Category: Amazon Bedrock Guardrails

This post shows you how to find those delegation failures in 30 minutes and what to do about them. It builds on the delegation model introduced in the Amazon Bedrock AgentCore companion post and turns it into an audit you can run today. This post also shows you how to enforce the boundaries you find on Amazon Web Services (AWS) with Amazon Bedrock AgentCore Policy, AWS Identity and Access Management (IAM), and Amazon Bedrock Guardrails.

AI handles triage while analysts handle judgment. And, by coupling automated continuous monitoring with AI system connectivity, recovery time can speed up, resulting in fewer after-hours status calls. Your existing Amazon Web Services (AWS) environment already has access to the capabilities that can make this a reality.

In this post, we look at why these three make up the AWS CAF-AI perspectives EdTech leaders should mature first, and what building each one looks like in practice.

Learn how with Amazon Bedrock Knowledge Bases, you build an assistant that grounds answers in your trusted documents through Retrieval Augmented Generation (RAG).

In this post, we show how Pariveda and Amazon Web Services (AWS) worked with HS1 to build real-time voice documentation for dental visits. The solution uses Amazon Transcribe for streaming speech recognition and Amazon Bedrock for structured note generation and orchestration. We also explain the architectural choices required to support real-time performance, template-driven output, security controls, and production scale.

This post shows how to build that on Amazon Web Services (AWS) using Amazon Bedrock, deployed in AWS GovCloud (US). You’ll deploy a working multi-agent workload, see how a supervisor coordinates specialized agents through the Converse API in Amazon Bedrock, and learn which AWS GovCloud (US) details break patterns copied from commercial Regions.

In this blog, learn how five durable pillars—full-stack builders, parallel decision-making, context as a competitive moat, disciplined prioritization, and trust at AI speed—can stabilize your AI product development strategy amid rapid technological change. Drawn from the AWS Product Acceleration team’s work with AI-native product leaders, these principles help organizations cut through the noise and convert AI-driven speed into real customer value rather than chaos.

The methodology described in this post translates subjective personality requirements into testable behaviors, versioned prompts, and documented boundaries. It addresses several dimensions of Responsible AI at AWS, an eight-dimension framework that guides how we build and evaluate AI systems.

This blog share how if your agency is managing millions of unstructured documents, from eligibility records to case files, this post introduces how DMI Document AI, built on Amazon Bedrock, can help you automate document processing, reduce backlogs, and redirect your team toward higher-value mission work.

This post shares how Leidos enhanced ManagedX by adopting multi-agent workflow patterns using the open source Strands Agents SDK, and how AWS Enterprise Support helped guide that evolution.