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1. Build real-time travel recommendations using AI agents on Amazon Bedrock

   In this post, we show how to build a generative AI solution using Amazon Bedrock that creates bespoke holiday packages by combining customer profiles and preferences with real-time pricing data. We demonstrate how to use Amazon Bedrock Knowledge Bases for travel information, Amazon Bedrock Agents for real-time flight details, and Amazon OpenSearch Serverless for efficient package search and...

2. Deploy a full stack voice AI agent with Amazon Nova Sonic

   In this post, we show how to create an AI-powered call center agent for a fictional company called AnyTelco. The agent, named Telly, can handle customer inquiries about plans and services while accessing real-time customer data using custom tools implemented with the Model Context Protocol (MCP) framework.

3. Manage multi-tenant Amazon Bedrock costs using application inference profiles

   This post explores how to implement a robust monitoring solution for multi-tenant AI deployments using a feature of Amazon Bedrock called application inference profiles. We demonstrate how to create a system that enables granular usage tracking, accurate cost allocation, and dynamic resource management across complex multi-tenant environments.

4. Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI

   Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content...

5. Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors

   In this post, we demonstrate how to integrate Amazon S3 Vectors with Amazon Bedrock Knowledge Bases for RAG applications. You'll learn a practical approach to scale your knowledge bases to handle millions of documents while maintaining retrieval quality and using S3 Vectors cost-effective storage.

6. Implementing on-demand deployment with customized Amazon Nova models on Amazon Bedrock

   In this post, we walk through the custom model on-demand deployment workflow for Amazon Bedrock and provide step-by-step implementation guides using both the AWS Management Console and APIs or AWS SDKs. We also discuss best practices and considerations for deploying customized Amazon Nova models on Amazon Bedrock.

7. Building enterprise-scale RAG applications with Amazon S3 Vectors and DeepSeek R1 on Amazon SageMaker AI

   Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search...

8. Accenture scales video analysis with Amazon Nova and Amazon Bedrock Agents

   This post was written with Ilan Geller, Kamal Mannar, Debasmita Ghosh, and Nakul Aggarwal of Accenture. Video highlights offer a powerful way to boost audience engagement and extend content value for content publishers. These short, high-impact clips capture key moments that drive viewer retention, amplify reach across social media, reinforce brand identity, and open new […]

9. Deploy conversational agents with Vonage and Amazon Nova Sonic

   In this post, we explore how developers can integrate Amazon Nova Sonic with the Vonage communications service to build responsive, natural-sounding voice experiences in real time. By combining the Vonage Voice API with the low-latency and expressive speech capabilities of Amazon Nova Sonic, businesses can deploy AI voice agents that deliver more human-like interactions than traditional voice...

10. Enabling customers to deliver production-ready AI agents at scale

    Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.