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Hand-picked stories worth reading right now86 articles found
How Yahoo enhances search retargeting using Amazon Bedrock
In this post, we demonstrate how Yahoo implemented Amazon Bedrock to enhance their Search Retargeting (SRT) capabilities in the Yahoo DSP ad tech suite. SRT is a core audience targeting solution that helps advertisers reach users based on their historical search behavior, bridging search intent with display, video, and native advertising. Beyond targeting keywords entered on Yahoo Search, SRT uses AI to identify and engage users who demonstrate intent through search activity both on Yahoo and across integrated partner systems.
Introducing explicit prompt caching for OpenAI GPT-5.6 models on Amazon Bedrock
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost.
Authenticate with Private Key JWT using Amazon Bedrock AgentCore Identity
This post explains how Private Key JWT client authentication works in AgentCore Identity and reviews the supported grant flows. We then walk through creating an AWS KMS signing key, registering its public key with your identity provider, configuring a credential provider on the AWS Management Console, and reviewing example AWS CloudTrail events that record your agent's access.
Generate Autonomous Business Insights with AI Agent and MCP Servers
Learn how Amazon Bedrock AgentCore delivers autonomous, cross-system business intelligence through configuration rather than custom code. Using pre-built MCP server connectors, fine-grained access control, and persistent memory, enterprises can query multiple data sources with natural language while enforcing role-based boundaries automatically.
Automating customer retention workflows in Amazon Quick
Learn how to build a no-code customer retention pipeline in Amazon Quick that detects at-risk customers from call transcripts and CSAT data, scores them by retention priority with a custom MCP Action, and generates personalized retention letters, reducing response time from days to minutes.
Market surveillance agent with LangGraph and Strands on AgentCore
Learn how to architect and deploy a production-ready multi-agent AI system using LangGraph for workflow orchestration and Strands for agent reasoning on Amazon Bedrock AgentCore. This post walks through a market surveillance example with state-driven orchestration, checkpoint-based recovery, and AgentCore memory and observability.
Deepgram enhances Amazon SageMaker AI support with AWS IAM Temporary Delegation
In this post, we cover why Deepgram built on IAM temporary delegation, how the integration works end-to-end, and what it unlocks for customers running Deepgram speech models on SageMaker AI. With this integration, Deepgram has reduced the time for initial investigation on a SageMaker AI support ticket from days to minutes.
Introducing Claude Opus 5 on AWS: Anthropic's most capable Opus model
This post covers Opus 5's improvements and practical guidance for AI engineers integrating the model into agentic systems and production inference workloads on Amazon Bedrock. See the documentation for Claude Platform on AWS.
AI Teammates: how monday.com runs production AI agents on Amazon Bedrock
AI Teammates are agentic AI on Amazon Bedrock, and few engineering organizations run them in production at the scale that monday.com does. Nine in ten Builders use AI coding tools every month, up from roughly half a year ago. Per-engineer PR throughput is up by more than half. Every figure in this post comes from monday's own internal production data. In this post, we share the architecture behind those numbers, the retrofits that made it work in a decade-old code base, and the confidence-scored merge play closing the gap to full autonomy.
Build specialized agent workflows for your business with Amazon Quick and NVIDIA NeMo Agent Toolkit
In this post, we show how Amazon Quick can serve as the business-user front door for specialized agent workflows. We use the NVIDIA NeMo Agent Toolkit to build a supply-chain risk example that helps a planner move from an Amazon Quick dashboard and knowledge context to a guided mitigation recommendation.
Transform your sales organization with Amazon Quick: your new agentic AI teammate
In this post, we walk through a few ways that Quick delivers on this promise. We cover the entire sales cycle, from identifying your highest-priority prospect, contacting them, working the deal to close, and keeping the CRM up to date as the account matures, while protecting your scarcest resource: your time.
Introducing Mobile Layout for Amazon Quick dashboards
Teams that rely on dashboards for daily decisions often must pinch and zoom to interact with controls originally designed for larger displays. Checking revenue during a morning standup, reviewing pipeline metrics between meetings, or monitoring operations while traveling all require extra effort when the dashboard was built for a desktop screen. Mobile Layout for Amazon [...]
Introducing Grok on Amazon Bedrock
This post covers what makes Grok 4.3 a great fit for agentic and enterprise workloads, how you access it through Amazon Bedrock, and how to use the capabilities most teams reach for first: a basic chat request, configurable reasoning effort, tool calling, structured output, image input, and stateful multi-turn conversations.
Building a restaurant telephony AI host with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic
In this post, we show you how to build a voice ordering system that answers a phone number and takes the order from greeting to confirmation. The system uses Amazon Bedrock AgentCore to host and run the agent and Amazon Nova 2 Sonic for real-time speech, connected to a restaurant backend through the Model Context Protocol (MCP). The walkthrough covers deploying the full stack with AWS Cloud Development Kit (AWS CDK) and bridging a phone call into the agent through a Session Initiation Protocol (SIP) gateway on Amazon Elastic Container Service (Amazon ECS) and AWS Fargate. It also warms the age
AWS adds AI-assisted product listing service to its Marketplace portfolio
Product listings in AWS Marketplace gained new AI-based features last month in anticipation of continued growth in the use of enterprise agents. Amazon Web Services Inc. unveiled AI-assisted product listings in Product Assistant chat, a feature that helps independent software vendors and consulting partners develop comprehensive product listings for AWS Marketplace using existing digital assets. [...] The post AWS adds AI-assisted product listing service to its Marketplace portfolio appeared first on SiliconANGLE.
Agentic vision: Building visual intelligence with Amazon Bedrock and MCP servers
In this post, we walk you through the Computer Vision MCP Server, which illustrates this approach, representing how AI systems can process visual information and make intelligent decisions through a single, standardized interface. This convergence transforms what was once a complex integration challenge into a streamlined process, making AI capabilities accessible to a broader range of applications and developers.
Monitor Amazon SageMaker Pipelines cross-account with custom Amazon CloudWatch dashboards
In this post, we present a solution designed to centralize the monitoring of SageMaker Pipelines across AWS accounts and Regions using Amazon CloudWatch custom dashboards. The accompanying GitHub repository provides a customizable AWS Cloud Development Kit (AWS CDK) example of the required infrastructure.
Scaling UX testing with Amazon Nova Act: A new approach to user flow analysis
Using generative AI enables parallel execution of comprehensive user flow testing at scale. This solution demonstrates how to build a cloud-deployed UX testing platform that automatically generates test scenarios from documentation, executes user flows at scale using the intelligent navigation capabilities of Nova Act, and provides actionable insights through automated analysis.
Implement on-behalf-of token exchange for multi-tenant agents with Amazon Bedrock AgentCore Gateway
Building multi-tenant agents with Amazon Bedrock AgentCore and Apply fine-grained access control with Bedrock AgentCore Gateway interceptors establish the conceptual foundation for on-behalf-of (OBO) token exchange in agentic systems. This post is the implementation guide. It walks through a complete multi-tenant OBO setup against Okta, shows the JSON Web Token (JWT) claim transformations on each hop, and demonstrates how audience binding produces defense in depth that scales across tenants.
Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore
In this post we show how to build a semantic layer on AWS using Stardog's Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without extract, transform, and load (ETL). The same Stardog deployment works behind AWS computes (Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS Lambda). We use AgentCore here because it bundles inbound auth, hosting, and tool credentials into one managed service
Scaling agentic workflows with native case management in Amazon Quick Automate
In this post, we show you how to combine case management with agentic automation capabilities in Quick Automate. We introduce case management and explore the lifecycle of cases in an agentic workflow from case creation through processing to resolution. We cover how to create and manage single or multiple cases, automatically track and update status, handle exceptions, and incorporate Human-in-the-loop (HITL) steps within workflows. We also show the case creator-processor pattern that enables dynamic scaling. Finally, we walk through how to structure case management for enterprise processes, in
Deploying quantized models on Amazon SageMaker AI with Unsloth
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure. The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct instance access, Amazon SageMaker AI inference endpoints for managed serving, and Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) when inference needs to fit into an existing container framework. You also learn operational practices for production deployments.
How KTern.AI built agentic AI for SAP on Amazon Bedrock AgentCore
Evolving from a traditional software as a service (SaaS) platform into a next-generation agentic AI platform meant orchestrating multiple specialized agents across long-running enterprise programs. Each agent operates with persistent context, secure tool access, and production-grade reliability. We built that system on Amazon Bedrock AgentCore using the Strands Agents SDK. This post walks through how we architected it, which agents we built, and the outcomes for our customers.
Enhancing enterprise inference on Amazon SageMaker HyperPod with data capture, Hugging Face, NVMe, and Route 53 integration
In this post, we walk through five capabilities now available in SageMaker HyperPod inference: multi-tier data capture for auditing and model improvement, direct deployment from Hugging Face Hub, local NVMe model loading for faster cold starts, automated Route 53 DNS for custom domains, and pod-level IAM through custom service accounts.
Introducing Claude apps gateway for AWS
Today, we're announcing the Claude apps gateway for AWS, a self-hosted control plane that gives organizations a single point of control over access, cost, and policy for Claude Code and Claude Desktop. In this post, we show how to set up and run Claude apps gateway for AWS with Amazon Bedrock and Claude Platform on AWS.
Building and connecting a production-ready ecommerce MCP server using Amazon Bedrock AgentCore and Mistral AI Studio
In this post, you build and connect that server end to end. You will implement MCP tools, set up two-layer JSON Web Token (JWT) authentication, deploy with AWS Cloud Development Kit (AWS CDK), and connect the result to Mistral AI's Vibe. The post also covers prerequisites, solution architecture, best practices for MCP servers and Vibe connectors, and resource cleanup. The ecommerce server that you build supports product search, order placement, review submission, and returns processing using Amazon DynamoDB for data and Amazon Cognito for identity management.
Securing Amazon Bedrock AgentCore Runtime with AWS WAF
This post shows you two architecture patterns that address this problem. Both use an internet-facing ALB with AWS WAF and route traffic through a VPC Interface Endpoint to AgentCore Runtime. Pattern 1 places an AWS Lambda proxy between the ALB and the VPC Endpoint, giving you full control over request transformation. Pattern 2 targets the VPC Endpoint ENI IP addresses directly from the ALB, removing the Lambda hop entirely. You also learn how to close the direct-access backdoor with a resource policy so that traffic flows through AWS WAF only. Both patterns have been tested end-to-end with Sig
Build a serverless image editing agent with Amazon Bedrock AgentCore harness
This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image editing tools, and a React frontend, with a single deployment command. The infrastructure is defined using AWS Cloud Development Kit (AWS CDK).
Monitoring discriminative ML models using Amazon SageMaker AI with MLflow
Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in MLflow, scale through pipelines, and trigger drift notifications.
Build an AI-powered AWS support companion with Amazon Bedrock AgentCore
In this post, you build an AWS Support Companion using Amazon Bedrock AgentCore. The agent uses Strands Agents as the orchestration framework and connects to AWS services through the Model Context Protocol (MCP). By the end, you have a working agent that can analyze CloudWatch logs, search AWS documentation, query community knowledge from AWS re:Post, and create support cases, all from a single conversational interface. The solution deploys with a single script using AWS CloudFormation and includes a web frontend built on AWS Amplify for interacting with the agent.
Why cash on delivery still works in Europe [Sponsored]
In a market that has spent a decade moving to cards, digital wallets and one-click checkout, cash on delivery can look like a relic. The customer pays the courier at the door, only once the parcel is ...
Teaching models to forget: Selective unlearning with Amazon Nova
In this post, we introduce Reverse Direct Preference Optimization (rDPO), the novel unlearning technique behind Amazon Nova Customizable Content Moderation Settings (CCMS), and show how it reduces over-deflection while preserving model quality. We also provide pointers for customers who want to apply these preference optimization techniques to their own experiments.
Run MiniMax models on Amazon Bedrock
In this post, we walk through how to get started with MiniMax models on Amazon Bedrock, including the capabilities supported by these models, the service tiers available, how on-demand inference scales to handle your workloads, and the different APIs you can use to access them. Using these models, customers can build agentic applications, long-context document analysis pipelines, and software engineering workflows, all backed by the security and operational guarantees of AWS.
Automatically redact PII in images with Amazon Nova
In this post, we present a multi-step pipeline directed by Amazon Nova, which uses its contextual vision reasoning to coordinate complementary tools, including Meta's open-source Segment Anything Model (SAM 3) deployed on Amazon SageMaker AI for pixel-level segmentation, and Amazon Textract for optical character recognition (OCR). This pipeline is designed to provide comprehensive and compliant PII redaction even for challenging edge cases such as fingerprints, ID cards, or license plates in arbitrary orientations.
How Amazon Bedrock catches AI-generated phishing
Social engineering through phishing remains one of the most common tactics for launching cyberattacks. AI-generated phishing email messages now pose a new challenge for security teams managing email systems, significantly raising the risk because of their advanced sophistication. Modern social engineers use generative AI and open source intelligence (OSINT) to craft thousands of unique messages [...]
Run NVIDIA Nemotron and OpenAI GPT OSS models on Amazon Bedrock in AWS GovCloud (US)
We're excited to introduce US-based frontier open-weight models in AWS GovCloud (US). With this release, Amazon Bedrock now supports OpenAI's open-weight GPT OSS models (120B and 20B) and NVIDIA Nemotron (Nano 9B v2, Nano 12B v2, Nano 30B, Super 120B) models. In this post, we cover these models and their capabilities, the inference options for data residency, the available service tiers and how to get started.
HippoRAG: Neurobiologically inspired RAG using Amazon Bedrock, Amazon Neptune, and personalized PageRank
In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality, Amazon Neptune Analytics for advanced graph algorithms including Personalized PageRank, and Amazon Titan Embeddings for vector representations. This implementation showcases how to build and deploy HippoRAG within AWS infrastructure for enterprise-scale applications.
Simplify model selection in Amazon Bedrock with the open source Model Profiler
The Amazon Bedrock Model Profiler is an open source tool that aggregates model metadata from multiple AWS APIs and external sources into a single, searchable interface. In this post, you'll learn what the Model Profiler provides, the real-world scenarios it supports, and how to deploy it in your own environment in under five minutes.
Accelerate protein design with BoltzGen on Amazon SageMaker AI
In this post, we demonstrate how to deploy BoltzGen on SageMaker AI and run an end-to-end protein design experiment. By the end of the walkthrough, you have a working setup that scales from quick validation runs to production batch processing. The setup offers two execution modes for different stages of research and uses step-level caching to reduce compute expenses during iterative workflows.
Introducing Claude Sonnet 5 on AWS: Anthropic's most capable Sonnet model
Today, we're excited to announce the availability of Anthropic's most advanced Sonnet model, Claude Sonnet 5, on Amazon Bedrock and Claude Platform on AWS. Claude Sonnet 5 is the first Sonnet model of Anthropic's latest generation and represents a meaningful step forward. It delivers top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional [...]
Build generative UI for AI agents on Amazon Bedrock AgentCore with the AG-UI protocol
This post walks through how AG-UI integrates into the Fullstack AgentCore Solution Template (FAST) to build interactive agent frontends on Amazon Bedrock AgentCore. We then show how CopilotKit extends this with generative UI, shared state, and human-in-the-loop interactions, all deployed on Amazon Bedrock AgentCore.
Implementing resilience patterns with Amazon Bedrock and LLM gateway
In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM gateway. These patterns address real-world challenges such as quota exhaustion during unexpected traffic surges, maximizing availability through geographic distribution of inference, and helping prevent noisy neighbor problems in multi-tenant environments.
Fine-tune Amazon Nova models for accurate email data extraction
In this post, you'll learn how fine-tuning Amazon Nova models using Amazon SageMaker AI addresses these specific issues by teaching the models to recognize your exact data patterns, distinguish between similar fields, and process information more efficiently-achieving up to 94.77% extraction accuracy while reducing costs 50%.
Implement a backup strategy for Amazon Quick Sight BI assets
In this post, we cover best practices for implementing an effective backup strategy for BI assets in Quick Sight. We start by covering the options for selecting the assets to include in your backup, then explain the high-level APIs available for that purpose, and finalize with sample code to help you get started quickly.
Debugging production agents with Amazon Bedrock AgentCore Observability
In this post, you learn how to debug production agent failures using built-in observability capabilities. We walk through common failure patterns, show how to analyze agent behavior with traces and metrics, and provide structured workflows for resolving issues such as infinite loops and tool invocation failures. This is Part 1 of a two-part series. Part 2 covers performance optimization and memory management.
Build interactive PDF text extraction from Amazon S3
In this post, you'll build a server that extracts text from PDF files in Amazon S3 in real time. This protocol-based approach provides programmatic document access. You'll walk through the architecture, set up the server, and run interactive document queries. Along the way, you'll compare this approach with Amazon Textract so you can decide which tool fits your workload.
Implementing super resolution by deploying SeedVR2 on Amazon SageMaker AI
In this post, we demonstrate how to implement video upscaling using SeedVR2 on SageMaker AI. We cover the solution architecture, walk through the deployment steps, and show performance comparisons that highlight the quality improvements and processing efficiency you can achieve. By the end of this post, you'll have the practical knowledge needed to implement this super resolution solution.
9 ways AI is reshaping enterprise operations: Key insights from AWS Summit NYC
The conversations at last week's AWS Summit NYC 2026 showed that AI evolution is entering a new phase. From physical robots tackling labor shortages to agentic systems reshaping enterprise operations, the focus is shifting from experimentation to practical deployment. TheCUBE's host, Gemma Allen, captured candid discussions with Amazon Web Services Inc. executives, partners and customers who are turning [...] The post 9 ways AI is reshaping enterprise operations: Key insights from AWS Summit NYC appeared first on SiliconANGLE.
Build a protein research copilot with Amazon Bedrock AgentCore
This post shows you how to build a conversational protein research assistant that combines three capabilities: Natural language query parsing to extract structured search parameters, vector similarity search over protein embeddings using a specialized language model and ai-generated scientific summaries of search results.
Five thoughts from Swami Sivasubramanian's keynote at AWS Summit and what it means for IT pros
When Amazon Web Services Inc. held its New York Summit last week, Vice President of Agentic AI Swami Sivasubramanian as usual was the headline act, delivering the opening keynote. Sivasubramanian made the case to enterprise leaders that the artificial intelligence conversation has moved beyond pilots and productivity hacks into a world where the real advantage lies in compounding [...] The post Five thoughts from Swami Sivasubramanian's keynote at AWS Summit and what it means for IT pros appeared first on SiliconANGLE.
Accelerate campaign workflow with insights from Adobe Marketing Agent for Amazon Quick
This post shows how to enable Adobe Marketing Agent for Amazon Quick using a Model Context Protocol (MCP). We walk you through how to configure the integration, authenticate using your Adobe credentials, and get the latest insights in Amazon Quick. The sample workflow returns audience rankings, loyalty segment summaries, journey usage, and conflict recommendations.
Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch
Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.
Amazon Bedrock AgentCore harness is now generally available: Go from idea to production-grade agent in minutes
Today, Amazon Bedrock AgentCore harness is generally available. Two API calls (CreateHarness to define an agent, and InvokeHarness to run it), and you have an agent running in seconds. The agent runs in its own isolated environment with a filesystem and shell, so it can read files, run commands, and write code safely. It remembers users and conversations across sessions, picks up skills you point it at (including the AWS-curated catalog), browses the web, calls your tools through gateway or MCP, and switches model providers mid-session without losing context. Every step streams back to you in
Parallelize speculative decoding with P-EAGLE on Amazon SageMaker AI
This post walks you through how to use P-EAGLE directly within Amazon SageMaker AI. It will demonstrate how to select a compatible model from the SageMaker JumpStart catalog, configure the parallel drafting specifications, and deploy a highly optimized real-time SageMaker AI endpoint to accelerate your generative AI applications.
Introducing Gemma 4 models on Amazon Bedrock
Today, we are announcing the availability of the Gemma 4 family on Amazon Bedrock. Built by Google DeepMind and released under the Apache 2.0 license, Gemma 4 is a family of open-weight models designed with a focus on intelligence-per-parameter across a broad range of deployment scenarios. The family includes three instruction-tuned variants: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B. These cover dense and mixture-of-experts (MoE) architectures, where only a fraction of the model's parameters activate per request. The variants offer built-in reasoning, native function calling, and multimod
Building Supercharger: How Rocket Close optimized title operations with agentic AI
Rocket Close, the title operations arm of the Rocket mortgage group, built an internal solution called Supercharger to optimize title operations using agentic AI. The system was built on Strands Agents, large language models, Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools. The AWS Machine Learning Blog post walks through the solution's features, the reasoning behind the technology stack, lessons the team learned, and the business impact at Rocket Close.
Build a meeting prep and follow-up assistant with Amazon Quick and Cisco Webex MCP servers
This AWS Machine Learning post describes how to build a custom meeting prep and follow-up assistant using Amazon Quick and Cisco Webex MCP servers. From a single prompt, the agent finds an upcoming Webex meeting, reviews prior meeting summaries and transcripts, and pulls related Vidcast highlights and transcript context. It then searches Webex message threads for unresolved follow-ups and creates a concise prep brief. After the meeting, the same assistant can summarize the discussion, identify action items, find related Vidcast updates, and draft a follow-up message for the right Webex space.
From PDFs to insights: Architecting an intelligent document processing pipeline with AWS generative AI services
This AWS Machine Learning Blog post describes how to build a cost-effective and scalable intelligent document processing pipeline on AWS, powered by Amazon Bedrock. It shows how Amazon Bedrock Data Automation (BDA) extracts and analyzes document content, while a Strands Agent hosted on Amazon Bedrock AgentCore Runtime coordinates specialized processing tasks, and Amazon Bedrock Knowledge Bases provide contextual understanding across multiple documents. The post argues that combining these capabilities in one architecture lets organizations transform document workflows with minimal development effort.
Extract Data with On-demand and Batch Pipelines Dynamically
AWS published an engineering guide for an intelligent document processing pipeline on Amazon Bedrock that lets a business choose speed or savings for each document. On-demand inference returns extracted fields within seconds for urgent work, while batch inference runs documents in bulk at a price 50% lower than the on-demand path. With parallelism turned on, the batch pipeline handles 1,000 documents within 15 minutes and uses Claude Sonnet 4 to read the pages.
Amazon's data centers used 2.5 billion gallons of water last year
Amazon disclosed for the first time how much water its data centers consume, reporting 2.5 billion gallons globally in 2025 at a rate of 0.12 liters per kilowatt-hour of electricity. The company says it cut water use by 2 percent at sites it owns and operates versus 2024 even while expanding, and it claims to run more water-efficiently than Microsoft, Google, and Meta. The disclosure landed days after Seattle passed a one-year data center moratorium, an effort some Amazon employees supported.
Spot trends faster, sort smarter: Unlocking Sparklines and Custom Sort in Amazon Quick
Amazon QuickSight, the AWS business intelligence tool, added two dashboard features aimed at non-technical decision makers: sparklines and custom sort for filter controls. Sparklines place a small inline trend line directly inside a table cell so a reader spots direction at a glance without opening a separate chart. Custom sort lets the person building a dashboard set the order of values inside dropdown and list filters instead of relying on default alphabetical order. Both features target teams that want dashboards to follow business logic rather than technical defaults.
Optimize blueprint extraction accuracy in Amazon Bedrock Data Automation
Amazon has added a feature to Bedrock Data Automation that automatically sharpens how its document extraction tool reads business paperwork. Called blueprint instruction optimization, it takes three to ten sample documents plus their correct answers and rewrites the extraction instructions to lift accuracy in minutes rather than weeks. No separate model training or fine-tuning is required, and the work runs through either the Amazon Bedrock console or an API.
I tried Siri AI, and so far it actually works
Apple's rebuilt Siri AI, shown at WWDC 2026, finally works for everyday multistep tasks, according to a hands-on test by The Verge. The new assistant adds a batch of events from an email or a messy flyer to your calendar in one shot, builds shopping lists, sets reminders, and answers practical questions by reading your own email, messages, and calendar. The Verge's writer tried each scenario and confirmed it worked, while noting the feature set still trails what Google's Gemini has done on Android for two years. The upgrade runs partly on a custom Google Gemini model hosted on Apple's privacy servers.
Hands-free first notice of loss: Using Strands Agents and Amazon Bedrock AgentCore Browser Tool for intelligent claims intake
AWS published a technical walkthrough of a hands-free first notice of loss (FNOL) intake system that automates the opening step of an insurance claim. The design pairs domain-reasoning agents built on the open-source Strands Agents SDK with the Amazon Bedrock AgentCore Browser Tool, which drives a managed Chrome session to fill claim portals on its own. Instead of handing adjusters a pile of raw photos and recordings, the system tags and scores evidence at submission time so a person starts with pre-analyzed context.
Build an agentic incident triage assistant with Amazon Quick and New Relic
AWS published a guide showing engineering teams how to build an AI agent that handles incident triage from a single prompt. The agent uses Amazon Quick to investigate an outage through the New Relic Model Context Protocol (MCP) Server, write a root cause analysis brief with evidence links, and open a tracked task in Asana for follow-up. The goal is to compress the slow evidence-gathering work that site reliability engineers do across separate tools into one automated flow.
Amazon employees ask Seattle to put the brakes on new data centers
Seattle's City Council voted 9-0 on June 9, 2026 to pause construction of large new data centers for one year, with an option to extend the freeze another six months. The move followed proposals from four companies to build five big facilities that together would draw about 369 megawatts, roughly a third of the electricity the city uses on an average day. Among the policy's loudest backers were current Amazon employees, who testified in support even though their employer is the region's largest tech firm.
Amazon is launching AI-generated custom merch
Amazon now lets US shoppers create custom merchandise from AI text prompts inside the Amazon app, with the designs printed on apparel and drinkware and shipped through its existing Merch on Demand service. Announced June 8, 2026, the feature works through Alexa for Shopping: a shopper describes an idea, sees an AI design in seconds, edits it, and orders it like any other Amazon purchase. Designing is free, and customers pay only for the finished product.
Unlocking AI flexibility in Europe: A guide to cross-region inference for EU data processing and model access
Amazon Web Services published a guide explaining how cross-Region Inference (CRIS) on Amazon Bedrock lets European customers run generative AI workloads with more capacity while keeping data inside the European Union. The feature automatically routes model requests across several EU AWS Regions, such as Frankfurt, Ireland, Paris, and Stockholm, so a request starting in Europe is only ever processed in Europe. Traffic stays on the encrypted AWS network and never crosses the public internet, which helps businesses meet data residency and GDPR obligations. For some models the EU cross-Region option also costs less than calling a single Region directly.
It's safe to close your laptop now: Hosting coding agents on Amazon Bedrock AgentCore
Amazon Bedrock AgentCore Runtime gives each agent session its own isolated microVM with a persistent workspace, secure tool access through Gateway, and built-in observability-so you can run Claude Code, Codex, Kiro, and Cursor in parallel without sharing secrets, ports, or filesystems. Close the lid, go to dinner, and pick up where you left off tomorrow.
End-to-end encrypted ML inference with Amazon SageMaker AI and FHE
This blog has previously discussed FHE for ML inference in the post Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing, but this post goes a little further. That previous post showed how to implement FHE-based inference 'from scratch' by hand-crafting a linear-regression algorithm using a low-level library called SEAL. Instead, this post shows a much more flexible and higher-level approach based on concrete-ml, a high-level library built specifically for FHE-based inference. It supports several common types of models 'out of the box' and is ev
Automate model quota request and operational issue triage on Amazon Bedrock
In this post, we introduce Amazon Bedrock Ops Alert, a three-layer automated monitoring solution that proactively detects operational issues, dynamically adjusts alarm thresholds, classifies alarms by category, automatically creates context-aware support cases, helps prevent duplicate cases when an unresolved case of the same alarm category is already active, and delivers contextualized notifications to AI SRE teams. We walk through the solution architecture and how you can deploy it in your own environment.
AWS Kiro accelerates software development by proving code correctness before it gets to work
AWS is upgrading its AI software development tool Kiro to catch problems before any code is written and to speed up large projects. The updates, all rolling out the same day, include a Requirements Analysis engine that uses a three-stage neurosymbolic pipeline to prove whether requirements contradict each other, plus Parallel Task Execution and a Quick Plan workflow. AWS frames the work as spec-driven development that applies mathematical rigor from hardware design to software, aiming to reduce hallucinations in AI coding agents.
Google Cloud sees its marketplace as the launchpad for the agentic enterprise
In an interview at Red Hat Summit 2026 on theCUBE, Google Cloud Marketplace managing director Dai Vu argued that cloud marketplaces are becoming the distribution backbone of what he calls the agentic enterprise. He said enterprise buyers increasingly want complete, outcome-driven solutions rather than point tools, a shift that plays into the marketplace model. Google has launched the Gemini Enterprise Agent Platform and committed $750 million to its partner ecosystem to accelerate readiness.