Category: Amazon Bedrock

In this post, you’ll learn how to build an identity incident review workflow that analyzes an event timeline, converts the findings into a strict incident record, and connects the review to a local account-control lookup. The examples separate observed facts from inferences and keep final incident and remediation decisions with a human reviewer.

Learn how the AMA Agentic Platform, built on Amazon Web Services (AWS) using Amazon Bedrock AgentCore. The solution puts AI directly into the hands of all 1,800-plus employees, a shift from treating AI as a feature to working with AI as a colleague.

In this post, we explain what IL5 requires, how AWS GovCloud (US) and SAP NS2 meet those requirements together, and how defense organizations can get started.

In this post, we explore practical design patterns for SaaS offerings on Amazon Web Services (AWS) and show how sovereignty can be a tunable design dimension rather than an all-or-nothing rebuild.

In this blog, learn more about the availability of Amazon Bedrock AgentCore in the AWS GovCloud (US-West) Region, giving government and regulated industries a fully managed solution to build, deploy, and operate AI agents at production scale, without needing to manage the underlying infrastructure. This launch is part of Amazon’s broader commitment to invest more than $50 billion in cloud and AI infrastructure, giving government customers access to the same cutting-edge AI capabilities available in commercial Regions.

A distributed approach, one that brings generative AI to the data rather than the reverse, is achievable today using Amazon Web Services (AWS) solutions such as Amazon Bedrock for orchestration, Amazon Neptune for data lineage, and AWS Identity and Access Management (IAM) for source-point security enforcement.

In Part 2 of our two-part series, we discuss the architecture of the Signal-Activated Agent Pattern and its application for proactive government AI. For Part 1, see Signal-activated generative AI: How agencies can reach more people and react faster.

This two-part series introduces the Signal-Activated Agent Pattern—an architectural approach, backed by a deployable Amazon Web Services (AWS) reference implementation, that helps government AI platforms move from generic, reactive question-answering to proactive, contextually personalized decision support. With this solution, agencies can reach more people, respond faster, and deliver the right value to the right official at each decision point.

Learn how Amazon Web Services (AWS) has collaborated with the University of Technology Sydney (UTS) to make a more engaging content delivery platform as an alternative to long lecture videos.

to meet this demand.
The Kayode Alabi Leadership and Career Initiative (KLCI Africa), a nonprofit social enterprise headquartered in Lagos, Nigeria, set out to solve this problem using generative AI and Amazon Web Services (AWS). In this post, we describe how KLCI Africa built Rafiki AI, a WhatsApp-based generative AI career advisor that delivers personalized career guidance to underserved and displaced youth in under 2 minutes.