Category: Analytics

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.

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.

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.

Every Thanksgiving, tens of thousands of Peloton Members log on for Turkey Burn, a community tradition that has grown into one of the most technically demanding real-time workloads in the fitness industry. In 2024 and 2025, that engineering foundation held flawlessly: two consecutive events, zero major incidents. This builds on a 2023 Guinness World Record that saw 27,556 simultaneous participants in a single cycling class. Behind those results is a sophisticated cloud architecture on AWS, shaped by years of rigorous engineering, deep partnership between Peloton and AWS teams, and a relentless commitment to continuous improvement.

Learn how financial institutions in Hong Kong face a defining moment in how they deliver technology-driven banking: the Hong Kong Monetary Authority (HKMA) launched Fintech 2030 on November 3, 2025, introducing the DART framework with named initiatives and clear supervisory expectations.

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.

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.

In this post, we present four production-hardening patterns (A-D) that extend Patterns 3 and 4 for payment workloads operating persistent session-based protocols. These patterns optimize connection reliability, maintenance workflows, tenant isolation, and observability at the infrastructure layer, benefiting organizations connecting to traditional payment rails through AWS PrivateLink and Resource Gateway.