Category: Technical How-to

In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on.

In this post, we demonstrate how to build a multi-agent SRE assistant using Amazon Bedrock AgentCore, LangGraph, and the Model Context Protocol (MCP). This system deploys specialized AI agents that collaborate to provide the deep, contextual intelligence that modern SRE teams need for effective incident response and infrastructure management.

In the benefits administration industry, claims processing is a vital operational pillar that makes sure employees and beneficiaries receive timely benefits, such as health, dental, or disability payments, while controlling costs and adhering to regulations like HIPAA and ERISA. In this post, we examine the typical benefit claims processing workflow and identify where generative AI-powered automation can deliver the greatest impact.

AI agents are evolving beyond basic single-task helpers into more powerful systems that can plan, critique, and collaborate with other agents to solve complex problems. Deep Agents—a recently introduced framework built on LangGraph—bring these capabilities to life, enabling multi-agent workflows that mirror real-world team dynamics. The challenge, however, is not just building such agents but […]

In this post, we show you how to integrate Amazon Bedrock Guardrails with third-party tokenization services to protect sensitive data while maintaining data reversibility. By combining these technologies, organizations can implement stronger privacy controls while preserving the functionality of their generative AI applications and related systems.

This post explores how Amazon Bedrock AgentCore helps you transition your agentic applications from experimental proof of concept to production-ready systems. We follow the journey of a customer support agent that evolves from a simple local prototype to a comprehensive, enterprise-grade solution capable of handling multiple concurrent users while maintaining security and performance standards.

In this post, we show how to integrate AWS DLCs with MLflow to create a solution that balances infrastructure control with robust ML governance. We walk through a functional setup that your team can use to meet your specialized requirements while significantly reducing the time and resources needed for ML lifecycle management.

In this post, we showed how to use the Amazon Q Business browser extension to give your team seamless access to AI-driven insights and assistance. The browser extension is now available in US East (N. Virginia) and US West (Oregon) AWS Regions for Mozilla, Google Chrome, and Microsoft Edge as part of the Lite Subscription.

In this post, we introduce topology-aware scheduling with SageMaker HyperPod task governance by submitting jobs that represent hierarchical network information. We provide details about how to use SageMaker HyperPod task governance to optimize your job efficiency.

In this post, we walk through how to streamline your RAG development lifecycle from experimentation to automation, helping you operationalize your RAG solution for production deployments with Amazon SageMaker AI, helping your team experiment efficiently, collaborate effectively, and drive continuous improvement.