Artificial Intelligence

Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.

We are excited to announce that customers in Canada can now access advanced foundation models including Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5 on Amazon Bedrock through cross-Region inference (CRIS). This post explores how Canadian organizations can use cross-Region inference profiles from the Canada (Central) Region to access the latest foundation models to accelerate AI initiatives. We will demonstrate how to get started with these new capabilities, provide guidance for migrating from older models, and share recommended practices for quota management.

Amazon SageMaker HyperPod clusters with Amazon Elastic Kubernetes Service (EKS) orchestration now support creating and managing interactive development environments such as JupyterLab and open source Visual Studio Code, streamlining the ML development lifecycle by providing managed environments for familiar tools to data scientists. This post shows how HyperPod administrators can configure Spaces for their clusters, and how data scientists can create and connect to these Spaces.

Anthropic’s newest foundation model, Claude Opus 4.5, is now available in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. In this post, I’ll show you what makes this model different, walk through key business applications, and demonstrate how to use Opus 4.5’s new tool use capabilities on Amazon Bedrock.

In this post, we show how to deploy the GPT-OSS-20B model on Amazon Bedrock using Custom Model Import while maintaining complete API compatibility with your current applications.

In this post, we introduce the Multi-Provider Generative AI Gateway reference architecture, which provides guidance for deploying LiteLLM into an AWS environment to streamline the management and governance of production generative AI workloads across multiple model providers. This centralized gateway solution addresses common enterprise challenges including provider fragmentation, decentralized governance, operational complexity, and cost management by offering a unified interface that supports Amazon Bedrock, Amazon SageMaker AI, and external providers while maintaining comprehensive security, monitoring, and control capabilities.

In this post, you’ll learn how to deploy geospatial AI agents that can answer complex spatial questions in minutes instead of months. By combining Foursquare Spatial H3 Hub’s analysis-ready geospatial data with reasoning models deployed on Amazon SageMaker AI, you can build agents that enable nontechnical domain experts to perform sophisticated spatial analysis through natural language queries—without requiring geographic information system (GIS) expertise or custom data engineering pipelines.

In this post, we share how Wipro implemented advanced prompt engineering techniques, custom validation logic, and automated code rectification to streamline the development of industrial automation code at scale using Amazon Bedrock. We walk through the architecture along with the key use cases, explain core components and workflows, and share real-world results that show the transformative impact on manufacturing operations.

This blog post has explores how MSD is harnessing the power of generative AI and databases to optimize and transform its manufacturing deviation management process. By creating an accurate and multifaceted knowledge base of past events, deviations, and findings, the company aims to significantly reduce the time and effort required for each new case while maintaining the highest standards of quality and compliance.

In this blog post, we show you how agentic workflows can accelerate the processing and interpretation of genomics pipelines at scale with a natural language interface. We demonstrate a comprehensive genomic variant interpreter agent that combines automated data processing with intelligent analysis to address the entire workflow from raw VCF file ingestion to conversational query interfaces.

Our team at Amazon builds Rufus, an AI-powered shopping assistant which delivers intelligent, conversational experiences to delight our customers. More than 250 million customers have used Rufus this year. Monthly users are up 140% YoY and interactions are up 210% YoY. Additionally, customers that use Rufus during a shopping journey are 60% more likely to […]