Category: Amazon Bedrock

Every major agent framework has a security model for this. Amazon Web Services (AWS), Microsoft, and Google each ship agent solutions with authentication and credential management built in. Amazon Bedrock Agents, for example, enforces AWS Identity and Access Management (IAM)-based authorization on which AWS Lambda functions, Amazon Simple Storage Service (Amazon S3) buckets, and Amazon Bedrock Knowledge Bases an agent might invoke.

This post shows how public sector and enterprise teams can build an agentic AI-powered classification system on Amazon Web Services (AWS) using the Strands Agents SDK, Amazon Bedrock, and serverless services. You will learn the architecture, the agent workflow, and how to implement this system using the Strands Agents SDK.

Learn how Amazon Web Services (AWS) offers a fundamentally different approach. By deploying agentic AI—specialized AI agents that work collaboratively to analyze, validate, and route documentation—federal agencies can dramatically accelerate conformity processing while maintaining or improving quality and compliance standards.

The Translational AI Center (TrAC) at Iowa State University is a pre-competitive research hub that bridges the gap between academic AI breakthroughs and real-world deployment. With over 70 affiliated faculty spanning seven ISU colleges, TrAC breaks down disciplinary silos and organizes research across thematic areas, including food and energy systems, healthcare, autonomy, materials design, and AI ethics.

The United States General Services Administration (GSA) Pegasys Financial Management program plays a vital role in managing the agency’s financial operations, processing hundreds of billions of dollars in transactions annually for GSA and 40 partner agencies, boards, and commissions. When GSA needed to modernize the infrastructure supporting this mission-critical system, the agency faced a significant challenge. It needed to migrate 30 terabytes of production data, over 100 virtual machines, and 70 external system interfaces to the cloud—all within 6 months while maintaining uninterrupted availability.

In this post, we discuss how AWS and Wiz support security and resilience for AMCs to help keep the focus more on patient care while maintaining a robust, secure operating environment.

The cost dimensions of agentic workloads on Amazon Web Services (AWS) are invisible to many organizations beginning their agentic journey. It’s straightforward to track tokens consumed because this dimension translates directly to your bill. But most organizations can’t determine the cost per request or the cost to answer a single user question. Without understanding the cost to answer a single user question, organizations are blind to cost issues. In this post, I talk about how to gain visibility into your agentic costs, and I identify three things you can do to better control your costs.

Real missions break perfect prototypes. Through Project MAVERICK (Mission Autonomy Versatile Rapid Innovation and Capabilities Kit), Amazon Web Services (AWS) confronts this reality head-on—bringing cloud capabilities directly into the field to test autonomous systems where it matters most.

This blog discusses how AWS has supported more than 600 customers with over $90 million of technology to innovate in health. Forty-four percent of these customers employed AWS AI services, seeding AI innovation across the global health landscape and proving that cloud-powered AI can improve health and wellness for all. This blog highlights nine of those organizations deploying AI to save lives today, culminating in AWS’s largest single social impact investment in health: a landmark technology collaboration with the Fleming Initiative to build the world’s first AI-powered platform for combating antimicrobial resistance.

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.