Category: Amazon Simple Storage Service (S3)

This post demonstrates on Massive Multitask Language Understanding (MMLU) [Hendrycks et al., 2021], an open-source multiple-choice benchmark accessible via Hugging Face Hub, selecting four business-relevant subjects.

This is part 3 of our three-part series on how Amazon Web Services (AWS) aligns with MPA Content Security Best Practices with a particular focus on media archives.

This is Part 2 of our three-part series on how Amazon Web Services (AWS) aligns with Motion Picture Association (MPA) Content Security Best Practices v5.3.1 for media archives. In this second installment, we turn to the operational and detective controls that complete the security picture.

In this post, we demonstrate how a large North American Utility is currently running a multi-agent AI architecture on Amazon Bedrock AgentCore to compress alarm triage from hours to minutes, avoiding the manual, multi-system investigation that operators perform dozens of times per shift. We will walk through the industry problem, the multi-agent design, and a reference implementation on GitHub that you can deploy into your own environment.

In this blog, we provide a practitioner-focused guide for enabling a new AWS Region in a financial services enterprise AWS environment. We walk through each workstream, from initial governance approval through networking, security, operations, and workload readiness, highlighting the considerations, dependencies, and common pitfalls that teams encounter.

This blog post introduces a serverless dynamic pricing platform built on Amazon Web Services (AWS) that helps restaurants automatically adjust menu prices based on real-time demand patterns. The solution enables restaurants to maximize revenue during peak hours while attracting price-sensitive customers during slower periods.

In the retail and consumer packaged goods (CPG) industry, accurate food labeling is a critical safety requirement. Retailers face an increasingly complex challenge: manually verifying that thousands of packaged foods comply with FDA regulations—particularly the allergen labeling requirements under the Food Allergen Labeling and Consumer Protection Act (FALCPA) of 2004.

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.