Category: Amazon Simple Storage Service (S3)

This post is a technical deep dive. It explains the Sandbox’s multi-tenant isolation model along with its inherited security and cost controls. It then examines two production-grade multi-agent AIOps systems our teams built on top of it, including the LangGraph (an open source multi-agent orchestration framework) state model, Retrieval-Augmented Generation (RAG) design, OpenSearch query patterns, parallel root cause analysis (RCA) with self-falsification, and the human-in-the-loop safeguards that help make agentic recovery safe in production. Code samples are illustrative and simplified for readability.

In a previous blog post, we described how AUMOVIO built an AI-powered engineering assistant on Amazon Bedrock that generates automotive-grade embedded code. That system accelerates development

In this post, we present a deployable reference architecture that addresses both challenges simultaneously. We show how Amazon Bedrock AgentCore orchestrates a single AI agent that reasons across unstructured documents in Amazon S3 and structured data in Snowflake.

In this blog, learn how Kite, a Gilead Company, modernized its bioinformatics infrastructure by transitioning from fragmented, project-specific workflows to a standardized, cloud-enabled approach using AWS HealthOmics. Discover how this collaboration with AWS Professional Services enabled over 100 researchers to access scalable, self-service computational workflows through their existing laboratory systems—without requiring CLI or infrastructure expertise.

Renewable energy operators face a growing challenge: Managing increasingly large, complex, high-value portfolios across solar, wind, and battery energy storage system (BESS) while meeting tighter compliance, grid, and market demands. Many operational workflows remain reactive, manual, and fragmented—limiting both performance and scalability. However, GreenBridge.AI is innovating renewable energy operations through AI agents for greater automated efficiency and scalability.

In this post, we show you how Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands Agents come together in VEMA (Voice-Enabled Manufacturing Assistant)—an AI-powered voice assistant that lets workers speak naturally to get instant answers, hands-free, in English or Spanish, without ever leaving their workstation.

This post describes how BBVA used pilots to identify reusable ML patterns, standardize operational workflows, and design extensible MLOps templates that accelerate ML delivery while maintaining governance and flexibility across teams and business domains.

Learn how by combining MedDream’s FDA-cleared diagnostic viewer with AWS HealthImaging and automated AWS Cloud Development Kit (AWS CDK) deployment, you can deploy a complete, production-ready medical imaging solution in under an hour—achieving sub-second loading times and reducing storage costs by up to 40% compared to on-premises infrastructure.

Learn how to architect an end-to-end pipeline that processes surgical video at the edge for de-identification, instrument detection, and surgical phase recognition—while using the cloud for model training and fleet management.

In this post, we describe a comprehensive, HIPAA-ready generative AI architecture for healthcare on Amazon Web Services (AWS) using a defense-in-depth approach. By layering compliance controls at multiple distinct levels, this architecture creates a system where no single point of failure compromises patient data protection, and each component that touches ePHI is independently auditable.