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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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​The post Abrir mercados digitales para que la IA pueda comprar —y negociar— por ustedes appeared first on Source LATAM.  

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Amazon DynamoDB global tables now support replication across multiple AWS accounts

Amazon DynamoDB global tables now support replication across multiple AWS accounts. DynamoDB global tables is a fully managed, serverless, multi-Region, and multi-active database used by tens of thousands of customers to power business-critical applications. With this new capability, you can replicate tables across AWS accounts and Regions to improve resiliency, isolate workloads at the account level, and apply distinct security and governance controls.

For multi-account global tables, DynamoDB automatically replicates tables across AWS accounts and Regions. This capability allows you to strengthen fault tolerance and helps ensure applications remain highly available even during account-level disruptions, while allowing customers to align data placement with organizational and security requirements. Multi-account global tables are ideal for customers that adopt multi-account strategies or use AWS Organizations to improve security isolation, enforce data perimeter guardrails, implement disaster recovery (DR), or separate workloads by business unit.

Multi-account global tables is available in all AWS Regions and is billed according to existing global tables pricing.

To get started, see the DynamoDB global tables documentation, and visit the AWS developer guide to learn more about the benefits of using a multi-account strategy for your AWS environment.

 

​Amazon DynamoDB global tables now support replication across multiple AWS accounts. DynamoDB global tables is a fully managed, serverless, multi-Region, and multi-active database used by tens of thousands of customers to power business-critical applications. With this new capability, you can replicate tables across AWS accounts and Regions to improve resiliency, isolate workloads at the account level, and apply distinct security and governance controls. For multi-account global tables, DynamoDB automatically replicates tables across AWS accounts and Regions. This capability allows you to strengthen fault tolerance and helps ensure applications remain highly available even during account-level disruptions, while allowing customers to align data placement with organizational and security requirements. Multi-account global tables are ideal for customers that adopt multi-account strategies or use AWS Organizations to improve security isolation, enforce data perimeter guardrails, implement disaster recovery (DR), or separate workloads by business unit. Multi-account global tables is available in all AWS Regions and is billed according to existing global tables pricing. To get started, see the DynamoDB global tables documentation, and visit the AWS developer guide to learn more about the benefits of using a multi-account strategy for your AWS environment.  

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AWS Marketplace introduces localized billing for Professional Services from AWS EMEA

AWS Marketplace now offers a more localized experience for Europe, Middle East, and Africa (EMEA) customers purchasing Professional Service solutions via AWS EMEA Marketplace Operator.

Customers can now procure Professional Services using localized payment methods and receive invoices from AWS EMEA. This removes previous procurement barriers caused by complex payment remittance processes between different AWS entities, which made it difficult for EMEA customers to purchase Professional Services through AWS Marketplace.

Key benefits include support for SEPA (Single Euro Payment Area) payment methods and invoicing consistency from the same AWS entity covering all AWS Marketplace purchases via AWS EMEA Marketplace Operator. This capability is ideal for EMEA customers purchasing consulting, implementation, or managed services through AWS Marketplace. It also benefits organizations that prefer local payment methods such as SEPA direct debit, want to consolidate AWS and Marketplace billing, or are seeking a simpler procurement experience for Professional Services.

This capability is available for EMEA customers who purchase professional services solutions in AWS Marketplace, with AWS EMEA as the Marketplace Operator. To learn more about purchasing Professional Services products in AWS Marketplace and receive invoices issued by AWS EMEA, visit the AWS Marketplace Buyer Guide and AWS EMEA Marketplace FAQs. For more information on how to add a bank account for SEPA, see Managing Your SEPA Direct Debit Payment Method in the AWS Billing and Cost Management user guide. 

 

​AWS Marketplace now offers a more localized experience for Europe, Middle East, and Africa (EMEA) customers purchasing Professional Service solutions via AWS EMEA Marketplace Operator. Customers can now procure Professional Services using localized payment methods and receive invoices from AWS EMEA. This removes previous procurement barriers caused by complex payment remittance processes between different AWS entities, which made it difficult for EMEA customers to purchase Professional Services through AWS Marketplace. Key benefits include support for SEPA (Single Euro Payment Area) payment methods and invoicing consistency from the same AWS entity covering all AWS Marketplace purchases via AWS EMEA Marketplace Operator. This capability is ideal for EMEA customers purchasing consulting, implementation, or managed services through AWS Marketplace. It also benefits organizations that prefer local payment methods such as SEPA direct debit, want to consolidate AWS and Marketplace billing, or are seeking a simpler procurement experience for Professional Services. This capability is available for EMEA customers who purchase professional services solutions in AWS Marketplace, with AWS EMEA as the Marketplace Operator. To learn more about purchasing Professional Services products in AWS Marketplace and receive invoices issued by AWS EMEA, visit the AWS Marketplace Buyer Guide and AWS EMEA Marketplace FAQs. For more information on how to add a bank account for SEPA, see Managing Your SEPA Direct Debit Payment Method in the AWS Billing and Cost Management user guide.   

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Amazon RDS now provides an enhanced console experience to connect to a database

Amazon RDS now provides an enhanced console experience that consolidates and provides all relevant information needed to connect to a database in one place, making it easier to connect to your RDS databases.

The new console experience provides ready-made code snippets for Java, Python, Node.js and other programming languages as well as tools like the psql command line utility. These code snippets are automatically adjusted based on your database’s authentication settings. For example, if your cluster uses IAM authentication, the generated code snippets will use token-based authentication to connect to the database. The console experience also includes integrated CloudShell access, offering the ability to connect to your databases directly from within the RDS console.

This feature is available for Amazon Aurora PostgreSQL, Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, Amazon RDS for MariaDB database engines across all commercial AWS Regions.

Get started with the new console experience for database connectivity through the Amazon RDS Console. To learn more, see the Amazon RDS and Aurora user guide

 

​Amazon RDS now provides an enhanced console experience that consolidates and provides all relevant information needed to connect to a database in one place, making it easier to connect to your RDS databases. The new console experience provides ready-made code snippets for Java, Python, Node.js and other programming languages as well as tools like the psql command line utility. These code snippets are automatically adjusted based on your database’s authentication settings. For example, if your cluster uses IAM authentication, the generated code snippets will use token-based authentication to connect to the database. The console experience also includes integrated CloudShell access, offering the ability to connect to your databases directly from within the RDS console. This feature is available for Amazon Aurora PostgreSQL, Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, Amazon RDS for MariaDB database engines across all commercial AWS Regions. Get started with the new console experience for database connectivity through the Amazon RDS Console. To learn more, see the Amazon RDS and Aurora user guide  

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Amazon Aurora DSQL now supports indexes on the NUMERIC data type

Amazon Aurora DSQL now supports index creation on NUMERIC data types. With this enhancement, you can now use NUMERIC columns in both primary keys and secondary indexes, which can improve query performance for workloads that rely on high-precision values such as currency amounts, measurements and statistical data.

 

​Amazon Aurora DSQL now supports index creation on NUMERIC data types. With this enhancement, you can now use NUMERIC columns in both primary keys and secondary indexes, which can improve query performance for workloads that rely on high-precision values such as currency amounts, measurements and statistical data.  

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Amazon Quick Suite Enables Easy Resolution of Ambiguous Map Locations

Quick Sight in Amazon Quick Suite now enables authors to resolve or update ambiguous locations directly on map visuals for accurate geographical data visualization. When Quick Suite encounters location names that exist in multiple regions—such as cities like Springfield or Abbeville that appear in multiple U.S. states—users can now explicitly define the correct geographical context through three resolution methods: adding supporting geospatial fields to create location hierarchies, searching for specific locations from Quick Suite’s geographical database, or entering exact latitude and longitude coordinates for precise positioning.

These enhancements address visualization accuracy needs for organizations working with datasets containing common location names that could refer to multiple places. With location mapping, dashboard authors can ensure their geospatial visuals correctly represent their data’s geographical context, leading to more reliable insights. The feature provides clear status tracking with Unmatched, Matched, and Unused location indicators, helping users understand and manage their location mappings effectively. Users can resolve ambiguous locations directly from map visuals by selecting «Resolve now» or accessing «Geo data match» options.

This feature is now available in all Amazon Quick Suite regions where Quick Sight is supported. Discover how to create maps and geospatial charts in Quick Suite and learn more about this new feature in our blog post.

 

​Quick Sight in Amazon Quick Suite now enables authors to resolve or update ambiguous locations directly on map visuals for accurate geographical data visualization. When Quick Suite encounters location names that exist in multiple regions—such as cities like Springfield or Abbeville that appear in multiple U.S. states—users can now explicitly define the correct geographical context through three resolution methods: adding supporting geospatial fields to create location hierarchies, searching for specific locations from Quick Suite’s geographical database, or entering exact latitude and longitude coordinates for precise positioning. These enhancements address visualization accuracy needs for organizations working with datasets containing common location names that could refer to multiple places. With location mapping, dashboard authors can ensure their geospatial visuals correctly represent their data’s geographical context, leading to more reliable insights. The feature provides clear status tracking with Unmatched, Matched, and Unused location indicators, helping users understand and manage their location mappings effectively. Users can resolve ambiguous locations directly from map visuals by selecting «Resolve now» or accessing «Geo data match» options. This feature is now available in all Amazon Quick Suite regions where Quick Sight is supported. Discover how to create maps and geospatial charts in Quick Suite and learn more about this new feature in our blog post.  

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AWS Lake Formation is now available in Asia Pacific (New Zealand) Region

AWS Lake Formation is now available in the Asia Pacific (New Zealand) Region, enabling you to centrally manage and scale fine-grained data access permissions and share data securely within and outside your organization.

AWS Lake Formation is a service that allows you to define where your data resides and what data access and security policies you want to apply. Your users can then access the centralized AWS Glue Data Catalog which describes available data sets and their appropriate usage. Your users can then usethese data sets with their choice of analytics and machine learning services, like Amazon EMR for Apache Spark, Amazon Redshift, AWS Glue, Amazon QuickSight, and Amazon Athena.

To learn more about Lake Formation, visit the documentation. For AWS Lake Formation Region availability, please see the AWS Region table.

 

​AWS Lake Formation is now available in the Asia Pacific (New Zealand) Region, enabling you to centrally manage and scale fine-grained data access permissions and share data securely within and outside your organization. AWS Lake Formation is a service that allows you to define where your data resides and what data access and security policies you want to apply. Your users can then access the centralized AWS Glue Data Catalog which describes available data sets and their appropriate usage. Your users can then usethese data sets with their choice of analytics and machine learning services, like Amazon EMR for Apache Spark, Amazon Redshift, AWS Glue, Amazon QuickSight, and Amazon Athena. To learn more about Lake Formation, visit the documentation. For AWS Lake Formation Region availability, please see the AWS Region table.  

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Amazon Connect launches an appeals workflow for agent performance evaluations

Amazon Connect now provides an integrated workflow to capture and resolve agent appeals of performance evaluations, enhancing evaluation fairness and agent engagement. When agents disagree with an evaluation, they can appeal the evaluation along with their reasoning directly within the Connect UI. For example, an agent who received a low evaluation score for active listening on a conversation, may appeal their evaluation by citing specific examples where they actively listened and acknowledged the customer’s problem. Designated managers then receive automated email notifications to review and resolve the appeal. Additionally, managers can monitor which evaluations have been appealed, and track their status, ensuring timely resolution of appeals.

This feature is available in all regions where Amazon Connect is offered. To learn more, please visit our documentation and our webpage.

 

​Amazon Connect now provides an integrated workflow to capture and resolve agent appeals of performance evaluations, enhancing evaluation fairness and agent engagement. When agents disagree with an evaluation, they can appeal the evaluation along with their reasoning directly within the Connect UI. For example, an agent who received a low evaluation score for active listening on a conversation, may appeal their evaluation by citing specific examples where they actively listened and acknowledged the customer’s problem. Designated managers then receive automated email notifications to review and resolve the appeal. Additionally, managers can monitor which evaluations have been appealed, and track their status, ensuring timely resolution of appeals. This feature is available in all regions where Amazon Connect is offered. To learn more, please visit our documentation and our webpage.  

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AWS Management Console now displays Account Name on the Navigation bar for easier account identification

Today, AWS announces the general availability of displaying account name in AWS Management Console across all Public Regions. AWS customers now have an easy way to identify their accounts at a glance. Users can now quickly distinguish between accounts visually using the account name that appears in the navigation bar for all authorized users in that account.

AWS customers manage multiple accounts to separate their workloads, such as maintaining distinct accounts for development and production environments or for different business units. Previously, users had to rely on account numbers to identify accounts. With this new feature, all authorized users can quickly identify the account using its name on the navigation bar.

The account name display feature is available at no additional cost in all public AWS Regions. To get started, make sure your administrator has enabled the feature (visit our managed policy documentation) and sign in to AWS Management Console. 

 

​Today, AWS announces the general availability of displaying account name in AWS Management Console across all Public Regions. AWS customers now have an easy way to identify their accounts at a glance. Users can now quickly distinguish between accounts visually using the account name that appears in the navigation bar for all authorized users in that account. AWS customers manage multiple accounts to separate their workloads, such as maintaining distinct accounts for development and production environments or for different business units. Previously, users had to rely on account numbers to identify accounts. With this new feature, all authorized users can quickly identify the account using its name on the navigation bar. The account name display feature is available at no additional cost in all public AWS Regions. To get started, make sure your administrator has enabled the feature (visit our managed policy documentation) and sign in to AWS Management Console.   

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Build Production-Ready Drug Discovery and Robotics Pipelines with NVIDIA NIMs on SageMaker JumpStart

Amazon SageMaker JumpStart now enables one-click deployment of four NVIDIA NIMs models purpose-built for biosciences and physical AI: ProteinMPNN, Nemotron-3.5B-Instruct, MSA Search NIM, and Cosmos Reason. NVIDIA NIM™ provides prebuilt, optimized inference microservices for rapidly deploying the latest AI models on any NVIDIA-accelerated infrastructure. These models bring advanced capabilities spanning protein design, reasoning with configurable outputs, and physical world understanding, enabling customers to accelerate biosciences research, drug discovery, and embodied AI applications on AWS infrastructure.

ProteinMPNN enables fast and efficient protein sequence optimization guided by structural data. This NIM generates high-quality sequences with enhanced binding affinity and stability, validated through experimental results. Designed for scalability and flexibility, ProteinMPNN integrates seamlessly into protein engineering workflows, transforming applications like enzyme design and therapeutic development.

MSA Search NIM supports GPU-accelerated Multiple Sequence Alignment (MSA) of a query amino acid sequence against a set of protein sequence databases. These databases are searched for similar sequences to the query and then the collection of sequences are aligned to establish similar regions even when the proteins have different lengths and motifs.

Nemotron-3.5B-Instruct delivers high reasoning performance, native tool calling support, and extended context processing with 256k token context window. This model employs an efficient hybrid Mixture-of-Experts (MoE) architecture to ensure higher throughput than its predecessors for agentic and coding workloads, while maintaining the reasoning depth of a larger model. It is ideal for building multi-agent workflows, developer productivity tools, processes automation, and for scientific and mathematical reasoning analysis, amongst others.

Cosmos Reason is an open , customizable, reasoning vision language model (VLM) for physical AI and robotics. It enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding, and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next.

With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.

To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

 

​Amazon SageMaker JumpStart now enables one-click deployment of four NVIDIA NIMs models purpose-built for biosciences and physical AI: ProteinMPNN, Nemotron-3.5B-Instruct, MSA Search NIM, and Cosmos Reason. NVIDIA NIM™ provides prebuilt, optimized inference microservices for rapidly deploying the latest AI models on any NVIDIA-accelerated infrastructure. These models bring advanced capabilities spanning protein design, reasoning with configurable outputs, and physical world understanding, enabling customers to accelerate biosciences research, drug discovery, and embodied AI applications on AWS infrastructure.
ProteinMPNN enables fast and efficient protein sequence optimization guided by structural data. This NIM generates high-quality sequences with enhanced binding affinity and stability, validated through experimental results. Designed for scalability and flexibility, ProteinMPNN integrates seamlessly into protein engineering workflows, transforming applications like enzyme design and therapeutic development.
MSA Search NIM supports GPU-accelerated Multiple Sequence Alignment (MSA) of a query amino acid sequence against a set of protein sequence databases. These databases are searched for similar sequences to the query and then the collection of sequences are aligned to establish similar regions even when the proteins have different lengths and motifs.
Nemotron-3.5B-Instruct delivers high reasoning performance, native tool calling support, and extended context processing with 256k token context window. This model employs an efficient hybrid Mixture-of-Experts (MoE) architecture to ensure higher throughput than its predecessors for agentic and coding workloads, while maintaining the reasoning depth of a larger model. It is ideal for building multi-agent workflows, developer productivity tools, processes automation, and for scientific and mathematical reasoning analysis, amongst others.
Cosmos Reason is an open , customizable, reasoning vision language model (VLM) for physical AI and robotics. It enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding, and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.