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  1. AWS Machine Learning Blog

    Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

    中文摘要

    通过 Amazon Bedrock AgentCore、Amazon Nova Sonic(用于实时语音)以及 Amazon Bedrock Knowledge Bases(用于政策解答),将语音旅行礼宾服务添加到航空公司应用程序中。旅客可以通过语音更改座位、查看延误信息并询问政策问题,而该代理通过 MCP 工具连接到您的后端,并在进行任何更改之前确认更改内容。

    英文原文

    Add a voice travel concierge to an airline app with Amazon Bedrock AgentCore, Amazon Nova Sonic for real-time speech, and Amazon Bedrock Knowledge Bases for policy answers. Travelers change seats, check delays, and ask policy questions by voice, while the agent reaches your backend through MCP tools and confirms every change before it writes.

  2. AWS Machine Learning Blog

    Supercharge regulated workloads with Claude Code and Amazon Bedrock

    中文摘要

    Anthropic Claude Opus 5.5 和 Claude Sonnet 5.5 在 AWS GovCloud(美国)区域的 Amazon Bedrock 上可用。了解如何使用 Claude Code,Anthropic 的代理编码工具,以实现合规对齐的、人工智能辅助的开发,用于受监管和 ITAR 工作负载。

    英文原文

    Anthropic Claude Opus 5.5 and Claude Sonnet 5.5 are available on Amazon Bedrock in the AWS GovCloud (US) Regions. Learn how to use them with Claude Code, Anthropic's agentic coding tool, for compliance-aligned, AI-assisted development on regulated and ITAR workloads.

  1. AWS Machine Learning Blog

    Making Amazon Quick enterprise-ready: Automated, auditable cross-account resource promotion

    中文摘要

    将 Amazon Quick 的资源(代理、操作连接器、知识库、流程和空间)从开发环境推广到生产环境 AWS 账户一直是一个手动且容易出错的过程。本文展示了如何通过在 Amazon Bedrock AgentCore 上使用可重复、可审计的 MCP 服务器来实现跨账户的自动化推广。

    英文原文

    Promoting Amazon Quick resources (agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account has been a manual, error-prone chore. This post shows how to automate cross-account promotion with an idempotent, auditable MCP server on Amazon Bedrock AgentCore.

  2. AWS Machine Learning Blog

    Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases

    中文摘要

    在 Amazon Bedrock 受管理的知识库上构建一个检索增强生成(RAG)应用程序,使用 LangChain,并查看代理检索如何处理单次检索回答不好的多部分问题。通过两种路径运行相同的查询,阅读跟踪事件,并比较每种检索路径的成本。

    英文原文

    Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly. Run the same query through both paths, read the trace events, and compare what each retrieval path costs.

  3. AWS Machine Learning Blog

    Downgrading user roles in Amazon Quick

    中文摘要

    Amazon Quick 并没有直接的控制台路径将用户从管理员或作者降级为读者。本文将介绍两种可靠的方法:一种是手动删除并重新创建的方法,另一种是使用 AWS CLI 的降级步骤,该步骤可以安全地降级角色,同时保留资产的所有权。

    英文原文

    Amazon Quick doesn't offer a direct console path to downgrade a user from Admin or Author to Reader. This post walks through two reliable methods: a manual delete-and-recreate approach and an AWS CLI step-down sequence that downgrades roles safely while preserving asset ownership.

  1. AWS Machine Learning Blog

    Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern

    中文摘要

    已裁定查询模式将Amazon Quick聊天代理与受约束的MCP服务器通过确定性规则引擎配对,以提供可证明完整且有据可依的合规性答案。本文将逐步介绍参考架构和可部署的AWS CDK示例,以租赁合规性为例进行说明。

    英文原文

    The Adjudicated Query pattern pairs the Amazon Quick chat agent with a bounded MCP server over a deterministic rules engine to deliver provably complete, defensible compliance answers. This post walks through the reference architecture and a deployable AWS CDK sample, using lease compliance as the running example.

  2. AWS Machine Learning Blog

    Add secure Web Search to Claude Desktop with Amazon Bedrock AgentCore

    中文摘要

    Amazon Bedrock 上的 Claude Desktop 仅限于该模型的知识截止日期,不支持网络搜索。在本文中,我们将逐步介绍如何使用 Amazon Bedrock AgentCore 网关将 Claude Desktop 连接到网络搜索,通过 AWS IAM Identity Center 和 Amazon Cognito 进行基于 JWT 的入站身份验证。

    英文原文

    Claude Desktop on Amazon Bedrock is limited to the model's knowledge cutoff without web search. In this post, we walk through connecting Claude Desktop to Web Search using Amazon Bedrock AgentCore Gateway, with JWT-based inbound authentication through AWS IAM Identity Center and Amazon Cognito.

  3. AWS Machine Learning Blog

    Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

    中文摘要

    微调可以教会一个小型搜索代理你的工具和环境,使其在更低的延迟和成本下,具备前沿模型的可靠性。在本文中,我们使用多轮强化学习(MTRL)在Amazon SageMaker AI上对一个由大型语言模型驱动的搜索代理进行微调,并分享我们在检索质量和可靠性方面测量到的提升。

    英文原文

    Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured in retrieval quality and reliability.

  4. AWS Machine Learning Blog

    Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

    中文摘要

    了解AWS专业服务如何利用基于Amazon Bedrock AgentCore的多智能体框架,实现企业级云迁移的端到端自动化。专门构建的AI智能体负责发现、基础设施即代码生成、组合治理以及迁移后操作,将IaC开发时间从数周缩短至数分钟。

    英文原文

    Learn how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Purpose-built AI agents handle discovery, infrastructure as code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.

  5. AWS Machine Learning Blog

    Serve live, governed data in AI-built apps with Amazon Quick

    中文摘要

    在 Amazon Quick 中,使用实时数据的应用程序,AI 构建的应用程序会实时查询您受控的 Quick Sight 数据集,而不是静态的、在构建时拍摄的快照。每次查询都会以查看应用程序的人员身份运行,因此行级和列级的安全性会根据每个读者进行应用。了解如何使用自然语言构建、发布和共享实时数据应用程序。

    英文原文

    With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so row-level and column-level security apply per reader. Learn how to build, publish, and share a live-data app using natural language.

  6. AWS Machine Learning Blog

    Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors

    中文摘要

    了解如何在NVIDIA NeMo Agent Toolkit(NAT)中使用Amazon S3向量作为持久化内存层,该工具在Amazon Elastic Kubernetes Service(Amazon EKS)上部署。本文将展示NAT的内存子系统的工作原理,并通过一个多元代理投资研究的用例,说明如何使用Amazon S3向量作为自定义内存提供程序。

    英文原文

    Learn how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS). This post shows how NAT's memory subsystem works and how to implement Amazon S3 Vectors as a custom memory provider, using a multi-agent investment research use case.

  7. AWS Machine Learning Blog

    Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows

    中文摘要

    环境代理会响应事件,例如 Amazon S3 上传、计划任务或警报,而不是等待聊天提示。本文将介绍如何使用 Amazon Bedrock AgentCore 在 Amazon SQS、AWS Lambda 和 Amazon DynamoDB 上构建与框架无关的环境代理,仅使用一个 ask_human 工具和一个用于人工审核的作业页面。

    英文原文

    Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore using Amazon SQS, AWS Lambda, and Amazon DynamoDB, with a single ask_human tool and a Jobs page for human-in-the-loop review.

  8. AWS Machine Learning Blog

    Implementing Multi-Environment Access for Claude Platform on AWS

    中文摘要

    了解如何通过单一订阅在AWS上配置对Claude平台的安全、多环境访问:跨账户SigV4用于AWS工作负载,开发人员的工作区作用域API密钥,以及外部环境的OIDC联合,所有操作都在专用AI服务账户中实现工作区级别的隔离。

    英文原文

    Learn how to configure secure, multi-environment access to Claude Platform on AWS from a single subscription: cross-account SigV4 for AWS workloads, workspace-scoped API keys for developers, and OIDC federation for external environments, with workspace-level isolation in a dedicated AI Services account.

  1. AWS Machine Learning Blog

    Query claims in natural language with Amazon Bedrock Knowledge Bases

    中文摘要

    这个技术教程在 Amazon Bedrock 知识库上构建了一个对话式声明助手,该助手能够用引用的方式回答自然语言问题。本文档涵盖了从 Amazon S3 导入声明文档、使用 AgenticRetrieveStream API 进行查询、多轮跟进、元数据过滤以及上下文基础的防护措施。

    英文原文

    This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations. It covers ingesting claim documents from Amazon S3, querying with the AgenticRetrieveStream API, multi-turn follow-ups, metadata filters, and contextual grounding guardrails.

  2. AWS Machine Learning Blog

    Build a multi-agent music production pipeline on Amazon Bedrock AgentCore Runtime Instances

    中文摘要

    Amazon Bedrock AgentCore 运行时实例提供多智能体工作流,使用 AWS 管理的带有 GPU 的 EC2 基础设施,具有持久化卷和多天会话。在本文中,我们部署了一个三智能体的音乐制作流程,这些智能体在同一块 GPU 实例上共存,共享文件系统,并相互传递工作以生成最终的音乐作品。

    英文原文

    Amazon Bedrock AgentCore Runtime Instances gives multi-agent workflows AWS managed EC2 infrastructure with GPUs, persistent volumes, and multi-day sessions. In this post, we deploy a three-agent music production pipeline where the agents colocate on one GPU instance, share a filesystem, and hand work to each other to produce a finished track.

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