In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance for inference workloads. In Part 2, we discuss enhancements made to observability, model customization, and model hosting.  ( 113 min )

In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate a whole new class of customer use cases to be hosted on SageMaker AI.  ( 111 min )

In this post, you’ll use a six-step checklist to build a new MCP server or validate and adjust an existing MCP server for Amazon Quick integration. The Amazon Quick User Guide describes the MCP client behavior and constraints. This is a “How to” guide for detailed implementation required by 3P partners to integrate with Amazon Quick with MCP.  ( 112 min )

In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service.  ( 115 min )

In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication.  ( 110 min )

In this post, we demonstrate how to build unified intelligence systems using Amazon Bedrock AgentCore through our real-world implementation of the Customer Agent and Knowledge Engine (CAKE).  ( 115 min )

In this post, we present a comprehensive evaluation framework for Amazon agentic AI systems that addresses the complexity of agentic AI applications at Amazon through two core components: a generic evaluation workflow that standardizes assessment procedures across diverse agent implementations, and an agent evaluation library that provides systematic measurements and metrics in Amazon Bedrock AgentCore Evaluations, along with Amazon use case-specific evaluation approaches and metrics.  ( 116 min )

The release of Anthropic Claude Sonnet 4.5 in the AWS GovCloud (US) Region introduces a straightforward on-ramp for AI-assisted development for workloads with regulatory compliance requirements. In this post, we explore how to combine Claude Sonnet 4.5 on Amazon Bedrock in AWS GovCloud (US) with Claude Code, an agentic coding assistant released by Anthropic. This […]  ( 110 min )

Today, we are announcing three new capabilities that address these requirements: proxy configuration, browser profiles, and browser extensions. Together, these features give you fine-grained control over how your AI agents interact with the web. This post will walk through each capability with configuration examples and practical use cases to help you get started.  ( 111 min )

In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight.  ( 120 min )

In this post, we provide you with a comprehensive approach to achieve this. First, we introduce a context message strategy that maintains continuous communication between servers and clients during extended operations. Next, we develop an asynchronous task management framework that allows your AI agents to initiate long-running processes without blocking other operations. Finally, we demonstrate how to bring these strategies together with Amazon Bedrock AgentCore and Strands Agents to build production-ready AI agents that can handle complex, time-intensive operations reliably.  ( 117 min )

Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart.  ( 108 min )

This post shows you how to implement robust error handling strategies that can help improve application reliability and user experience when using Amazon Bedrock. We'll dive deep into strategies for optimizing performances for the application with these errors. Whether this is for a fairly new application or matured AI application, in this post you will be able to find the practical guidelines to operate with on these errors.  ( 116 min )

This post shows you how to implement intelligent notification filtering using Amazon Bedrock and its gen-AI capabilities. You'll learn model selection strategies, cost optimization techniques, and architectural patterns for deploying gen-AI at IoT scale, based on Swann Communications deployment across millions of devices.  ( 111 min )

LinqAlpha is a Boston-based multi-agent AI system built specifically for institutional investors. The system supports and streamlines agentic workflows across company screening, primer generation, stock price catalyst mapping, and now, pressure-testing investment ideas through a new AI agent called Devil’s Advocate. In this post, we share how LinqAlpha uses Amazon Bedrock to build and scale Devil’s Advocate.  ( 115 min )

In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.  ( 112 min )

Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. Through its partnership with AWS, Iberdrola implemented different agentic architectures using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization.  ( 112 min )

Amazon Nova Sonic delivers real-time, human-like voice conversations through the bidirectional streaming interface. In this post, you learn how Amazon Nova Sonic can solve some of the challenges faced by cascaded approaches, simplify building voice AI agents, and provide natural conversational capabilities. We also provide guidance on when to choose each approach to help you make informed decisions for your voice AI projects.  ( 110 min )

This blog post dives deeper into the implementation architecture for the Automated Reasoning checks rewriting chatbot.  ( 110 min )

In this post, we show how this integrated approach transforms enterprise LLM fine-tuning from a complex, resource-intensive challenge into a streamlined, scalable solution for achieving better model performance in domain-specific applications.  ( 118 min )

Working with the Generative AI Innovation Center, New Relic NOVA (New Relic Omnipresence Virtual Assistant) evolved from a knowledge assistant into a comprehensive productivity engine. We explore the technical architecture, development journey, and key lessons learned in building an enterprise-grade AI solution that delivers measurable productivity gains at scale.  ( 113 min )

In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration.  ( 113 min )

This post walks through how agent-to-agent collaboration on Amazon Bedrock works in practice, using Amazon Nova 2 Lite for planning and Amazon Nova Act for browser interaction, to turn a fragile single-agent setup into a predictable multi-agent system.  ( 111 min )

Today, we're announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples.  ( 111 min )

In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices.  ( 115 min )

In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.  ( 125 min )

Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost savings while maintaining operational excellence. The document classification system, developed using the Generative AI Intelligent Document Processing (GenAI IDP) Accelerator, is designed to integrate seamlessly into existing workflows. It revolutionizes how employees interact with document management systems by reducing the time spent on manual classification tasks.  ( 111 min )

In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications.  ( 110 min )

Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand guidelines, deliver consistent content, and enhance the effectiveness and efficiency of new campaign creation.  ( 116 min )

BGL is a leading provider of self-managed superannuation fund (SMSF) administration solutions that help individuals manage the complex compliance and reporting of their own or a client’s retirement savings, serving over 12,700 businesses across 15 countries. In this blog post, we explore how BGL built its production-ready AI agent using Claude Agent SDK and Amazon Bedrock AgentCore.  ( 113 min )

In this post, we demonstrate how to build a secure file upload solution by integrating Google Drive with Amazon Quick Suite custom connectors using Amazon API Gateway and AWS Lambda.  ( 113 min )

This post explores nine essential best practices for building enterprise AI agents using Amazon Bedrock AgentCore. Amazon Bedrock AgentCore is an agentic platform that provides the services you need to create, deploy, and manage AI agents at scale. In this post, we cover everything from initial scoping to organizational scaling, with practical guidance that you can apply immediately.  ( 121 min )

On November 21, 2025, Amazon SageMaker introduced a built-in data agent within Amazon SageMaker Unified Studio that transforms large-scale data analysis. In this post, we demonstrate, through a detailed case study of an epidemiologist conducting clinical cohort analysis, how SageMaker Data Agent can help reduce weeks of data preparation into days, and days of analysis development into hours—ultimately accelerating the path from clinical questions to research conclusions.  ( 112 min )

Pupdate The New Year had barely begun and we had a cold snap and some snow. Milo’s back in remission thankfully, though there have been a few hiccups with his treatment this time around. Some of that’s expected (low neutrophils), but the vets struggling to get canulas in due to vein scarring is new and […]  ( 14 min )

Pupdate The New Year had barely begun and we had a cold snap and some snow. Milo’s back in remission thankfully, though there have been a few hiccups with his treatment this time around. Some of that’s expected (low neutrophils), but the vets struggling to get canulas in due to vein scarring is new and […]  ( 14 min )

After previous trips to The Three Valleys and Espace Killy, Paradiski felt like a way to complete the set of long established multi location French ski areas. Inghams again Having organised the last few trips with Inghams, they were where I looked first, and in the end the provider I chose. Getting there Another flight […]  ( 17 min )

After previous trips to The Three Valleys and Espace Killy, Paradiski felt like a way to complete the set of long established multi location French ski areas. Inghams again Having organised the last few trips with Inghams, they were where I looked first, and in the end the provider I chose. Getting there Another flight […]  ( 17 min )

In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities--when requested or for urgent cases--and an analytics pipeline for performance insights.  ( 116 min )

View on sreweekly.com SRE Weekly will be going on hiatus for 6 weeks, while I’m on leave caring for my partner after her kidney transplant surgery this week. It’s incredible that the National Kidney Registry’s Paired Exchange program allowed me to donate a kidney to help her even though we don’t have matching blood types! […]  ( 4 min )

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 generation, […]  ( 117 min )

This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment.  ( 111 min )

In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you'll have a working implementation of global cross-Region inference in af-south-1.  ( 111 min )

The agent-based approach we present is applicable to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To demonstrate these concepts in action, we walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to various content review needs by adjusting the agent configurations, tools, and verification sources.  ( 110 min )

In this post, we walk you through Pushpay's journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS.  ( 113 min )

This blog post demonstrates how to build an intelligent contract management solution using Amazon Quick Suite as your primary contract management solution, augmented with Amazon Bedrock AgentCore for advanced multi-agent capabilities.  ( 111 min )

In this post, we discuss how to use AppSync Events as the foundation of a capable, serverless, AI gateway architecture. We explore how it integrates with AWS services for comprehensive coverage of the capabilities offered in AI gateway architectures. Finally, we get you started on your journey with sample code you can launch in your account and begin building.  ( 118 min )

This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock.  ( 111 min )

View on sreweekly.com A message from our sponsor, incident.io: incident.io lives inside Slack and Microsoft Teams, breaking down emergencies into actionable steps to resolution. Alerts auto-create channels, ping the right people, log actions in real time, and generate postmortems with full context.Move fast when you break things and stay organized inside the tool your team already […]  ( 4 min )

功能存在 ≠ 用户知道 作为一位开了24,000公里的特斯拉车主，我对这辆车非常满意——操控精准如臂使指，安全性能令人安心，每一次出行都充满信心与愉悦。 但有一个细节，我一直耿耿于怀：在使用导航时，只要打转向灯，屏幕上就会弹出摄像头监控画面。偏偏这个画面会遮挡关键的导航地图信息，尤其在复杂路口，常常让我措手不及，差点错过转弯。 我一度以为这是无法更改的设计缺陷，直到昨天坐朋友的车，才恍然大悟——原来这个监控窗口是可以移动的！ 朋友轻描淡写地说：“你只要长按那个画面，就能拖到左边。” 果然，一试就成。窗口移到左侧后，我用余光就能看清盲区，再也不用扭头或担心挡住导航路线。 就这么一个小小的交互操作，竟能让驾驶体验提升一大截。可笑的是，我开了两万多公里，竟从未发现。 这让我想起早年用微信的经历：想在一段文字中间插入几个字，总是习惯性一路回删重打。直到有朋友告诉我，“长按空格键，光标就能自由移动”——那一刻，我才意识到自己错过了多少效率。 这类“长按拖动”“长按移动光标”的操作，本质上属于隐式交互（Implicit Interaction），也常被称作“专家模式”：功能确实存在，但没有明确引导，普通用户很难主动发现。 功能存在 ≠ 用户知道 在产品工作中，我们常常陷入一种“知识的诅咒”： 我们知道功能在哪、怎么用、为什么重要、能解决什么问题。 但——用户不知道。 这不是一句调侃，而是产品设计中最容易被忽视、却最值得警惕的真相。 很多团队都经历过这样的场景：用户提出一个需求，你心里立刻冒出一句：“这功能我们早就做了啊！” 你甚至能立刻指出入口、按钮样式和操作路径。 可用户依然一脸茫然。 这不是用户的问题，而是产品的问题。 当用户“重复提需求”，其实是可用性在报警 经常和用户交流的好处，不只是获取新点子，更在于你会听到他们反复提出一些“早已存在”的功能需求。 这时，请别急着纠正：“这个我们早就有了！” 相反，这恰恰是一个暴露可用性问题的信号。 为什么用户找不到已有功能？常见原因包括： 入口不明显：藏得太深，或缺乏视觉提示； 命名不直观：术语专业，但不符合用户语言； 路径不符合自然操作流：用户想A，你却让他先做B再做C； 功能与用户的心智模型错位：你以为他需要”监控画面”，他只关心”别挡住导航”。 真正有价值的反馈，往往藏在“已实现的需求”里 很多团队热衷于听“新需求”——因为那意味着可以开发新功能、增加亮点、拓展边界。 但真正高价值的反馈，往往是那些“你已经做过，但用户没发现”的需求。 为什么？因为它们揭示的是更深层的问题： 可用性缺陷 信息架构混乱 命名与认知偏差 用户行为路径与设计预期脱节 这些问题比“缺功能”更隐蔽，却对体验影响更大。一个没人能找到的功能，本质上等于没有上线。 结语：功能上线 ≠ 价值实现 只有当用户理解、发现并顺利使用时，功能才算真正“上线”。 产品设计的终极目标，不是堆砌功能，而是让价值被看见、被感知、被轻松获取。 下次再听到用户提出“已有功能”的需求时，请别急着解释—— 不妨问问自己：我们的设计，是否真的站在了用户的视角？  ( 1 min )

Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner.  ( 109 min )

In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock.  ( 114 min )

PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. In this post, we walk through the PDI Intelligence Query (PDIQ) process flow and architecture, focusing on the implementation details and the business outcomes it has helped PDI achieve.  ( 114 min )

In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process.  ( 108 min )