AWS for Industries

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 this post, we build on that foundation by constructing specialized AI agents for each data modality along with a supervisor agent that orchestrates cross-modal analysis using Amazon Bedrock AgentCore and Strands Agents SDK. We also train predictive AI models with Amazon SageMaker AI to predict patient outcomes from multimodal features. To further explore the implementation details and get hands-on experience, refer to the accompanying code repository.

In this two-part blog series, we show how you can build agents that interact with multimodal HCLS data, making it easier for end users to query, explore, and ask questions of the data. We build on previous guidance for multimodal data analysis, which demonstrates how to store, query, and analyze clinical, genomic, and medical imaging data using purpose-built AWS services.

Introduction Voice communications infrastructure remains a critical challenge for organizations migrating to the cloud. While IT workloads successfully move to AWS, Session Border Controllers (SBCs) often stay anchored to legacy hardware platforms that cannot scale dynamically or integrate with modern cloud operations. Ribbon’s SBC Cloud Native edition (CNe) on Amazon Elastic Kubernetes Service (Amazon EKS) […]

Learn more about Engineering Development Hub (EDH), a unified, cloud-based engineering workbench that brings together the applications, compute, and data you need to design, test, and validate complex systems—all in a single, open source environment.

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

When AWS released Amazon S3 Tables, this calculus changed. In this post, you will learn how Amica reduced their ETL job runtimes by 80% by building a data lake for core insurance data using S3 Tables.

Learn how Peloton uses Amazon Bedrock for access to frontier models, including Anthropic’s Claude Sonnet 4.6, Opus 4.7 and Opus 4.8. Amazon Bedrock also provides global cross-region inference, integration into AWS CloudTrail and AWS CloudWatch, and model access logging for the observability and audit controls its engineering organization requires.

Retailers have always stubbed their toes on early technology adoption—from scanned product barcodes ringing up at the wrong price, to self-checkout lanes that created more shrink than savings, to ecommerce platforms that oversold products they couldn’t ship. But every time, the retailers who learned to master the technology, instead of abandoning it, were the ones who pulled ahead. AI agents are no different.

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.