• Categoría: Noticias de Microsoft

marzo 6, 2026

Explorar qué significa la IA para la educación y la próxima generación

Por: Paul Nyhan, escritor de Microsoft.

A medida que la IA transforma la manera en que trabajamos, vivimos y aprendemos, la educación superior es más que otro actor: debe liderar el camino.

La educación superior debe encontrar un equilibrio mientras prepara a la próxima generación para un mundo que se transforma con la IA. Debe enseñar a los estudiantes nuevas y necesarias habilidades de IA, manteniéndose fiel a la misión que la ha guiado durante siglos: cultivar el pensamiento crítico, la comunicación y los valores humanos.

Y tiene que hacer todo esto más rápido.

Estos son mensajes centrales de un nuevo libro, «Grados de cambio: qué significa la IA para la educación y la próxima generación», de Juan M. Lavista Ferres, quien dirige el Instituto de la Economía de la IA de Microsoft y es director del AI for Good Lab de la compañía.

«Primero, debemos dotar a las personas de las habilidades necesarias para usar la IA de forma eficaz — un paso fundamental para garantizar su difusión generalizada», escribe Lavista Ferres. «Segundo, y de manera más profunda, debemos entender cómo la propia IA transforma la educación en su conjunto.»

Aquí hay cuatro formas en que Lavista Ferres y los colaboradores del libro —decenas de autores y académicos— dicen que la educación superior puede y debe liderar en la era de la IA:

La alfabetización en IA no es una optativa; ahora es un requisito básico

La IA se vuelve, muy rápido, omnipresente, para ayudar a la gente con todo, desde pagar facturas hasta viajar en autobuses públicos. Eso significa que las universidades y colegios deben enseñar a los estudiantes habilidades técnicas de IA porque la alfabetización en IA, y en última instancia la fluidez en IA, serán necesarias tanto para el trabajo como para la vida personal. El mercado laboral ya lo ha comenzado a dejar claro. Los primeros datos muestran que la competencia en IA ya tiene un aumento salarial del 23%.

Este aprendizaje no puede ocurrir en el vacío. La alfabetización en IA debe integrarse en la enseñanza en instituciones y planes de estudio que se mantengan fundamentados en las competencias de la educación superior, incluidos el razonamiento ético, la descomposición y análisis de problemas, la comunicación y la colaboración. Aquí, los instructores están en primera línea de la economía de la IA.

La educación superior debe adaptarse con rapidez y mantenerse fiel a su misión

A medida que la IA acelera las tareas rutinarias y transforma la manera en que las personas resuelven problemas, la agencia humana debe permanecer en el centro de la educación superior. Los educadores deben integrar la IA generativa de forma reflexiva en cursos, formaciones, títulos e infraestructuras para apoyar este aprendizaje sin erosionar la misión de larga trayectoria de las instituciones.

Por igual importante, las escuelas deben avanzar más rápido, incluso a través de la integración de habilidades en IA y alfabetización ética en los planes de estudio, para hacer que la educación integral en IA sea accesible para todos los estudiantes.

La alfabetización es bidireccional

Los desarrolladores deben comprender el impacto de la IA en la sociedad y las preocupaciones éticas, mientras que educadores, responsables políticos y el público deben aprender cómo funciona la IA. El éxito depende de esta alfabetización dual, en la que cada grupo entiende el trabajo del otro.

La recompensa será enorme y el precio por la inacción será muy alto. Los trabajadores que externalizan el trabajo rutinario a la IA para poder centrarse en la supervisión, el trabajo creativo y los juicios complejos liderarán la innovación responsable. Pero sin alfabetización dual, la regulación y la adopción se retrasarán y dificultarán el progreso.

Los profesores y decanos no pueden hacer esto solos

El éxito en la era de la IA depende de que las universidades, la industria y los responsables políticos trabajen juntos. En medio de este trabajo en equipo, el profesorado y las instituciones deben desempeñar un papel principal para apoyar a los estudiantes que están a la vanguardia de la era de la IA. Pueden lograrlo a través de equilibrar la necesidad de agilidad académica con la misión de preparar de manera amplia a los graduados para el presente y el futuro.

Esto significa que las instituciones de educación superior deben establecer estándares claros para las credenciales técnicas de IA que sean reconocidos por los empleadores, proporcionar el apoyo financiero y estructural necesario para integrar las habilidades y la preparación en IA en los títulos y cursos, y garantizar la inclusión y accesibilidad para estudiantes diversos.

«Grados de cambio: Lo que significa la IA para la educación y la próxima generación» ya está disponible en Wiley y librerías online.

Etiquetas:

• Educación
• IA

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Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Ireland) AWS region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable 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.

I8ge instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve 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. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads.

I8ge instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 180 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications.

To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.

 

​Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Ireland) AWS region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable 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. I8ge instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve 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. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads. I8ge instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 180 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications. To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.  

Today, AWS announces expanded coverage for Database Savings Plans, with support for Amazon OpenSearch Service and Amazon Neptune Analytics.

With Database Savings Plans, you can save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment. Database Savings Plans automatically applies to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change from m7i.large.search to c8g.2xlarge.search within OpenSearch Service, or scale Neptune Analytics workloads while continuing to benefit from the discounted pricing.

Database Savings Plans for Amazon OpenSearch Service and Amazon Neptune Analytics is available starting today in all AWS Regions, except China Regions.

You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.

 

​Today, AWS announces expanded coverage for Database Savings Plans, with support for Amazon OpenSearch Service and Amazon Neptune Analytics. With Database Savings Plans, you can save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment. Database Savings Plans automatically applies to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change from m7i.large.search to c8g.2xlarge.search within OpenSearch Service, or scale Neptune Analytics workloads while continuing to benefit from the discounted pricing. Database Savings Plans for Amazon OpenSearch Service and Amazon Neptune Analytics is available starting today in all AWS Regions, except China Regions. You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.  

Multi-party approval (MPA) now supports MPA administrators running test approvals to confirm that their approval team is set up correctly and that approvers are active and reachable. With this new capability, customers ensure their approval teams do not become unresponsive due to natural attrition, incorrect approver selection, or reduced engagement. MPA administrators and security teams can now proactively assess their approval configurations before relying on them for sensitive operations.

The baseline feature enables proactive team health management by allowing manual initiation of test approval sessions through the AWS Organizations console. Customers can verify approver availability, identify inactive team members, and maintain compliance with internal governance requirements. Key use cases include regular team responsiveness verification, recommended every 90 days by AWS using the MPA Console, onboarding validation for new approval configurations, and operation health checks to ensure approval workflows function effectively when needed.

This feature is available in all AWS commercial regions. To learn more about implementing baseline testing for your multi-party approval workflows, visit the Multi-party approval documentation.    

 

​Multi-party approval (MPA) now supports MPA administrators running test approvals to confirm that their approval team is set up correctly and that approvers are active and reachable. With this new capability, customers ensure their approval teams do not become unresponsive due to natural attrition, incorrect approver selection, or reduced engagement. MPA administrators and security teams can now proactively assess their approval configurations before relying on them for sensitive operations. The baseline feature enables proactive team health management by allowing manual initiation of test approval sessions through the AWS Organizations console. Customers can verify approver availability, identify inactive team members, and maintain compliance with internal governance requirements. Key use cases include regular team responsiveness verification, recommended every 90 days by AWS using the MPA Console, onboarding validation for new approval configurations, and operation health checks to ensure approval workflows function effectively when needed. This feature is available in all AWS commercial regions. To learn more about implementing baseline testing for your multi-party approval workflows, visit the Multi-party approval documentation.      

AWS Elastic Beanstalk now offers AI-powered environment analysis to help you quickly identify root causes and get recommended solutions for environment health issues. When your environment experiences problems, Elastic Beanstalk collects recent events, instance health, and logs from your environment and sends them to Amazon Bedrock for analysis. This feature is designed for developers and operations teams who need to diagnose and resolve environment issues faster without manually reviewing logs and events.

You can request an AI analysis directly from the Elastic Beanstalk console using the AI Analysis button when your environment’s health status is Warning, Degraded, or Severe. You can also use the AWS CLI with the RequestEnvironmentInfo and RetrieveEnvironmentInfo API operations. The analysis provides step-by-step troubleshooting recommendations tailored to your environment’s current state, helping you reduce mean time to resolution.

AI-powered environment analysis is available in all AWS Regions where both AWS Elastic Beanstalk and Amazon Bedrock are available. 

For more information about the AI-powered environment analysis and for a full list of supported platform versions, see the Elastic Beanstalk developer guide. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.

 

​AWS Elastic Beanstalk now offers AI-powered environment analysis to help you quickly identify root causes and get recommended solutions for environment health issues. When your environment experiences problems, Elastic Beanstalk collects recent events, instance health, and logs from your environment and sends them to Amazon Bedrock for analysis. This feature is designed for developers and operations teams who need to diagnose and resolve environment issues faster without manually reviewing logs and events. You can request an AI analysis directly from the Elastic Beanstalk console using the AI Analysis button when your environment’s health status is Warning, Degraded, or Severe. You can also use the AWS CLI with the RequestEnvironmentInfo and RetrieveEnvironmentInfo API operations. The analysis provides step-by-step troubleshooting recommendations tailored to your environment’s current state, helping you reduce mean time to resolution.
AI-powered environment analysis is available in all AWS Regions where both AWS Elastic Beanstalk and Amazon Bedrock are available. 
For more information about the AI-powered environment analysis and for a full list of supported platform versions, see the Elastic Beanstalk developer guide. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.  

Today, AWS Shield announces findings from network security director, currently in preview, are now available in AWS Security Hub. AWS Shield network security director identifies missing or misconfigured network security services like AWS WAF, VPC security groups, and VPC network access control lists (ACLs) in your AWS Organization and provides remediation recommendations.

Network security director findings now also appear in the Inventory section of the Security Hub console. With network security director, you can continuously analyze your network across accounts or organizational units in your AWS Organization, and receive findings highlighting missing or misconfigured network security services per AWS best practices. The severity of each finding is determined based on a combination of the misconfiguration identified and the network topology of the resource the finding is associated with.

To learn more, visit the overview page.

 

​Today, AWS Shield announces findings from network security director, currently in preview, are now available in AWS Security Hub. AWS Shield network security director identifies missing or misconfigured network security services like AWS WAF, VPC security groups, and VPC network access control lists (ACLs) in your AWS Organization and provides remediation recommendations. Network security director findings now also appear in the Inventory section of the Security Hub console. With network security director, you can continuously analyze your network across accounts or organizational units in your AWS Organization, and receive findings highlighting missing or misconfigured network security services per AWS best practices. The severity of each finding is determined based on a combination of the misconfiguration identified and the network topology of the resource the finding is associated with. To learn more, visit the overview page.  

Amazon OpenSearch Service now offers a Capacity Optimized option for blue/green deployments, ensuring domain updates can complete even when available instance capacity is less than required. Updates are performed in incremental batches, reducing the number of additional instances needed during the process.

Amazon OpenSearch Service uses a blue/green deployment process when updating domains — creating an idle copy of the original environment, applying updates, and routing traffic to the new environment once complete. This minimizes downtime and preserves the original environment as a fallback. Until now, blue/green deployments required 100% instance capacity upfront. For example, for a cluster with 100 data nodes, another 100 nodes were needed to proceed. If sufficient capacity was unavailable, customers had to wait and retry later.

Now, customers can choose between two deployment strategies. The default Full Swap option maintains current behavior, requiring full capacity upfront for the fastest deployment. The new Capacity Optimized option attempts a full capacity deployment first, but automatically falls back to batch deployment if capacity is insufficient. OpenSearch Service determines the appropriate batch size based on cluster size and available instances. Because updates are applied in batches, this option may take longer than a full-swap deployment. Customers can select their preferred option in the deployment configuration settings via the OpenSearch Service console or API. We recommend choosing the Capacity Optimized deployment option for clusters with 30 or more nodes.

The Capacity Optimized option is available for all OpenSearch and Elasticsearch versions, across all AWS Commercial Regions where OpenSearch Service is available. See here for a full listing of our Regions. To learn more, visit the documentation page.

 

​Amazon OpenSearch Service now offers a Capacity Optimized option for blue/green deployments, ensuring domain updates can complete even when available instance capacity is less than required. Updates are performed in incremental batches, reducing the number of additional instances needed during the process. Amazon OpenSearch Service uses a blue/green deployment process when updating domains — creating an idle copy of the original environment, applying updates, and routing traffic to the new environment once complete. This minimizes downtime and preserves the original environment as a fallback. Until now, blue/green deployments required 100% instance capacity upfront. For example, for a cluster with 100 data nodes, another 100 nodes were needed to proceed. If sufficient capacity was unavailable, customers had to wait and retry later. Now, customers can choose between two deployment strategies. The default Full Swap option maintains current behavior, requiring full capacity upfront for the fastest deployment. The new Capacity Optimized option attempts a full capacity deployment first, but automatically falls back to batch deployment if capacity is insufficient. OpenSearch Service determines the appropriate batch size based on cluster size and available instances. Because updates are applied in batches, this option may take longer than a full-swap deployment. Customers can select their preferred option in the deployment configuration settings via the OpenSearch Service console or API. We recommend choosing the Capacity Optimized deployment option for clusters with 30 or more nodes. The Capacity Optimized option is available for all OpenSearch and Elasticsearch versions, across all AWS Commercial Regions where OpenSearch Service is available. See here for a full listing of our Regions. To learn more, visit the documentation page.  

Amazon Connect Health is now generally available, bringing purpose-built agentic AI to healthcare organizations to streamline patient engagement and point-of-care workflows. Amazon Connect Health delivers five AI agents designed to reduce administrative burden across the care continuum — enabling patients faster access to care and freeing clinicians from paperwork and administrative burden to focus on what matters most: their patients. These agents are ready to deploy within existing patient, clinician, and healthcare workflows — such as patient access centers (i.e., contact centers), Electronic Health Records (EHR) applications, and telehealth solutions — in days, not months. All the features follow responsible AI best practices, implement safety guardrails, are HIPAA-eligible, and deliver the same security and reliability standards as any AWS service.

Agents available at launch:

• Patient verification (GA) – Confirms patient identity in real time against EHR records with appointment lookup, reducing inbound call-handling time.

• Appointment management (Preview) – Books appointments via natural language voice interaction, 24/7, with real-time insurance eligibility checks, enabling after-hours scheduling, and relieving burden on human staff. 

• Patient insights (Preview) – Surfaces relevant patient history and clinical context before the visit, so clinicians walk in prepared. Reduces the time clinicians spend piecing together information before a patient’s visit.

• Ambient documentation (GA) – Captures patient-clinician conversations during the visit and generates clinical notes in real time.

• Medical coding (Preview) – Automatically generates ICD-10 and CPT codes from clinical notes post-visit, with full audit trails.

Amazon Connect Health patient engagement capabilities are natively integrated with Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale. Clinical and administrative staff can configure and customize these AI capabilities in minutes using the Amazon Connect Health application, enabling rapid testing and seamless deployment into contact center workflows. The point-of-care capabilities — ambient listening, patient insight, and medical coding — are available via Amazon Connect Health unified SDK (SDK documentation), enabling developers to integrate the features directly into existing EHR and clinician-facing applications. 

 

Amazon Connect Health is available in US East (N. Virginia) and US West (Oregon). To get started, visit the Amazon Connect Health product page. For technical details, see the Amazon Connect Health documentation

 

​Amazon Connect Health is now generally available, bringing purpose-built agentic AI to healthcare organizations to streamline patient engagement and point-of-care workflows. Amazon Connect Health delivers five AI agents designed to reduce administrative burden across the care continuum — enabling patients faster access to care and freeing clinicians from paperwork and administrative burden to focus on what matters most: their patients. These agents are ready to deploy within existing patient, clinician, and healthcare workflows — such as patient access centers (i.e., contact centers), Electronic Health Records (EHR) applications, and telehealth solutions — in days, not months. All the features follow responsible AI best practices, implement safety guardrails, are HIPAA-eligible, and deliver the same security and reliability standards as any AWS service.
Agents available at launch:

Patient verification (GA) – Confirms patient identity in real time against EHR records with appointment lookup, reducing inbound call-handling time.

Appointment management (Preview) – Books appointments via natural language voice interaction, 24/7, with real-time insurance eligibility checks, enabling after-hours scheduling, and relieving burden on human staff. 

Patient insights (Preview) – Surfaces relevant patient history and clinical context before the visit, so clinicians walk in prepared. Reduces the time clinicians spend piecing together information before a patient’s visit.

Ambient documentation (GA) – Captures patient-clinician conversations during the visit and generates clinical notes in real time.

Medical coding (Preview) – Automatically generates ICD-10 and CPT codes from clinical notes post-visit, with full audit trails.

Amazon Connect Health patient engagement capabilities are natively integrated with Amazon Connect, a complete AI-powered contact center solution delivering personalized customer experiences at scale. Clinical and administrative staff can configure and customize these AI capabilities in minutes using the Amazon Connect Health application, enabling rapid testing and seamless deployment into contact center workflows. The point-of-care capabilities — ambient listening, patient insight, and medical coding — are available via Amazon Connect Health unified SDK (SDK documentation), enabling developers to integrate the features directly into existing EHR and clinician-facing applications. 
 
Amazon Connect Health is available in US East (N. Virginia) and US West (Oregon). To get started, visit the Amazon Connect Health product page. For technical details, see the Amazon Connect Health documentation  

Starting today, healthcare organizations can now transform legacy clinical documents into queryable FHIR resources in AWS HealthLake in days instead of months, unlocking use cases such as longitudinal patient record generation, population health analytics, and clinical data exchange. AWS HealthLake data transformation agent (preview) is an AI-powered capability that converts Consolidated Clinical Document Architecture (CCDA) files into Fast Health Interoperability Resources Release 4 (FHIR R4)-compliant resources without requiring specialized FHIR expertise, through an integrated experience that combines real-time conversion testing, AI-assisted template customization, and scalable bulk import.

The data transformation agent includes ready-to-use templates for CCDA 2.1 to FHIR R4 data conversion. Developers can submit individual CCDA files through a synchronous conversion API or console workflow and receive transformed FHIR Bundles in seconds. They can preview results, interactively validate conversion quality, and sign off on templates before production use. An enhanced import workflow automatically detects uploaded CCDA files, applies the active template, matches and reconciles patients based on identifiers, and ingests the resulting FHIR resources into the target AWS HealthLake datastore with detailed logs. All capabilities are available both on the AWS console and programmatically via API for seamless integration into existing workflows.

When default templates need adjustment, the data transformation agent offers an AI-powered experience to customize them directly in the console. Users can describe changes such as «skip medications with status entered-in-error» or «map procedure dates to performedDateTime instead of performedPeriod» in natural language, and the AI agent modifies the underlying template automatically. Manual curation is also available for power users who wish to make targeted template edits. Users can then immediately test against sample files, iterate conversationally, and publish once satisfied.

AWS HealthLake is available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Europe West (London), Europe (Ireland), and Asia Pacific SouthEast (Sydney) Regions. Visit the AWS Region Table to see all the regions.

To learn more, see the AWS HealthLake product page.

 

​Starting today, healthcare organizations can now transform legacy clinical documents into queryable FHIR resources in AWS HealthLake in days instead of months, unlocking use cases such as longitudinal patient record generation, population health analytics, and clinical data exchange. AWS HealthLake data transformation agent (preview) is an AI-powered capability that converts Consolidated Clinical Document Architecture (CCDA) files into Fast Health Interoperability Resources Release 4 (FHIR R4)-compliant resources without requiring specialized FHIR expertise, through an integrated experience that combines real-time conversion testing, AI-assisted template customization, and scalable bulk import. The data transformation agent includes ready-to-use templates for CCDA 2.1 to FHIR R4 data conversion. Developers can submit individual CCDA files through a synchronous conversion API or console workflow and receive transformed FHIR Bundles in seconds. They can preview results, interactively validate conversion quality, and sign off on templates before production use. An enhanced import workflow automatically detects uploaded CCDA files, applies the active template, matches and reconciles patients based on identifiers, and ingests the resulting FHIR resources into the target AWS HealthLake datastore with detailed logs. All capabilities are available both on the AWS console and programmatically via API for seamless integration into existing workflows. When default templates need adjustment, the data transformation agent offers an AI-powered experience to customize them directly in the console. Users can describe changes such as «skip medications with status entered-in-error» or «map procedure dates to performedDateTime instead of performedPeriod» in natural language, and the AI agent modifies the underlying template automatically. Manual curation is also available for power users who wish to make targeted template edits. Users can then immediately test against sample files, iterate conversationally, and publish once satisfied. AWS HealthLake is available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Europe West (London), Europe (Ireland), and Asia Pacific SouthEast (Sydney) Regions. Visit the AWS Region Table to see all the regions. To learn more, see the AWS HealthLake product page.  

Today, AWS announces the Lambda durable functions Kiro power, bringing Lambda durable function development expertise to agentic AI development in Kiro. With this power, you can build resilient, long-running multi-step applications and AI workflows faster with AI agent-assisted development directly in your local development environment.

When you work with durable functions, the AI agent dynamically loads relevant guidance and development expertise. This includes replay model best practices, step and wait operations, concurrent execution with map and parallel patterns, error handling with retry strategies and compensating transactions, testing patterns, and deployment with AWS CloudFormation, AWS Cloud Development Kit (AWS CDK), and AWS Serverless Application Model (AWS SAM). With this guidance, you can go from idea to a working durable function quickly, whether you are building order processing pipelines, AI agent orchestration with human-in-the-loop approvals, or payment coordination workflows.

The Lambda durable functions power is available today with one-click installation from the Kiro IDE and the Kiro powers page. Explore the power on GitHub. To get started with Lambda durable functions, see the developer guide.

 

​Today, AWS announces the Lambda durable functions Kiro power, bringing Lambda durable function development expertise to agentic AI development in Kiro. With this power, you can build resilient, long-running multi-step applications and AI workflows faster with AI agent-assisted development directly in your local development environment.
When you work with durable functions, the AI agent dynamically loads relevant guidance and development expertise. This includes replay model best practices, step and wait operations, concurrent execution with map and parallel patterns, error handling with retry strategies and compensating transactions, testing patterns, and deployment with AWS CloudFormation, AWS Cloud Development Kit (AWS CDK), and AWS Serverless Application Model (AWS SAM). With this guidance, you can go from idea to a working durable function quickly, whether you are building order processing pipelines, AI agent orchestration with human-in-the-loop approvals, or payment coordination workflows.
The Lambda durable functions power is available today with one-click installation from the Kiro IDE and the Kiro powers page. Explore the power on GitHub. To get started with Lambda durable functions, see the developer guide.