AWS for Industries

This two-part series explores how ontology-grounded agentic AI transforms Design Failure Mode and Effects Analysis (DFMEA) for safety-critical automotive components — from the strategic imperative driving adoption to the architectural patterns and implementation details. In this post, we focus on: How AI can help with DFMEA, how engineering ontologies enable AI to reason for failure mechanisms rather than pattern-match, and what engineering leaders should prioritize today.

In this post, we explore how modern, cloud-based ETRM solutions, like PCI’s ETRM, can help energy leaders better manage portfolio risks and navigate volatile markets with confidence.

This blog explains how multi-agent AI systems can automate the reasoning chain between supply chain data and action—closing the gap that control towers leave open by translating natural language questions into SQL queries, performing root cause analysis, and generating execution-ready materials in seconds rather than hours.

​​​​​​​AWS is building the infrastructure for programmatic advertising’s shift to agentic AI where autonomous agents plan campaigns, orchestrate models, and optimize bids across the full funnel. Today, the bidstream processes billions of decisions daily, each within milliseconds, relying on rule-based heuristics and lightweight models constrained by real-time latency budgets and CPU-only infrastructure. That constraint is […]

Real-time bidding has revolutionized digital advertising—every time a user browses a website, streams a video, or opens a mobile app, split-second auctions determine which ad appears. Ad buyers (demand-side platforms (DSPs)) and ad sellers (supply-side platforms) (SSPs)) transact millions of times per second, operating in a petabyte-scale, millisecond-latency world where even minor delays erode performance […]

This blog introduces a structured placement approach for AI workloads across AWS hybrid infrastructure. By evaluating each AI lifecycle phase against four dimensions (data sovereignty, latency, data gravity, and operational readiness), architects can determine the optimal deployment tier among AWS Regions, AWS Local Zones, AWS Outposts and AWS AI Factories.

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.

Learn how at the 2026 AWS Life Sciences Symposium, we brought together some of the most innovative companies in MedTech to share what it takes to build in this environment, and what becomes possible when you have the right data foundation and AI backbone.

In this post, we show how to build that system on Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Bedrock, and Amazon Bedrock AgentCore, with authentication, tracing, cost control, and sandboxed code execution at every layer.

This post presents a new Kiro Power called Arm SoC Migration Power helping developers migrate codebases between Arm SoCs. The Kiro Arm SoC Migration Power is released as a collaboration of Arm and AWS.