Artificial Intelligence

We’re excited to share the Amazon Bedrock Data Automation Model Context Protocol (MCP) server, for seamless integration between Amazon Q and your enterprise data. In this post, you will learn how to use the Amazon Bedrock Data Automation MCP server to securely integrate with AWS Services, use Bedrock Data Automation operations as callable MCP tools, and build a conversational development experience with Amazon Q.

In this blog post, we explore how Amazon Q Business is transforming enterprise data interaction through Agentic Retrieval Augmented Generation (RAG).

University Startups, headquartered in Bethesda, MD, was founded in 2020 to empower high school students to expand their education beyond a traditional curriculum. University Startups is focused on special education and related services in school districts throughout the US. In this post, we explain how University Startups uses generative AI technology on AWS to enable students to design a specific plan for their future either in education or the work force.

In this post, we demonstrate how to deploy the DeepSeek-R1-Distill-Qwen-32B model using AWS DLCs for vLLMs on Amazon EKS, showcasing how these purpose-built containers simplify deployment of this powerful inference engine. This solution can help you solve the complex infrastructure challenges of deploying LLMs while maintaining performance and cost-efficiency.

In this post, we demonstrate how to prompt Amazon Nova understanding models to cite sources in responses. Further, we will also walk through how we can evaluate the responses (and citations) for accuracy.

In this post, we explore how Amazon Bedrock AgentCore Runtime simplifies the deployment and management of AI agents.

This post presents how AWS and PwC are developing new reasoning checks that combine deep industry expertise with Automated Reasoning checks in Amazon Bedrock Guardrails to support innovation.

In this post, Amazon shares how they developed a multi-node inference solution for Rufus, their generative AI shopping assistant, using Amazon Trainium chips and vLLM to serve large language models at scale. The solution combines a leader/follower orchestration model, hybrid parallelism strategies, and a multi-node inference unit abstraction layer built on Amazon ECS to deploy models across multiple nodes while maintaining high performance and reliability.

This post describes an approach of combining three powerful technologies to illustrate an architecture that you can adapt and build upon for your specific financial analysis needs: LangGraph for workflow orchestration, Strands Agents for structured reasoning, and Model Context Protocol (MCP) for tool integration.

In this post, we explore Amazon Bedrock AgentCore Memory, a fully managed service that enables AI agents to maintain both immediate and long-term knowledge, transforming one-off conversations into continuous, evolving relationships between users and AI agents. The service eliminates complex memory infrastructure management while providing full control over what AI agents remember, offering powerful capabilities for maintaining both short-term working memory and long-term intelligent memory across sessions.