJoinThreshold for optimizing join operations—to help you better control the behavior and tuning of SQL queries in a Clean Rooms collaboration. Additionally, you can choose to cache an existing table’s data containing results from a SQL query or create and cache a new table, which help improve the performance and reduce costs for complex queries using large datasets. For example, an advertiser running lift analysis on their advertising campaigns can specify a custom number of workers for an instance type and configure Spark properties—without editing their SQL query—to optimize costs. With AWS Clean Rooms, customers can create a secure data clean room in minutes and collaborate with any company on AWS or Snowflake to generate unique insights about advertising campaigns, investment decisions, and research and development. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
AWS Step Functions announces improved observability with a new metrics dashboard, giving you visibility into your workflow operations at both the account and state machine levels. AWS Step Functions is a visual workflow service capable of orchestrating over 14,000+ API actions from over 220 AWS services to build distributed applications and data processing workloads.
With this launch, you can now view usage and billing metrics in one dashboard on the AWS Step Functions console. Metrics are available at both account and state-machine level. You can now view these metrics for both standard and express workflows. In addition, existing metrics, such as ApproximateOpenMapRunCount, are available on the metrics dashboard.
New dashboard and metrics are available in all AWS Regions where AWS Step Functions is available. To get started, open a dashboard today in the AWS Step Functions console. To learn more, visit the Step Functions developer guide.
AWS Step Functions announces improved observability with a new metrics dashboard, giving you visibility into your workflow operations at both the account and state machine levels. AWS Step Functions is a visual workflow service capable of orchestrating over 14,000+ API actions from over 220 AWS services to build distributed applications and data processing workloads. With this launch, you can now view usage and billing metrics in one dashboard on the AWS Step Functions console. Metrics are available at both account and state-machine level. You can now view these metrics for both standard and express workflows. In addition, existing metrics, such as ApproximateOpenMapRunCount, are available on the metrics dashboard. New dashboard and metrics are available in all AWS Regions where AWS Step Functions is available. To get started, open a dashboard today in the AWS Step Functions console. To learn more, visit the Step Functions developer guide.
Today, Amazon GameLift Servers launched the addition of built-in telemetry metrics across all server SDKs and game engine plugins. Built on OpenTelemetry, an open source framework, Amazon GameLift Servers telemetry metrics enable game developers to generate, collect, and export critical client-side metrics for game-specific insights.
With this release, Amazon GameLift Servers can now be configured to collect and publish telemetry metrics for game servers running on managed Amazon EC2 and container fleets. Customers can leverage both pre-defined metrics and custom metrics, publishing them to Amazon Managed Service for Prometheus or Amazon CloudWatch. This data can be visualized through ready-to-use dashboards (via Amazon Managed Grafana or Amazon CloudWatch) to help game developers optimize resource utilization, improve player experience, and identify and resolve potential operational issues.
Telemetry metrics are now available in all Amazon GameLift Servers supported regions, except AWS China. For more information on monitoring resources using telemetry metrics on Amazon GameLift Servers, please visit the Amazon GameLift Servers documentation.
Today, Amazon GameLift Servers launched the addition of built-in telemetry metrics across all server SDKs and game engine plugins. Built on OpenTelemetry, an open source framework, Amazon GameLift Servers telemetry metrics enable game developers to generate, collect, and export critical client-side metrics for game-specific insights. With this release, Amazon GameLift Servers can now be configured to collect and publish telemetry metrics for game servers running on managed Amazon EC2 and container fleets. Customers can leverage both pre-defined metrics and custom metrics, publishing them to Amazon Managed Service for Prometheus or Amazon CloudWatch. This data can be visualized through ready-to-use dashboards (via Amazon Managed Grafana or Amazon CloudWatch) to help game developers optimize resource utilization, improve player experience, and identify and resolve potential operational issues. Telemetry metrics are now available in all Amazon GameLift Servers supported regions, except AWS China. For more information on monitoring resources using telemetry metrics on Amazon GameLift Servers, please visit the Amazon GameLift Servers documentation.
You can now create Amazon S3 Access Grants in the AWS Asia Pacific (Thailand) and AWS Mexico (Central) Regions.
Amazon S3 Access Grants map identities in directories such as Microsoft Entra ID, or AWS Identity and Access Management (IAM) principals, to datasets in S3. This helps you manage data permissions at scale by automatically granting S3 access to end users based on their corporate identity.
Visit the AWS Region Table for complete regional availability information. To learn more about Amazon S3 Access Grants, visit our product page.
You can now create Amazon S3 Access Grants in the AWS Asia Pacific (Thailand) and AWS Mexico (Central) Regions. Amazon S3 Access Grants map identities in directories such as Microsoft Entra ID, or AWS Identity and Access Management (IAM) principals, to datasets in S3. This helps you manage data permissions at scale by automatically granting S3 access to end users based on their corporate identity. Visit the AWS Region Table for complete regional availability information. To learn more about Amazon S3 Access Grants, visit our product page.
