Category: Generative AI

In this post, we present a centralized Model Context Protocol (MCP) server implementation using Amazon Bedrock that provides shared access to tools and resources for enterprise AI workloads. The solution enables organizations to accelerate AI innovation by standardizing access to resources and tools through MCP, while maintaining security and governance through a centralized approach.

In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.

In this post, we explore how to use Amazon Q Developer CLI with the AWS Diagram MCP and the AWS Documentation MCP servers to create sophisticated architecture diagrams that follow AWS best practices. We discuss techniques for basic diagrams and real-world diagrams, with detailed examples and step-by-step instructions.

In this post, we explore how to use Amazon Q CLI with the AWS Cost Analysis MCP server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions.

In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.

We demonstrate two methods for generating structured responses with Amazon Bedrock: Prompt Engineering and Tool Use with the Converse API. Prompt Engineering is flexible, works with Bedrock models (including those without Tool Use support), and handles various schema types (e.g., Open API schemas), making it a great starting point. Tool Use offers greater reliability, consistent results, seamless API integration, and runtime validation of JSON schema for enhanced control.

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in Amazon Bedrock Agents to solve complex business questions in the biopharmaceutical industry. We show how specialized agents in research and development (R&D), legal, and finance domains can work together to provide comprehensive business insights by analyzing data from multiple sources.

In this post, we shared how an internal technology team at Amazon evaluated Amazon Nova models, resulting in notable improvements in inference speed and cost-efficiency.

Today we are excited to introduce the Text Ranking and Question and Answer UI templates to SageMaker AI customers. In this blog post, we’ll walk you through how to set up these templates in SageMaker to create high-quality datasets for training your large language models.

Today, we’re excited to announce a new integration between Arize AI and Amazon Bedrock Agents that addresses one of the most significant challenges in AI development: observability. In this post, we demonstrate the Arize Phoenix system for tracing and evaluation.