Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network.  ( 109 min )

In this post, we'll explore how multimodal BioFMs work, showcase real-world applications in drug discovery and clinical development, and contextualize how AWS enables organizations to build and deploy multimodal BioFMs.  ( 113 min )

In this post, we walk through building a scalable, event-driven transcription pipeline that automatically processes audio files uploaded to Amazon Simple Storage Service (Amazon S3), and show you how to use Amazon EC2 Spot Instances and buffered streaming inference to further reduce costs.  ( 111 min )

Today, Amazon SageMaker AI  supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure.  ( 114 min )

Today, we're introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment.  ( 108 min )

Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions  ( 108 min )

Today, we're excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. In this post, we walk through how Claude Cowork integrates with Amazon Bedrock and show an example of how knowledge workers use it in practice.  ( 108 min )

In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks.  ( 115 min )

Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, 4, and 8 RTX PRO 6000 GPU instances, with each GPU providing 96 GB of GDDR7 memory. This launch provides the capability to use a single-node GPU, G7e.2xlarge instance to host powerful open source foundation models (FMs) like GPT-OSS-120B, Nemotron-3-Super-120B-A12B (NVFP4 variant), and Qwen3.5-35B-A3B, offering organizations a cost-effective and high-performing option.  ( 112 min )

You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator's large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence.  ( 113 min )

In this post, we'll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic.  ( 114 min )

View on sreweekly.com A message from our sponsor, incident.io: “Lifting and shifting” noise to new tools just buys a different UI for the same burnout. incident.io’s migration framework prioritizes service cataloging and inventory to fix ownership, preventing team friction during the transition to a scalable on-call system. Organizational Second Hit Syndrome A previously unpublished article […]  ( 4 min )

In this post, we share how Amazon Bedrock's granular cost attribution works and walk through example cost tracking scenarios.  ( 114 min )

In this post, we show you how to use Model Distillation, a model customization technique on Amazon Bedrock, to transfer routing intelligence from a large teacher model (Amazon Nova Premier) into a much smaller student model (Amazon Nova Micro). This approach cuts inference cost by over 95% and reduces latency by 50% while maintaining the nuanced routing quality that the task demands.  ( 112 min )

In this post, we show you how to build a video semantic search solution on Amazon Bedrock using Nova Multimodal Embeddings that intelligently understands user intent and retrieves accurate video results across all signal types simultaneously. We also share a reference implementation you can deploy and explore with your own content.  ( 116 min )

This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments.  ( 114 min )

In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows.  ( 111 min )

In this post, we demonstrate two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production ready performance.  ( 114 min )

Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on […]  ( 114 min )

In this post, you'll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You'll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started.  ( 111 min )

Today, we're announcing sheet tooltips in Amazon Quick Sight. Dashboard authors can now design custom tooltip layouts using free-form layout sheets. These layouts combine charts, key performance indicator (KPI) metrics, text, and other visuals into a single tooltip that renders dynamically when readers hover over data points.  ( 110 min )

In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2.  ( 111 min )

This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […]  ( 110 min )

In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation.  ( 117 min )

We're excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed deployments, but now deployments are optimized for their specific use case and performance constraint.  ( 107 min )

This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating your generative AI deployments from concept to production.  ( 111 min )

In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely, reliably, and at scale.  ( 116 min )

With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI. In this post, we build an AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution.  ( 112 min )

This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You'll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting.  ( 117 min )

View on sreweekly.com A message from our sponsor, Archera: AI workloads are unpredictable, which makes cloud commitments feel like a gamble. Archera insures your commitments against underutilization, so you can push coverage higher without the risk of getting stuck. If usage drops, Archera covers the downside. Commitment Release Guarantee included. Start Saving Ashby taught us […]  ( 4 min )

This post shows you how to manage FM transitions in Amazon Bedrock, so you can make sure your AI applications remain operational as models evolve. We discuss the three lifecycle states, how to plan migrations with the new extended access feature, and practical strategies to transition your applications to newer models without disruption.  ( 111 min )

Today, we're announcing AWS Agent Registry (preview) in AgentCore, a single place to discover, share, and reuse AI agents, tools, and agent skills across your enterprise.  ( 109 min )

This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run.  ( 113 min )

In this post, you will learn how to build stateful MCP servers that request user input during execution, invoke LLM sampling for dynamic content generation, and stream progress updates for long-running tasks. You will see code examples for each capability and deploy a working stateful MCP server to Amazon Bedrock AgentCore Runtime.  ( 115 min )

In this post, we'll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you'll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We'll show you how to evaluate your results using training metrics and loss curves.  ( 119 min )

In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential. In this post, you will learn four practical approaches to implementing human-in-the-loop constructs using AWS services.  ( 110 min )

This post walks you through understanding audio embeddings, implementing Amazon Nova Multimodal Embeddings, and building a practical search system for your audio content. You'll learn how embeddings represent audio as vectors, explore the technical capabilities of Amazon Nova, and see hands-on code examples for indexing and querying your audio libraries. By the end, you'll have the knowledge to deploy production-ready audio search capabilities.  ( 116 min )

In this post, we explore where RFT is most effective, using the GSM8K mathematical reasoning dataset as a concrete example. We then walk through best practices for dataset preparation and reward function design, show how to monitor training progress using Amazon Bedrock metrics, and conclude with practical hyperparameter tuning guidelines informed by experiments across multiple models and use cases.  ( 119 min )

With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs.  ( 109 min )

This post walks through building an automated podcast generator that creates engaging conversations between two AI hosts on any topic, demonstrating the streaming capabilities of Nova Sonic, stage-aware content filtering, and real-time audio generation.  ( 112 min )

In this post, we show you how to build a natural text-to-SQL solution using Amazon Bedrock that transforms business questions into database queries and returns actionable answers.  ( 115 min )

In this post, we walk through building a custom HR onboarding agent with Quick. We show how to configure an agent that understands your organization’s processes, connects to your HR systems, and automates common tasks, such as answering new-hire questions and tracking document completion.  ( 114 min )

In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment.  ( 113 min )

In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents and Amazon OpenSearch.  ( 115 min )

This blog post demonstrates how Windward helps enhance and accelerate alert investigation processes by combining geospatial intelligence with generative AI, enabling analysts to focus on decision-making rather than data collection.  ( 110 min )

Amazon Bedrock AgentCore Gateway provides a centralized layer for managing how AI agents connect to tools and MCP servers across your organization. In this post, we walk through how to configure AgentCore Gateway to connect to an OAuth-protected MCP server using the Authorization Code flow.  ( 114 min )

View on sreweekly.com A message from our sponsor, Depot: CI was designed for humans who context-switch while waiting. Agents don’t. They’re just blocked. Depot CEO Kyle Galbraith on how they re-imagined Depot CI to close the loop: run against local patches, rerun a single job, SSH into the runner to check reality. Per-second billing, no […]  ( 3 min )

In this post, we explore how ActorSimulator in Strands Evaluations SDK addresses the challenge with structured user simulation that integrates into your evaluation pipeline.  ( 113 min )

This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible.  ( 113 min )

In this post, we show you how to configure AWS Network Firewall to restrict AgentCore resources to an allowlist of approved internet domains. This post focuses on domain-level filtering using SNI inspection — the first layer of a defense-in-depth approach.  ( 115 min )

Through a strategic partnership with the AWS Generative AI Innovation Center (GenAIIC), Rocket Close developed an intelligent document processing solution that has significantly reduced processing time, making the process 15 times faster. The solution, which uses Amazon Textract for OCR processing and Amazon Bedrock for foundation models (FMs), achieves a strong 90% overall accuracy in document segmentation, classification, and field extraction.  ( 112 min )

In this post, we go through how to use managed session storage to persist your agent's filesystem state and how to execute shell commands directly in your agent's environment.  ( 114 min )

This post demonstrates how to build an automated competitive price intelligence system that streamlines manual workflows, supporting teams to make data-driven pricing decisions with real-time market insights.  ( 114 min )

Pupdate We’ve (finally) had some warm and sunny days, so the coats have mostly been off for walks :) Bath Half $daughter0 is in her final year of her degree at Bath, and after getting into running last year she decided to run the Bath Half with some friends. That provided a good excuse for […]  ( 14 min )

Pupdate We’ve (finally) had some warm and sunny days, so the coats have mostly been off for walks :) Bath Half $daughter0 is in her final year of her degree at Bath, and after getting into running last year she decided to run the Bath Half with some friends. That provided a good excuse for […]  ( 14 min )

In this post, we introduce Amazon Bedrock AgentCore Evaluations, a fully managed service for assessing AI agent performance across the development lifecycle. We walk through how the service measures agent accuracy across multiple quality dimensions. We explain the two evaluation approaches for development and production and share practical guidance for building agents you can deploy with confidence.  ( 122 min )

In this post, you learn how to build a FinOps agent using Amazon Bedrock AgentCore that helps your finance team manage AWS costs across multiple accounts. This conversational agent consolidates data from AWS Cost Explorer, AWS Budgets, and AWS Compute Optimizer into a single interface, so your team can ask questions like "What are my top cost drivers this month?" and receive immediate answers.  ( 112 min )

In this post, we show you how to build a similar system for your organization. You will learn the architecture decisions, implementation details, and deployment process that can help you automate your own compliance workflows.  ( 113 min )

In this post, we demonstrate how to implement agentic QA automation through QA Studio, a reference solution built with Amazon Nova Act. You will see how to define tests in natural language that adapt automatically to UI changes, explore the serverless architecture that executes tests reliably at scale, and get step-by-step deployment guidance for your AWS environment.  ( 109 min )

I'm excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution.  ( 108 min )

Traditional frameworks designed for static deployments cannot address the dynamic interactions that define agentic workloads. AI Risk Intelligence (AIRI), from AWS Generative AI Innovation Center, provides the automated rigor required to govern agents at enterprise scale—a fundamental reimagining of how security, operations, and governance work together systemically.  ( 111 min )

In this post, you'll learn how Ring implemented metadata-driven filtering for Region-specific content, separated content management into ingestion, evaluation and promotion workflows, and achieved cost savings while scaling up.  ( 113 min )

In this post, we explore the challenges that Volkswagen Group faced in producing brand-compliant marketing assets at scale. We walk through how we built a generative AI solution that generates photorealistic vehicle images, validates technical accuracy at the component level, and helps enforce brand guideline compliance alignment across the ten brands.  ( 114 min )

In this post, we show you how to use Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency's STIX instrument.  ( 115 min )

In this post, we walk through two use cases that help enhance the user viewing experience using agentic AI tools and frameworks including Strands Agents SDK, Amazon Bedrock AgentCore, and Amazon Nova Sonic 2.0. This agentic AI system uses a Model Context Protocol (MCP) to deliver a personal entertainment concierge that understands user preferences through natural dialogue.  ( 114 min )

View on sreweekly.com A message from our sponsor, Clickhouse: AI isn’t replacing SREs. It’s changing how they work. The near future of observability isn’t autonomous agents, it’s collaboration. ClickHouse’s ClickStack Notebooks bring SREs and AI into a shared investigative workspace, combining human intuition with structured, reliable tooling to debug faster and think more clearly. Read […]  ( 4 min )

TL;DR QCon is one of my favourite events, and I’ve been to a lot of them over the years. 2026 was the best yet, so kudos to the programme committee and C4media organisers. The most fun bit was hosting the security track, where I got to run a mini security conference within the conference, with […]  ( 16 min )

TL;DR QCon is one of my favourite events, and I’ve been to a lot of them over the years. 2026 was the best yet, so kudos to the programme committee and C4media organisers. The most fun bit was hosting the security track, where I got to run a mini security conference within the conference, with […]  ( 15 min )

TL;DR 2026 was the best Monki Gras so far with a theme of ‘prepping craft’. A room full of techies, and a great gathering of friends, but not really a tech conference. It’s transcended tech, and become the place where the talks are about more important stuff. Monki What? Monki Gras is a London based […]  ( 15 min )

TL;DR 2026 was the best Monki Gras so far with a theme of ‘prepping craft’. A room full of techies, and a great gathering of friends, but not really a tech conference. It’s transcended tech, and become the place where the talks are about more important stuff. Monki What? Monki Gras is a London based […]  ( 15 min )

Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, Opus 4.6, Sonnet 4.5, Sonnet 4.6, and Haiku 4.5) and Amazon (Nova 2 Lite) models directly in the Auckland Region with cross region inference. In this post, we explore how cross-Region inference works from the New Zealand Region, the models available through geographic and global routing, and how to get started with your first API call. We  ( 112 min )

In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance.  ( 112 min )

Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio.  ( 114 min )

Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available.  ( 110 min )

In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each approach is designed for different use cases and cost-performance trade-offs.  ( 111 min )

In this series of posts, you will learn how streaming architectures help address these challenges using Pipecat voice agents on Amazon Bedrock AgentCore Runtime. In Part 1, you will learn how to deploy Pipecat voice agents on AgentCore Runtime using different network transport approaches including WebSockets, WebRTC and telephony integration, with practical deployment guidance and code samples.  ( 114 min )

In this post, we walk through the end-to-end workflow of using RFT on Amazon Bedrock with OpenAI-compatible APIs: from setting up authentication, to deploying a Lambda-based reward function, to kicking off a training job and running on-demand inference on your fine-tuned model.  ( 114 min )