AWS Machine Learning Blog

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how to configure rate limits on Amazon Bedrock AgentCore gateway to enforce per-user and per-target traffic controls. Define request, token, and connection limits scoped by JWT claims or IAM identity to protect downstream models, tools, and agents from traffic spikes.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：As engineering teams adopt coding agents like Codex, leaders need visibility into adoption, consumption, and reliability. This post shows how to route Codex OpenTelemetry metrics through a local collector to Amazon CloudWatch for an AWS native view of usage by user, team, and cost center.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A regulated customer needed all Claude Code inference processed in a single AWS Region (London), not just in-geography. This post shows two ways to pin Claude Code on Amazon Bedrock to one Region: an application inference profile or the Mantle endpoint, paired with an IAM Region condition, plus how to verify compliance in AWS CloudTrail.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommendations, and deploy the recommended configuration without leaving your notebook workflow.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In this post, we'll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI agents on Amazon Bedrock AgentCore run in the cloud, but users' tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock AgentCore harness is now generally available. Learn how to add it as an agent step in n8n workflows using a new open-source community node, and build agents with persistent memory, real tools, code execution, and VPC isolation — all from the n8n editor with no infrastructure or agent code.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Today, we are introducing the general availability of Web Search on Amazon Bedrock. It is a server-side built-in tool that grounds model responses in current web knowledge. With Web Search, grounding becomes a native capability of Amazon Bedrock, with no third-party vendors to onboard, no external APIs to orchestrate, and no additional third party vendor security reviews to conduct. In this post, we walk through what Web Search on Amazon Bedrock is, why it matters, how to enable it using the OpenAI Responses API, and how to get started with the tool.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Extracting insights from dozens of websites by hand quickly becomes overwhelming. This post shows how to build an automated web insight extraction solution with Amazon Bedrock AgentCore Browser, Amazon Bedrock, Amazon OpenSearch Serverless, and AWS Lambda that monitors RSS feeds, renders pages reliably, and makes AI-extracted insights searchable.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock now supports automatic Automated Reasoning policy refinement. The refinement engine diagnoses failing tests and proposes formal-logic fixes for rule issues and language issues, and you approve every change before it takes effect. This post walks through both refinement modes with complete API and console workflows.

Amazon Quick 宣布推出 Agentic Catalog Experience（预览版），这是一种 AI 驱动的工作流，帮助数据管理员通过自然语言发现上游目录资产，并自动创建数据集和主题，同时继承上游语义。该体验旨在消除数据发现和语义重建的最后一公里瓶颈，将数据准备时间从数周缩短到分钟，首批支持 AWS Glue Data Catalog 和 Databricks Unity Catalog。

本文介绍了在AWS上部署Moonshot AI的2.8万亿参数开源MoE模型Kimi K3的两种方法：使用Amazon SageMaker HyperPod或Amazon EKS。需要p6-b300实例（8块B300 GPU）和预留容量，通过vLLM提供OpenAI兼容的推理端点。

本文展示了Yahoo如何利用Amazon Bedrock与生成式AI增强其搜索重定向（SRT）功能，显著提升关键词扩展的语义相关性和受众覆盖范围。

了解如何使用 Amazon Quick 为 Amazon SageMaker AI 端点构建推理元监控系统。这一治理层位于生产机器学习推理管道之上，持续跟踪预测和数据质量、检测漂移、整合延迟的真实值，并提供自动化的性能仪表板。

OpenAI GPT-5.6 Sol、Terra 和 Luna 模型已在 Amazon Bedrock 上正式可用，同时引入了显式提示缓存功能，可精确控制提示的缓存和重用部分。本文介绍了如何开始使用、设置显式缓存以及迁移现有 GPT 工作负载以降低推理成本。

Amazon Bedrock高级提示优化功能可同时针对最多5个模型优化提示，并比较原始与优化后的性能（质量、延迟和成本）。该功能旨在将数天到数周的手动迭代工作缩短为几分钟的引导式流程，支持模型迁移和当前模型改进。

Amazon Bedrock AgentCore Identity 现在支持私钥 JWT 客户端身份验证，使代理能够使用签名的 JWT 而不是共享密钥向身份提供商进行身份验证。私钥安全地存储在 AWS KMS 中，公钥注册到身份提供商。本文解释了工作流程、支持的授权流程（M2M、OBO、用户委托），并提供了详细的配置步骤，包括创建 KMS 签名密钥、注册公钥以及设置凭证提供程序。还展示了用于审计的 CloudTrail 事件示例。

本文介绍了如何通过Amazon Bedrock AgentCore实现自主、跨系统的商业智能，无需自定义代码。利用预构建的MCP服务器连接器、细粒度访问控制和持久内存，企业可以用自然语言查询多个数据源，同时自动执行基于角色的边界。

了解如何使用 Amazon Quick 构建无代码的客户留存管道，通过通话记录和 CSAT 数据识别有流失风险的客户，利用自定义 MCP Action 按留存优先级评分，并生成个性化留存信函，将响应时间从几天缩短到几分钟。