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.

In this post, we share four high-impact, widely adopted use cases built with Nova in Amazon Bedrock, supported by real-world customers deployments, offerings available from AWS partners, and experiences. These examples are ideal for organizations researching their own AI adoption strategies and use cases across industries.

In this post, we explore how Amazon Bedrock AgentCore Memory transforms raw conversational data into persistent, actionable knowledge through sophisticated extraction, consolidation, and retrieval mechanisms that mirror human cognitive processes. The system tackles the complex challenge of building AI agents that don’t just store conversations but extract meaningful insights, merge related information across time, and maintain coherent memory stores that enable truly context-aware interactions.

Misconfiguration issues in distributed training with Amazon EKS can be prevented following a systematic approach to launch required components and verify their proper configuration. This post walks through the steps to set up and verify an EKS cluster for training large models using DLCs.

This post provides a comprehensive guide on integrating the Almond kernel into SageMaker Studio, offering a solution for Scala development within the platform.

In this post, we explore how to build a conversational device management system using Amazon Bedrock AgentCore. With this solution, users can manage their IoT devices through natural language, using a UI for tasks like checking device status, configuring WiFi networks, and monitoring user activity.

This post shows how Salesforce integrated Amazon Bedrock Custom Model Import into their machine learning operations (MLOps) workflow, reused existing endpoints without application changes, and benchmarked scalability. We share key metrics on operational efficiency and cost optimization gains, and offer practical insights for simplifying your deployment strategy.

In this post, we explore how Physical AI represents the next frontier in intelligent automation, where artificial intelligence transcends digital boundaries to perceive, understand, and manipulate the tangible world around us.

In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain’s document processing, and Streamlit’s interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters.

In this post, we’ll show how Kitsa, a health-tech company specializing in AI-driven clinical trial recruitment and site selection, used Amazon Quick Automate to transform their clinical trial site selection solution. Amazon Quick Automate, a capability of Amazon Quick Suite, enables enterprises to build, deploy and maintain resilient workflow automations at scale.

In this post, we explore how Amazon Quick Suite’s Model Context Protocol (MCP) client enables secure, standardized connections to enterprise applications and AI agents, eliminating the need for complex custom integrations. You’ll discover how to set up MCP Actions integrations with popular enterprise tools like Atlassian Jira and Confluence, AWS Knowledge MCP Server, and Amazon Bedrock AgentCore Gateway to create a collaborative environment where people and AI agents can seamlessly work together across your organization’s data and applications.