Amazon FSx for OpenZFS now allows you to create Multi-AZ file systems in shared VPCs within your AWS organization, making it easier for you to decentralize network and storage administration.
VPC sharing is a feature that allows resource owners («owner accounts») to share one or more VPC subnets with other accounts («participant accounts») in their AWS organization. Participant accounts can then view, create, modify, delete, and manage their application resources in the subnets shared with them. Previously, participant accounts could create Single-AZ OpenZFS file systems in VPCs shared with them, but could only create Multi-AZ file systems in VPCs they owned. Starting today, participant accounts can create any FSx for OpenZFS file system in a shared VPC, allowing organizations to run highly available file systems with centralized network management.
Amazon FSx for OpenZFS now allows you to create Multi-AZ file systems in shared VPCs within your AWS organization, making it easier for you to decentralize network and storage administration.
VPC sharing is a feature that allows resource owners («owner accounts») to share one or more VPC subnets with other accounts («participant accounts») in their AWS organization. Participant accounts can then view, create, modify, delete, and manage their application resources in the subnets shared with them. Previously, participant accounts could create Single-AZ OpenZFS file systems in VPCs shared with them, but could only create Multi-AZ file systems in VPCs they owned. Starting today, participant accounts can create any FSx for OpenZFS file system in a shared VPC, allowing organizations to run highly available file systems with centralized network management.
You can create Multi-AZ FSx for OpenZFS file systems from shared VPC participant accounts in all AWS Regions where Amazon FSx for OpenZFS is available. To learn more, visit the FSx for OpenZFS documentation and the FSx for OpenZFS product page.
Amazon RDS for Oracle now offers M8i and R8i instances with Oracle Database Standard Edition 2 (SE2) with the License Included (LI). M8i and R8i 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. The new instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances.
With RDS for Oracle SE2 LI, customers don’t have to separately purchase Oracle license and support. Amazon RDS for Oracle SE2 LI offers subscription based pay-per-use pricing inclusive of software license, support, compute resources, and a managed database service. To use RDS for Oracle SE2 LI, customers can create database instances from the AWS Management Console or using the AWS CLI. and specify the LI option. For more details about how you can lower cost and simplify operations of running Oracle databases, refer to the AWS blog Rethink Oracle Standard Edition Two on Amazon RDS for Oracle.
Amazon RDS for Oracle now offers M8i and R8i instances with Oracle Database Standard Edition 2 (SE2) with the License Included (LI). M8i and R8i 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. The new instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. With RDS for Oracle SE2 LI, customers don’t have to separately purchase Oracle license and support. Amazon RDS for Oracle SE2 LI offers subscription based pay-per-use pricing inclusive of software license, support, compute resources, and a managed database service. To use RDS for Oracle SE2 LI, customers can create database instances from the AWS Management Console or using the AWS CLI. and specify the LI option. For more details about how you can lower cost and simplify operations of running Oracle databases, refer to the AWS blog Rethink Oracle Standard Edition Two on Amazon RDS for Oracle. Configuration details for available instance types can be found on the Amazon RDS for Oracle Instance Types page. Review the AWS blog Rethink Oracle Standard Edition Two on Amazon RDS for Oracle to explore how you can lower cost and simplify operations by using Amazon RDS Oracle SE2 License Included instances for your Oracle databases. For pricing and AWS Region availability, see Amazon RDS for Oracle Pricing.
Automaticen procesos empresariales con agentes más flujos de trabajo en Microsoft Copilot Studio
Por: Ashvini Sharma, director asociado de gestión de producto, Copilot Studio.
Presentamos nuevas capacidades en Microsoft Copilot Studio que les ayudan a automatizar sus procesos empresariales a través de combinar agentes de IA y flujos de trabajo. Agentes y flujos de trabajo ya existen en Copilot Studio como dos capacidades complementarias con fortalezas únicas. Los agentes aportan razonamiento y adaptabilidad; los flujos de trabajo aportan estructura y consistencia.
Entonces, ¿cómo saber cuándo usar agentes y cuándo flujos de trabajo?
Ya no es una decisión de una cosa o una cosa otra. Aquí tienen cómo usar agentes y flujos de trabajo juntos para combinar fortalezas y reducir riesgos.
¿Qué son los agentes y los flujos de trabajo?
Los agentes son soluciones de IA flexibles que dependen de modelos fundamentales para actuar, compartir conocimientos y gestionar tareas. Son poderosos justo porque son flexibles. Pueden interpretar entradas no estructuradas, razonar sobre el contexto y tomar decisiones más allá de la lógica fija.
Sin embargo, las organizaciones a menudo necesitan saber que las partes repetitivas de sus procesos se comportarán de manera consistente cada vez que se ejecutan. La autonomía pura del agente no siempre cumple con ese requisito en producción.
Los flujos de trabajo, en cambio, son automatizaciones potentes que impulsan la ejecución de procesos con consistencia y rapidez. Están diseñados para ofrecer la fiabilidad que muchos procesos empresariales requieren.
Al mismo tiempo, la automatización rígida basada en reglas tiene su propio techo. Es casi imposible anticipar todos los posibles formatos de entrada, casos límite y contextos de toma de decisiones al construir un conjunto de reglas de flujo de trabajo. Por tanto, cuando la automatización del flujo de trabajo se encuentra con algo inesperado, no puede avanzar.
Dos patrones para la automatización de escalado con IA
Aunque tanto los agentes como los flujos de trabajo tienen sus puntos fuertes, vemos que los clientes obtienen el mayor valor en Copilot Studio al combinar ambos. En la práctica, hemos observado que surgen dos patrones en la forma en que los clientes aplican Copilot Studio, y ofrecemos mejoras en los productos para fortalecerlos y apoyarlos.
Flujos de trabajo que utilizan agentes
El primer patrón son los flujos de trabajo que llaman a agentes. En estos casos, el flujo de trabajo proporciona la estructura para el proceso empresarial: los pasos definidos, la lógica de ramificación, las transferencias y una pista de auditoría. Mientras tanto, el agente se encarga de las partes del proceso que requieren juicio. Esto puede incluir interpretar un documento, sintetizar información de múltiples fuentes o decidir cómo enrutar una excepción.
Una vez que el agente completa su trabajo, el control vuelve al flujo de trabajo y la ejecución continúa de manera predecible.
Para facilitar la adición de agentes a los flujos de trabajo en Copilot Studio, presentamos nodos agentes: la capacidad de que los flujos de trabajo en Copilot Studio llamen de manera directa a un agente dentro de un flujo de trabajo. Pueden construir una automatización determinista y fiable, y en el momento exacto en que necesitan razonamiento de IA, el flujo tan solo lo pasa a un agente.
Configurar un nodo agente dentro de un flujo de trabajo es sencillo:
Creen un paso de flujo de trabajo llamado «Añadir un agente».
Seleccionen cualquier agente de Copilot Studio que quieran incluir en el flujo de trabajo.
Proporcionen las instrucciones o la tarea que el agente debe cumplir, e incluyan la opción de contactar con una persona designada si se necesita una aclaración específica.
Añadan el resto de los pasos del flujo de trabajo.
Cuando ejecutan el flujo de trabajo, el agente hará su trabajo en la etapa adecuada y luego el resto del flujo continuará de manera automática.
Añadir un nodo agente dentro de un flujo de trabajo
Cuándo usar agentes dentro de los flujos de trabajo
Usar nodos de agente para incluir agentes en sus flujos de trabajo desbloquea escenarios que la automatización rígida por sí sola no puede manejar. Algunos usos potenciales incluyen los siguientes:
Un flujo de trabajo de compras que dirige a un agente para evaluar las propuestas de proveedores en relación con las políticas de la empresa.
Un flujo de trabajo de incorporación de RRHH que personaliza los materiales de bienvenida según el rol y el departamento.
Un proceso de atención al cliente que escale casos complejos a un agente de IA para recomendaciones de resolución.
En general, dondequiera que su flujo de trabajo tome una decisión que no pueda capturarse con lógica simple de si-entonces —donde necesita usar el razonamiento sobre el contexto, orquestar herramientas o extraer conocimiento de múltiples fuentes— un nodo agente puede ayudar a cerrar la brecha y hacer su flujo de trabajo más efectivo. Esta capacidad está disponible ahora en todas las regiones.
El segundo patrón es por igual importante: agentes que utilizan flujos de trabajo como herramientas. Cuando un agente trabaja en una tarea compleja, no necesita redescubrir cómo actuar cada vez. En su lugar, puede llamar a un flujo de trabajo fiable y probado para ejecutar un subproceso bien definido—y luego usar el resultado para continuar su razonamiento y respuesta.
Esta capacidad ayuda a los agentes a construir sobre la infraestructura de procesos existente en lugar de reinventarla. Además, ayuda a dar a las organizaciones más confianza en que las partes de alta frecuencia o de alto riesgo pueden funcionar con la coherencia y los controles que la organización requiere.
Hay dos formas de añadir flujos de trabajo a un agente:
De manera alternativa, desde dentro del agente, pueden acceder a su biblioteca de flujos de trabajo preexistentes y añadirlos como herramientas. Luego, proporcionen instrucciones explícitas a su agente sobre cuándo usar el flujo de trabajo.
Eso es todo: el orquestador de su agente seleccionará los flujos de trabajo adecuados en el momento adecuado cuando sea necesario para completar su trabajo.
Biblioteca de flujos preexistentes que pueden añadir a su agente
Cuándo usar flujos de trabajo dentro de los agentes
Añadir flujos de trabajo dentro de sus agentes ayuda a dar estructura y coherencia a interacciones que aún requieren flexibilidad. Algunos usos potenciales incluyen los siguientes:
Un agente de ventas recopila los detalles correctos del producto y el nivel de precios para una oferta, luego llama a un flujo de trabajo para generar el presupuesto, aplicar las reglas de descuento y encaminarlo para su aprobación.
Un agente de atención al cliente determina que un reembolso está justificado, luego llama a un flujo de trabajo para validarlo conforme a las normas del negocio, procesar la reversión del pago y enviar la confirmación.
Un agente de compras evalúa qué proveedor y qué condiciones se aplican a una solicitud, luego llama a un flujo de trabajo para crear la orden de compra en el sistema ERP y la enruta a través de la cadena de aprobación.
Por lo general, en cualquier lugar donde su agente necesite ejecutar un proceso repetible de manera fiable —al hacer cumplir las normas de negocio, coordinar sistemas o asegurándose de que se completen los pasos clave—, un flujo de trabajo puede ayudar a fundamentar sus acciones y hacer que los resultados sean más consistentes.
Empiecen a usar agentes y flujos de trabajo juntos
Juntas, estas dos maneras de combinar agentes y flujos de trabajo les ofrecen flexibilidad para crear automatizaciones que funcionen mejor para sus necesidades reales. Los agentes gestionan la ambigüedad cuando los flujos de trabajo se vuelven frágiles; los flujos de trabajo imponen la estructura donde los agentes pueden derivar.
Al adoptar una combinación de agentes y flujos de trabajo, resulta más fácil para los distintos equipos involucrarse de maneras que mejor se adapten a su forma de trabajar. Los equipos empresariales pueden ampliar y adaptar estas soluciones de automatización sin tener que reconstruir desde cero. Los equipos de cumplimiento pueden auditarlos. Por último, sus equipos de seguridad y gobernanza pueden elegir el equilibrio adecuado entre consistencia y agilidad, en función de lo que requiera cada escenario.
Un flujo de trabajo de compras llama a un agente para evaluar contratos con proveedores que llegan en formatos inconsistentes.
Un agente de atención al cliente, que gestiona una solicitud abierta, llama a un flujo de trabajo para iniciar un reembolso o actualizar un registro de cuenta.
Un proceso de aprobación invoca a un agente para sintetizar el contexto antes de enviarlo a un responsable de la toma de decisiones—y, por separado, ese mismo agente llama a un flujo de trabajo para enviar notificaciones, registrar resultados o iniciar pasos posteriores.
Estos escenarios muestran cómo la automatización y la inteligencia pueden reforzarse de manera mutua, al combinar estructura y flexibilidad para ofrecer resultados más adaptables y fiables.
Today, AWS announces the release of full repository code review, a new capability in AWS Security Agent that performs deep, context-aware security analysis of your entire codebase. Unlike traditional static analysis tools that match code against known vulnerability patterns, full repository code review reasons about your application’s architecture, trust boundaries, and data flows to surface systemic vulnerabilities that pattern-matching tools miss. When vulnerabilities are found, the scanner generates code remediation, specific fixes tied to the exact file and line, so teams can identify and remediate security vulnerabilities faster than ever before. This capability is available at no additional charge for existing AWS Security Agent customers during the preview.
AI-driven cybersecurity capabilities are advancing rapidly. AWS Security Agent can find vulnerabilities and build working exploits at a scale and speed we haven’t seen before. AWS is prioritizing free early access for customers, giving defenders the opportunity to strengthen their codebases and share what they learn so the whole industry can benefit.
Full repository code review is available in in all AWS Regions where AWS Security Agent is available.
Today, AWS announces the release of full repository code review, a new capability in AWS Security Agent that performs deep, context-aware security analysis of your entire codebase. Unlike traditional static analysis tools that match code against known vulnerability patterns, full repository code review reasons about your application’s architecture, trust boundaries, and data flows to surface systemic vulnerabilities that pattern-matching tools miss. When vulnerabilities are found, the scanner generates code remediation, specific fixes tied to the exact file and line, so teams can identify and remediate security vulnerabilities faster than ever before. This capability is available at no additional charge for existing AWS Security Agent customers during the preview.
AI-driven cybersecurity capabilities are advancing rapidly. AWS Security Agent can find vulnerabilities and build working exploits at a scale and speed we haven’t seen before. AWS is prioritizing free early access for customers, giving defenders the opportunity to strengthen their codebases and share what they learn so the whole industry can benefit.
Full repository code review is available in in all AWS Regions where AWS Security Agent is available.
To get started, visit the AWS Security Agent console to enable full repository code review and run your first review. To learn more, see the AWS Security Agent documentation.
Amazon Connect Customer now enables you to embed Cases and Customer Profiles into custom agent applications, helping agents access case details and customer context alongside the tools they already use to resolve issues. Developers can use the Amazon Connect SDK to bring native Connect experiences into custom applications, reducing the need to build and maintain these capabilities from scratch.
The Amazon Connect SDK is available in all AWS Regions where Amazon Connect Customer is available. To learn more and get started, visit the administrator guide and developer guide.
Amazon Connect Customer now enables you to embed Cases and Customer Profiles into custom agent applications, helping agents access case details and customer context alongside the tools they already use to resolve issues. Developers can use the Amazon Connect SDK to bring native Connect experiences into custom applications, reducing the need to build and maintain these capabilities from scratch. The Amazon Connect SDK is available in all AWS Regions where Amazon Connect Customer is available. To learn more and get started, visit the administrator guide and developer guide.
AWS Lambda now supports scheduled scaling for functions running on Lambda Managed Instances, using Amazon EventBridge Scheduler. This capability allows you to define one-time or recurring schedules that proactively adjust your function’s capacity limits ahead of expected traffic, to meet your performance targets during peak periods and avoid costs during idle periods.
Lambda Managed Instances lets you run Lambda functions on managed Amazon EC2 instances with built-in routing, load balancing, and autoscaling. Capacity scales between your configured minimum and maximum execution environment limits based on traffic. Previously, customers with predictable traffic patterns, such as business-hours applications or marketing events, were required to manually adjust capacity limits ahead of known demand changes or build custom automation to manage scaling on a schedule. With scheduled scaling, you can now define schedules that proactively adjust your function’s capacity limits ahead of expected traffic. For example, you can schedule capacity limits to increase before business hours so execution environments are ready when the first requests arrive. You can also define a schedule that scales capacity to zero during idle periods (so you only pay when the function is actively serving traffic), and schedule it to scale back up before traffic returns.
AWS Lambda now supports scheduled scaling for functions running on Lambda Managed Instances, using Amazon EventBridge Scheduler. This capability allows you to define one-time or recurring schedules that proactively adjust your function’s capacity limits ahead of expected traffic, to meet your performance targets during peak periods and avoid costs during idle periods. Lambda Managed Instances lets you run Lambda functions on managed Amazon EC2 instances with built-in routing, load balancing, and autoscaling. Capacity scales between your configured minimum and maximum execution environment limits based on traffic. Previously, customers with predictable traffic patterns, such as business-hours applications or marketing events, were required to manually adjust capacity limits ahead of known demand changes or build custom automation to manage scaling on a schedule. With scheduled scaling, you can now define schedules that proactively adjust your function’s capacity limits ahead of expected traffic. For example, you can schedule capacity limits to increase before business hours so execution environments are ready when the first requests arrive. You can also define a schedule that scales capacity to zero during idle periods (so you only pay when the function is actively serving traffic), and schedule it to scale back up before traffic returns. Scheduled scaling for functions running on Lambda Managed Instances is available in all AWS Regions where Lambda Managed Instances is supported. You can create schedules using the Amazon EventBridge Scheduler console, AWS CLI, AWS SDK, AWS CDK, or AWS CloudFormation. To learn more, visit the AWS Lambda Managed Instances documentation, Amazon EventBridge Scheduler documentation, AWS Lambda pricing, and Amazon EventBridge pricing.
Previously, the Amazon CloudFront Premium flat-rate plan supported a single usage allowance, and customers who outgrew it needed to contact us to discuss custom pricing options. Now, the Premium plan offers a range of self-service monthly usage levels ranging from 500 million to 6 billion requests and 50 TB to 600 TB, so customers can scale within the plan as their applications grow. Enterprises and mid-sized businesses whose baseline traffic previously made them ineligible for flat-rate plans can now adopt the Premium plan at a usage level that fits their application.
You select your Premium plan usage level in the CloudFront console, see your new monthly flat-rate price instantly, and can change your usage level at any time with no commitment required. All Premium plan features are included at every usage level. Flat-rate plans provide a single monthly price covering content delivery, AWS WAF and DDoS protection, bot management, Amazon Route 53 DNS, Amazon CloudWatch Logs ingestion, serverless edge compute, and Amazon S3 storage credits — with no overage charges.
Previously, the Amazon CloudFront Premium flat-rate plan supported a single usage allowance, and customers who outgrew it needed to contact us to discuss custom pricing options. Now, the Premium plan offers a range of self-service monthly usage levels ranging from 500 million to 6 billion requests and 50 TB to 600 TB, so customers can scale within the plan as their applications grow. Enterprises and mid-sized businesses whose baseline traffic previously made them ineligible for flat-rate plans can now adopt the Premium plan at a usage level that fits their application.
You select your Premium plan usage level in the CloudFront console, see your new monthly flat-rate price instantly, and can change your usage level at any time with no commitment required. All Premium plan features are included at every usage level. Flat-rate plans provide a single monthly price covering content delivery, AWS WAF and DDoS protection, bot management, Amazon Route 53 DNS, Amazon CloudWatch Logs ingestion, serverless edge compute, and Amazon S3 storage credits — with no overage charges.
To get started, visit the CloudFront console. To learn more, refer to the Launch Blog or Amazon CloudFront Developer Guide.
Amazon EventBridge Scheduler expands its AWS SDK integrations with 13 additional services and 619 new API actions across new and existing AWS services, including AWS Lambda Managed Instances. You can now schedule direct invocations of a broader set of AWS services without writing custom integration code.
EventBridge Scheduler is a serverless scheduler that allows you to create, run, and manage billions of scheduled events and tasks across more than 270 AWS services, without provisioning or managing the underlying infrastructure. With this expansion, you can now schedule a broader set of AWS API actions directly from Scheduler, including scaling Lambda managed instances up or down on a time-based schedule for precise control over capacity provisioning.
These enhancements are now generally available in all AWS Regions where AWS EventBridge Scheduler is available. Specific services and API actions are subject to the availability of the target service in the AWS Region. To learn more about AWS EventBridge Scheduler SDK integrations, visit the Developer Guide.
Amazon EventBridge Scheduler expands its AWS SDK integrations with 13 additional services and 619 new API actions across new and existing AWS services, including AWS Lambda Managed Instances. You can now schedule direct invocations of a broader set of AWS services without writing custom integration code. EventBridge Scheduler is a serverless scheduler that allows you to create, run, and manage billions of scheduled events and tasks across more than 270 AWS services, without provisioning or managing the underlying infrastructure. With this expansion, you can now schedule a broader set of AWS API actions directly from Scheduler, including scaling Lambda managed instances up or down on a time-based schedule for precise control over capacity provisioning. These enhancements are now generally available in all AWS Regions where AWS EventBridge Scheduler is available. Specific services and API actions are subject to the availability of the target service in the AWS Region. To learn more about AWS EventBridge Scheduler SDK integrations, visit the Developer Guide.
Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, including new capabilities for Lake Formation access controls and Apache Iceberg table properties configuration. Feature Store is a fully managed repository to store, share, and manage features for machine learning models. Data scientists can now use the modern, modular SDK v3 interfaces to manage feature groups with fine-grained access control and optimized offline storage.
Data scientists can use the SageMaker Python SDK v3 to manage feature groups with streamlined workflows and reduced boilerplate. With Lake Formation integration, data scientists can enforce column-level and row-level access control on offline store data through an opt-in setting at feature group creation. With Iceberg properties support, data scientists can configure additional table properties such as compaction and snapshot expiration directly through the SDK to optimize storage and query performance. These capabilities allow data scientists to govern access to feature data and optimize offline store performance from a single SDK without managing separate tools.
Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, including new capabilities for Lake Formation access controls and Apache Iceberg table properties configuration. Feature Store is a fully managed repository to store, share, and manage features for machine learning models. Data scientists can now use the modern, modular SDK v3 interfaces to manage feature groups with fine-grained access control and optimized offline storage. Data scientists can use the SageMaker Python SDK v3 to manage feature groups with streamlined workflows and reduced boilerplate. With Lake Formation integration, data scientists can enforce column-level and row-level access control on offline store data through an opt-in setting at feature group creation. With Iceberg properties support, data scientists can configure additional table properties such as compaction and snapshot expiration directly through the SDK to optimize storage and query performance. These capabilities allow data scientists to govern access to feature data and optimize offline store performance from a single SDK without managing separate tools. These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. To get started, install SageMaker Python SDK v3.8.0 or later. For more information, see Lake Formation access controls and Iceberg metadata management documentation.
Amazon Elastic Kubernetes Service (Amazon EKS) now supports Amazon Application Recovery Controller (ARC) zonal shift and zonal autoshift when using the open source Karpenter project for compute provisioning. ARC helps you manage and coordinate recovery for your applications across AWS Regions and Availability Zones (AZs). With this launch, you can better maintain Kubernetes application availability by automating the process of shifting in-cluster network traffic away from an impaired AZ.
Customers increasingly deploy highly available applications in Amazon EKS across multiple AZs to eliminate a single point of failure. With ARC zonal shift, you can temporarily mitigate an AZ impairment by redirecting in-cluster network traffic away from the impacted AZ. For a fully automated experience, authorize AWS to manage this on your behalf using ARC zonal autoshift, which includes practice runs to verify your cluster functions as expected with one less AZ. When a zonal shift is activated for your EKS cluster, Karpenter stops provisioning new capacity in the impaired AZ, halts voluntary disruptions such as consolidation and drift for nodes in that AZ, and prevents voluntary disruptions in healthy zones if they depend on scheduling pods to the impaired zone. Pods with strict scheduling requirements such as volume affinities that require the impaired zone will not trigger launch attempts. When the zonal shift expires or is canceled, Karpenter resumes normal operations.
This Karpenter feature works with both manual zonal shifts and zonal autoshifts. No custom ARC resources are required as Karpenter integrates directly with the existing EKS cluster ARC resource. To enable zonal shift support, set the ENABLE_ZONAL_SHIFT setting in your Karpenter settings. To learn more, visit the Karpenter documentation and the ARC zonal shift documentation.
Amazon Elastic Kubernetes Service (Amazon EKS) now supports Amazon Application Recovery Controller (ARC) zonal shift and zonal autoshift when using the open source Karpenter project for compute provisioning. ARC helps you manage and coordinate recovery for your applications across AWS Regions and Availability Zones (AZs). With this launch, you can better maintain Kubernetes application availability by automating the process of shifting in-cluster network traffic away from an impaired AZ. Customers increasingly deploy highly available applications in Amazon EKS across multiple AZs to eliminate a single point of failure. With ARC zonal shift, you can temporarily mitigate an AZ impairment by redirecting in-cluster network traffic away from the impacted AZ. For a fully automated experience, authorize AWS to manage this on your behalf using ARC zonal autoshift, which includes practice runs to verify your cluster functions as expected with one less AZ. When a zonal shift is activated for your EKS cluster, Karpenter stops provisioning new capacity in the impaired AZ, halts voluntary disruptions such as consolidation and drift for nodes in that AZ, and prevents voluntary disruptions in healthy zones if they depend on scheduling pods to the impaired zone. Pods with strict scheduling requirements such as volume affinities that require the impaired zone will not trigger launch attempts. When the zonal shift expires or is canceled, Karpenter resumes normal operations. This Karpenter feature works with both manual zonal shifts and zonal autoshifts. No custom ARC resources are required as Karpenter integrates directly with the existing EKS cluster ARC resource. To enable zonal shift support, set the ENABLE_ZONAL_SHIFT setting in your Karpenter settings. To learn more, visit the Karpenter documentation and the ARC zonal shift documentation.
