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Amazon EKS simplifies providing IAM permissions to EKS add-ons in AWS GovCloud (US) Regions

Amazon Elastic Kubernetes Service (EKS) now offers a direct integration between EKS add-ons and EKS Pod Identity in AWS GovCloud (US) Regions, streamlining the lifecycle management process for critical cluster operational software that needs to interact with AWS services outside the cluster.

EKS add-ons that enable integration with underlying AWS resources need IAM permissions to interact with AWS services. EKS Pod Identities simplify how Kubernetes applications obtain AWS IAM permissions. With today’s launch, you can directly manage EKS Pod Identities using EKS add-ons operations through the EKS console, CLI, API, eksctl, and IAC tools like AWS CloudFormation, simplifying usage of Pod Identities for EKS add-ons. This integration expands the selection of Pod Identity compatible EKS add-ons available for installation through the EKS console during cluster creation.

EKS add-ons integration with Pod Identities is generally available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To get started, see the EKS user guide.

 

​Amazon Elastic Kubernetes Service (EKS) now offers a direct integration between EKS add-ons and EKS Pod Identity in AWS GovCloud (US) Regions, streamlining the lifecycle management process for critical cluster operational software that needs to interact with AWS services outside the cluster. EKS add-ons that enable integration with underlying AWS resources need IAM permissions to interact with AWS services. EKS Pod Identities simplify how Kubernetes applications obtain AWS IAM permissions. With today’s launch, you can directly manage EKS Pod Identities using EKS add-ons operations through the EKS console, CLI, API, eksctl, and IAC tools like AWS CloudFormation, simplifying usage of Pod Identities for EKS add-ons. This integration expands the selection of Pod Identity compatible EKS add-ons available for installation through the EKS console during cluster creation. EKS add-ons integration with Pod Identities is generally available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To get started, see the EKS user guide.  

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Amazon EC2 and VPC now display related resources for security groups

Amazon Web Services (AWS) is announcing the general availability of the «Related resources» tab for security groups in the Amazon EC2 and VPC consoles. This new feature provides customers with a consolidated view of all resources that depend on a specific security group, eliminating the need to manually check multiple services before making configuration changes. Security groups act as virtual firewalls that control inbound and outbound traffic for AWS resources, and understanding their dependencies is critical for maintaining secure and stable infrastructure.

Previously, customers managing complex security group configurations had to navigate through multiple AWS services individually to identify dependencies before modifying or deleting security groups. This manual process required checking EC2 instances, Elastic Network Interfaces, ElastiCache clusters, RDS databases, and other services one by one, making it time-consuming and error-prone. The «Related resources» tab streamlines this workflow by displaying all dependent resources in a single location, enabling customers to quickly assess the impact of proposed changes and make informed decisions with confidence. This enhancement is beneficial for organizations managing large-scale deployments where security groups may be attached to dozens or hundreds of resources across different services.

This feature is now available in all AWS commercial regions at no additional cost.

To learn more about managing security groups and viewing the «Related resources» tab in the Amazon EC2 and VPC consoles, see the Amazon EC2 User Guide.

 

​Amazon Web Services (AWS) is announcing the general availability of the «Related resources» tab for security groups in the Amazon EC2 and VPC consoles. This new feature provides customers with a consolidated view of all resources that depend on a specific security group, eliminating the need to manually check multiple services before making configuration changes. Security groups act as virtual firewalls that control inbound and outbound traffic for AWS resources, and understanding their dependencies is critical for maintaining secure and stable infrastructure. Previously, customers managing complex security group configurations had to navigate through multiple AWS services individually to identify dependencies before modifying or deleting security groups. This manual process required checking EC2 instances, Elastic Network Interfaces, ElastiCache clusters, RDS databases, and other services one by one, making it time-consuming and error-prone. The «Related resources» tab streamlines this workflow by displaying all dependent resources in a single location, enabling customers to quickly assess the impact of proposed changes and make informed decisions with confidence. This enhancement is beneficial for organizations managing large-scale deployments where security groups may be attached to dozens or hundreds of resources across different services. This feature is now available in all AWS commercial regions at no additional cost. To learn more about managing security groups and viewing the «Related resources» tab in the Amazon EC2 and VPC consoles, see the Amazon EC2 User Guide.  

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Introducing Amazon EC2 C8id, M8id, and R8id instances

AWS is announcing the general availability of new Amazon EC2 C8id, M8id, and R8id instances powered by custom Intel Xeon 6 processors. These instances deliver up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation C6id, M6id, and R6id instances.

C8id, M8id, and R8id instances offer up to 384 vCPUs, 3TiB of memory, and 22.8TB of NVMe SSD storage, 3x more than previous generation instances. These instances deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics than previous sixth-generation instances. Additionally, these instances support Instance Bandwidth Configuration, allowing 25% flexible allocation between network and EBS bandwidth, allocating resources optimally for each workload.

C8id instances are ideal for compute-intensive workloads such as high-performance web servers, batch processing, distributed analytics, ad serving, video encoding, and gaming servers. M8id instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. R8id instances are ideal for memory-intensive workloads such as in-memory databases, real-time big data analytics, large in-memory caches, and scientific computing applications.

C8id, M8id and R8id instances are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). R8id instances are additionally available in Europe (Frankfurt). Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 instance type page.

 

​AWS is announcing the general availability of new Amazon EC2 C8id, M8id, and R8id instances powered by custom Intel Xeon 6 processors. These instances deliver up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation C6id, M6id, and R6id instances. C8id, M8id, and R8id instances offer up to 384 vCPUs, 3TiB of memory, and 22.8TB of NVMe SSD storage, 3x more than previous generation instances. These instances deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics than previous sixth-generation instances. Additionally, these instances support Instance Bandwidth Configuration, allowing 25% flexible allocation between network and EBS bandwidth, allocating resources optimally for each workload. C8id instances are ideal for compute-intensive workloads such as high-performance web servers, batch processing, distributed analytics, ad serving, video encoding, and gaming servers. M8id instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. R8id instances are ideal for memory-intensive workloads such as in-memory databases, real-time big data analytics, large in-memory caches, and scientific computing applications. C8id, M8id and R8id instances are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). R8id instances are additionally available in Europe (Frankfurt). Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 instance type page.  

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AWS Batch now supports unmanaged compute environments for Amazon EKS

AWS Batch now extends its job scheduling capabilities to unmanaged compute environments on Amazon EKS. With unmanaged EKS compute environments, you can leverage AWS Batch’s job orchestration while maintaining full control over your Kubernetes infrastructure for security, compliance, or operational requirements.

With this capability, you can create unmanaged compute environments through CreateComputeEnvironment API and AWS Batch console by selecting your existing EKS cluster and specifying a Kubernetes namespace, then associate your EKS nodes with the compute environment using kubectl labeling.

AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Unmanaged compute environments on Amazon EKS are available today in all AWS regions where AWS Batch is available. For more information, see the AWS Batch User Guide.

 

​AWS Batch now extends its job scheduling capabilities to unmanaged compute environments on Amazon EKS. With unmanaged EKS compute environments, you can leverage AWS Batch’s job orchestration while maintaining full control over your Kubernetes infrastructure for security, compliance, or operational requirements. With this capability, you can create unmanaged compute environments through CreateComputeEnvironment API and AWS Batch console by selecting your existing EKS cluster and specifying a Kubernetes namespace, then associate your EKS nodes with the compute environment using kubectl labeling. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Unmanaged compute environments on Amazon EKS are available today in all AWS regions where AWS Batch is available. For more information, see the AWS Batch User Guide.  

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Cartesia Sonic 3 text-to-speech model is now available on Amazon SageMaker JumpStart

Cartesia’s Sonic 3 model is now available in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. Sonic 3 is Cartesia’s latest state space model (SSM) for streaming text-to-speech (TTS), delivering high naturalness, accurate transcript following, and industry-leading latency with fine-grained control over volume, speed, and emotion.

Sonic 3 supports 42 languages and provides advanced controllability through API parameters and SSML tags for volume, speed, and emotion adjustments. The model includes natural laughter support, stable voices optimized for voice agents, and emotive voices for expressive characters. With sub-100ms latency, Sonic 3 enables real-time conversational AI that captures human speech nuances including emotions and tonal shifts.

With SageMaker JumpStart, customers can deploy Sonic 3 with just a few clicks to address their voice AI use cases. To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

 

​Cartesia’s Sonic 3 model is now available in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. Sonic 3 is Cartesia’s latest state space model (SSM) for streaming text-to-speech (TTS), delivering high naturalness, accurate transcript following, and industry-leading latency with fine-grained control over volume, speed, and emotion.
Sonic 3 supports 42 languages and provides advanced controllability through API parameters and SSML tags for volume, speed, and emotion adjustments. The model includes natural laughter support, stable voices optimized for voice agents, and emotive voices for expressive characters. With sub-100ms latency, Sonic 3 enables real-time conversational AI that captures human speech nuances including emotions and tonal shifts. With SageMaker JumpStart, customers can deploy Sonic 3 with just a few clicks to address their voice AI use cases. To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.  

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Amazon EC2 G7e instances now available in US West (Oregon) region

Starting today, Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in US West (Oregon) region. G7e instances offer up to 2.3x inference performance compared to G6e.

Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads.

You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia) and US East (Ohio). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.

To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.

 

​Starting today, Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in US West (Oregon) region. G7e instances offer up to 2.3x inference performance compared to G6e.
Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads.
You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia) and US East (Ohio). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.  

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Structured outputs now available in Amazon Bedrock

Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient.

Structured outputs helps with common production tasks such as extracting key fields and powering workflows that use APIs or tools, where small formatting errors can break downstream systems. By ensuring schema compliance, it reduces the need for custom validation logic and lowers operational overhead through fewer failed requests and retries—so you can confidently deploy AI applications that require predictable, machine-readable outputs. You can use structured outputs in two ways: define a JSON schema that describes the response format you want, or use strict tool definitions to ensure a model’s tool calls match your specifications.

Structured outputs is generally available for Anthropic Claude 4.5 models and select open-weight models across the Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream APIs in all commercial AWS Regions where Amazon Bedrock is supported. To learn more about structured outputs and the supported models, visit the Amazon Bedrock documentation.

 

​Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient. Structured outputs helps with common production tasks such as extracting key fields and powering workflows that use APIs or tools, where small formatting errors can break downstream systems. By ensuring schema compliance, it reduces the need for custom validation logic and lowers operational overhead through fewer failed requests and retries—so you can confidently deploy AI applications that require predictable, machine-readable outputs. You can use structured outputs in two ways: define a JSON schema that describes the response format you want, or use strict tool definitions to ensure a model’s tool calls match your specifications. Structured outputs is generally available for Anthropic Claude 4.5 models and select open-weight models across the Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream APIs in all commercial AWS Regions where Amazon Bedrock is supported. To learn more about structured outputs and the supported models, visit the Amazon Bedrock documentation.  

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Apache Spark lineage now available in Amazon SageMaker Unified Studio for IDC based domains

Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. Data Lineage provides you with the information you need to identify the root cause of complex issues and understand the impact of changes.

This feature supports lineage capture of schema and transformations of data assets and columns from Spark executions in EMR-EC2, EMR-Serverless, EMR-EKS, and AWS Glue. You can then explore this lineage visually as a graph in SageMaker Unified Studio or query it using APIs. You can also use lineage to compare transformations across Spark job’s history.

Spark lineage is available in all existing SageMaker Unified Studio regions. For detailed information on how to get started with lineage using these new features, refer to the documentation.

 

​Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. Data Lineage provides you with the information you need to identify the root cause of complex issues and understand the impact of changes. This feature supports lineage capture of schema and transformations of data assets and columns from Spark executions in EMR-EC2, EMR-Serverless, EMR-EKS, and AWS Glue. You can then explore this lineage visually as a graph in SageMaker Unified Studio or query it using APIs. You can also use lineage to compare transformations across Spark job’s history. Spark lineage is available in all existing SageMaker Unified Studio regions. For detailed information on how to get started with lineage using these new features, refer to the documentation.  

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Amazon ECS adds Network Load Balancer support for Linear and Canary deployments

Amazon Elastic Container Service (Amazon ECS) announces native support for linear and canary deployment strategies for ECS services using Network Load Balancers (NLB). Now, applications that commonly use NLB, such as those requiring TCP/UDP-based connections, low latency, long-lived connections, or static IP addresses, can take advantage of managed, incremental traffic shifting natively from ECS when rolling out updates.

With this launch, ECS customers using NLB can shift traffic in a controlled manner during deployments, such as moving traffic in increments or starting with a small percentage to validate changes before completing a rollout. These deployment strategies provide additional confidence during updates by allowing teams to observe application behavior at each traffic-shift step, and integrate with Amazon CloudWatch alarms to automatically stop or roll back deployments if issues are detected. This is especially valuable for workloads running behind an NLB, such as online gaming backends, financial transaction systems, and real-time messaging services.

To get started, select your NLB target groups, listener, and preferred deployment strategy in the ECS service configuration using the AWS Management Console, AWS CLI, or Infrastructure-as-Code tools. This can be enabled for both new and existing ECS services in all AWS commercial and AWS GovCloud (US) Regions. For more information, see the documentation for Amazon ECS linear deployments and Amazon ECS canary deployments.

 

​Amazon Elastic Container Service (Amazon ECS) announces native support for linear and canary deployment strategies for ECS services using Network Load Balancers (NLB). Now, applications that commonly use NLB, such as those requiring TCP/UDP-based connections, low latency, long-lived connections, or static IP addresses, can take advantage of managed, incremental traffic shifting natively from ECS when rolling out updates.
With this launch, ECS customers using NLB can shift traffic in a controlled manner during deployments, such as moving traffic in increments or starting with a small percentage to validate changes before completing a rollout. These deployment strategies provide additional confidence during updates by allowing teams to observe application behavior at each traffic-shift step, and integrate with Amazon CloudWatch alarms to automatically stop or roll back deployments if issues are detected. This is especially valuable for workloads running behind an NLB, such as online gaming backends, financial transaction systems, and real-time messaging services.
To get started, select your NLB target groups, listener, and preferred deployment strategy in the ECS service configuration using the AWS Management Console, AWS CLI, or Infrastructure-as-Code tools. This can be enabled for both new and existing ECS services in all AWS commercial and AWS GovCloud (US) Regions. For more information, see the documentation for Amazon ECS linear deployments and Amazon ECS canary deployments.  

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Abrir mercados digitales para que la IA pueda comprar —y negociar— por ustedes

Abrir mercados digitales para que la IA pueda comprar —y negociar— por ustedes

Un brillante cubo digital con un ícono de carrito de compra en un fondo compuesto por un tablero de circuitos

Por: Samantha Kubota, escritora de Microsoft.

Imaginen un mundo en el que tienen un asistente digital que puede hacer más que responder a sus preguntas en un chat. En este futuro, podrían enviar a su asistente a un mercado digital para que haga la compra, reserve un vuelo o incluso negocie los términos del contrato de alquiler de su apartamento.

Estos agentes impulsados por IA podrían interactuar con agentes de empresas en su nombre y defenderlos, todo ello sin que tengan que mover un dedo.

Ese futuro no es solo para el mundo de la ciencia ficción. En un artículo publicado el jueves en Communications of the ACM, investigadores de Microsoft afirman que este tipo de economía agéntica abierta es la forma más beneficiosa para que la IA avance, al maximizar las oportunidades tanto para empresas como para particulares.

¿Qué es una economía agente abierta?

En la actualidad, a medida que la economía agéntica toma forma, muchos de los agentes de IA con los que interactuamos existen en lo que se llama un «jardín amurallado». Si necesitan ayuda para reprogramar un vuelo, por ejemplo, podrían hablar con el agente de IA en la web de la aerolínea. Pero en una economía agéntica abierta, dicen los investigadores, habría una «red de agentes» que formaría un ecosistema descentralizado donde los agentes de IA podrían interactuar con libertad entre sí, sin estar confinados al «jardín amurallado» de un solo sitio web.

Comparan la economía agéntica abierta con la promesa de la World Wide Web, en la que cualquier agente podría realizar transacciones con cualquier otro. Los agentes asistentes desempeñarían un papel similar al de los navegadores web, y los agentes de servicio similares a los sitios web.

Uno de los autores del artículo, el investigador de Microsoft David Rothschild, dice que él y sus colegas eligieron escribir sobre este tema porque temen que los «jardines amurallados» aparezcan en algunas plataformas importantes y puedan frenar la innovación. Rothschild señala que algunas empresas ya han construido grandes plataformas en las que querrán mantener a los usuarios existentes.

«Hay muy pocas empresas que hayan capturado todo nuestro tiempo digital», explica. «Tienen un porcentaje enorme de nuestra atención, y harán todo lo posible para mantenerlos aislados en sus plataformas.»

Rothschild dice que iniciar ahora la discusión sobre una economía agente abierta es fundamental.

«Si no, el impulso y la facilidad nos van a empujar a esa versión de jardines amurallados», dice. «Y eso significaría menos bienestar general y menos oportunidades para la sociedad.»

¿Por qué tener una economía agéntica abierta?

En el artículo, investigadores de Microsoft afirman que permitir que muchos agentes de IA diferentes operen en un mercado abierto es el mejor camino para seguir. En este contexto, los agentes ayudan a que los mercados funcionen de forma más fluida, facilitan que las personas cambien entre servicios y dan acceso a más personas a herramientas y servicios digitales de forma descentralizada.

El coautor y director de programa Matt Vogel afirma que cree que una economía agéntica abierta será beneficiosa tanto para las personas como para las empresas.

«Los consumidores pueden encontrar el negocio que mejor se adapte a sus necesidades y cambiar con facilidad entre ellos», dice Vogel. «No están atrapados ni encerrados en uno de ellos.»

Y las empresas pasarán de intentar presentar su producto a través de la publicidad a mejorarlo, dice Rothchild.

«Prevemos un movimiento desde lo que llamaremos la ‘economía de la atención’ hacia la ‘economía de preferencias’», explica. «La esperanza es que las marcas continúen con el gasto de dinero para hacerlas más exitosas, pero lo hacen de una manera que en verdad mejora el producto y mejora nuestra comprensión del producto, en lugar de tan solo ponerse delante de nosotros.»

Cómo llegar a una economía agéntica abierta

Los investigadores afirman que para formar una economía agéntica abierta serán necesarios varios desarrollos tecnológicos y estructurales clave. Primero, la adopción generalizada de agentes asistentes y de servicio, así como la comunicación programática entre agentes. Una vez que las personas y las empresas adopten el uso de agentes de IA como sus representantes, estos deben ser capaces de comunicarse entre sí, de forma no guionizada.

Esto se haría en un mercado digital neutral, similar a cómo se creó la Estación Espacial Internacional en cooperación con empresas y gobiernos, en lugar de en una plataforma única propiedad de una sola empresa.

Para que los agentes puedan hacer su trabajo, los investigadores afirman que sería necesario establecer marcos y protocolos estandarizados para que los agentes puedan descubrirse entre sí y realizar interacciones seguras.

Aunque los consumidores adoptarían y formarían a sus agentes asistentes personales de IA, los investigadores afirman que varios grupos deben participar en la construcción y gestión de la economía agente abierta: empresas tecnológicas, organismos de normalización, empresas/proveedores de servicios, gobiernos y organismos reguladores. Los investigadores sostienen que esto ayudaría a garantizar que los nuevos mercados se mantengan abiertos, competitivos y seguros.

«Para garantizar un futuro seguro y próspero con un mercado abierto, es necesario desarrollar más tecnología que crea un espacio muy cerrado en el que los agentes estén muy limitados, pero que también añada cierta seguridad y cierto control», dice Rothschild. «Este es un intercambio que creemos importante afrontar de manera abierta — una vía abierta de investigación y una vía abierta de desarrollo. Creemos que merece la pena luchar por un futuro más abierto.»

Descubran más en «La economía agéntica.» Para más información sobre trabajos relacionados, consulten la entrada del blog Microsoft Research en Magentic Marketplace, un entorno de simulación de código abierto para estudiar mercados agénticos.

Imagen principal cortesía de da-kuk/Getty Images.

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