Today, we’re excited to announce the availability of Claude Fable 5.1 on Amazon Bedrock and Claude Platform on AWS. Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Given its capabilities, Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data … Read more
When Atos set out to upskill 400 engineers from theory to delivery in agentic AI, the team faced a familiar challenge: how to build real-world capability, not only theoretical knowledge. Online courses and classroom-based instruction build foundations, but they do not always give teams the confidence or practical experience needed to apply AI effectively to … Read more
Generative AI spend behaves unlike any cost line before it. Traditional compute scales with provisioned capacity. AI spend scales with behavior: a single engineer running an agentic coding loop against a premium model can burn more tokens in a few hours than a team does in a week. This is the tokenomics problem: usage is … Read more
Amazon Quick proof of concept (POC) projects often succeed with a small pilot team, then stall when security and compliance teams review the production plan. A permission model that works for ten pilot users often breaks when you add five departments. Agents can return data outside their intended scope, and compliance teams struggle to audit … Read more
An agentic system can research, reason, and orchestrate multi-step workflows, but the moment it hits a paywall, it stops. It has no wallet, no card, and no spending limit. t54 solved that problem. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. Their trust layer … Read more
This blog post is co-written with Kiran Dhamane, Abhishek I S, and Mayur Ghodekar from ZS Associates Organizations in regulated industries face a persistent tension: give developers the agility they need for ad-hoc analytics, or lock down the environment to meet compliance requirements. In this post, we explore how ZS built a security-hardened Amazon SageMaker … Read more
Boomi Scribe alleviates documentation, one of the most persistent sources of technical debt for enterprise development teams. Enterprise developers often struggle to create and maintain documentation, especially when workflows (automated business processes) involve integrations with multiple enterprise applications and data sources. Boomi recognized this challenge and built Boomi Scribe, an AI-powered agent running on AWS … Read more
Model Context Protocol (MCP) servers allow foundation models to access external data and tools, supporting standardized, secure access to files, databases, and APIs. They give AI agents the ability to interact with real-world applications, reduce hallucinations with accurate context, and offer stateful, multi-turn capabilities. Industry-standard architectures quickly evolved and adopted MCP to power agentic AI … Read more
We’re excited to share that AWS has been recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. In this evaluation of 13 providers, AWS received the highest score in the Strategy category. We believe this recognition reflects our commitment to delivering flexible, cost-efficient AI infrastructure that helps you move from experimentation … Read more
Most organizations scaling their use of agents and tools hit the same challenges. Teams build in isolation, with no shared record of what exists, who owns it, or whether it’s been reviewed. The problem has moved from building agents and tools to discovering and governing them. AWS Agent Registry is purpose-built to solve this. Now … Read more
Teams that add Retrieval Augmented Generation (RAG) to a foundation model usually start with a single retrieval step against a single knowledge base. That works until the questions get harder, when the answer spans several sources, or the system has to decide which source to consult before it can respond. Enterprise agentic retrieval solves that: … Read more
Multi-tenant agentic chat assistants have become a frequent request for large-scale customers, and document chat sits at the top of the list. A user uploads a contract, a report, or a product manual, and then researches or asks questions about it immediately or in the future. The conversational interface is straightforward to build, but the … Read more
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It provides low-latency online serving for real-time inference, an offline store for historical retention and training feature data, and supports both streaming and batch ingestion patterns. As ML platforms mature, two operational gaps surface … Read more
This post is co-written with Vianney Bruned, Filippo Giruzzi, Belkiss Saidi, and Carlos Ramirez from Decathlon. Decathlon is one of the world’s largest sporting goods retailers, with more than 100,000 teammates and 400 million users worldwide. The company relies on accurate demand forecasting at scale to support the availability of the appropriate products in each … Read more
When Salesforce set out to make Agentforce (Salesforce’s AI foundation for agents) highly available (HA) across multiple Availability Zones (AZs), the team faced a gap. Amazon SageMaker AI Inference Components (ICs) could cut GPU costs, but their default placement didn’t guarantee the Multi-AZ resilience Salesforce’s compliance bar required. For Salesforce, the ICs delivered an 8x … Read more
Creative teams face growing demand for more assets, formats, and revisions, while their scripts, references, models, and outputs often remain fragmented across tools. Creators must repeatedly transfer context and assemble results manually. With 78% of creative leaders saying demand exceeds their teams’ capacity, faster generation alone does not solve the underlying workflow problem. To address … Read more
Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India, with India geographic cross-Region inference. If you have local data processing requirements in India, including in financial services, healthcare, and the public sector, you can now use these OpenAI models at scale. Amazon Bedrock processes inference requests and data within India. Both … Read more
Self-hosted speech AI has historically carried an observability trade-off. The service can tell you an endpoint is up and how many requests it served. The questions that actually drive capacity planning and cost management stay locked inside the vendor’s container: what you are billed for, which features your traffic uses, and what the inference engine … Read more
This post is a collaboration between AWS, NVIDIA and Heidi. Reducing automatic speech recognition (ASR) inference costs on Amazon Elastic Compute Cloud (Amazon EC2) becomes critical when GPU utilization per request is low but latency requirements are strict. A single ASR inference request typically uses only 15–20 percent of a GPU’s compute capacity, yet the default … Read more
AI teams building production agents face a frustrating asymmetry: the diversity of agent frameworks keeps growing, but evaluation tooling has not kept pace. Most evaluation systems assume you built your agent in a specific way: a specific SDK, a specific large language model (LLM) client, a specific tracing pattern. The moment you step outside that … Read more
GoDaddy is one of the world’s largest domain registrar and web hosting companies, serving more than 20 million customers and managing approximately 82 million domain names. At that scale, access to timely business data directly affects how quickly the company can act. When GoDaddy’s analytics infrastructure faced challenges under the weight of thousands of dashboards, … Read more
Booking a phlebotomy appointment shouldn’t be a hassle for oncology patients already managing treatment. Natera’s service, powered by Amazon Bedrock AgentCore, allows a phlebotomist to come to the patient, helping Natera deliver a more convenient experience. Natera, a global diagnostics company specializing in cell-free DNA testing, wanted to transform their patient experience by replacing manual … Read more
In 2021, we published Bring your own model with Amazon SageMaker script mode. That post showed how to use script mode on managed framework containers from AWS to write custom training and inference code. Script mode was a leap forward: you didn’t need to build or maintain Docker images to run your own algorithm on … Read more
Data preparation for supervised fine-tuning (SFT) doesn’t end when your dataset is clean and correctly formatted. The harder questions come next. How much data do you actually need? Should you collect more, or select a better subset of what you have? How do you generate high-quality examples when human annotation doesn’t scale? And how do … Read more
Data preparation determines the ceiling of any supervised fine-tuning (SFT) project. You’ve evaluated your foundation model (FM), and out-of-the-box performance isn’t meeting your production requirements. Maybe the model doesn’t follow your output schema reliably, struggles with your domain’s classification taxonomy, or can’t maintain the tone your application demands. The question isn’t whether to customize, it’s … Read more
Organizations often deploy agents using Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. These agents may access governed knowledge bases hosted in separate AWS accounts. This cross-account separation helps maintain clear workload boundaries but can introduce integration challenges. This post explains how AgentCore agents in … Read more
Observability agents are fast. They query alerts, correlate logs with traces, and produce a root cause hypothesis in minutes. The part that still takes time is verification. You read the agent’s text summary, open your observability tools in a browser, navigate to the trace waterfall, check the service map to scope impact, and cross-reference what … Read more
Amazon Quick Desktop brings governed, AI-assisted reporting to the files your team already manages on Amazon FSx for NetApp ONTAP (FSx for ONTAP), cutting weekly report preparation from hours to minutes. Today, producing those reports takes hours of manual effort each week. Teams re-read the same documents, reformat metrics, and copy summaries into Slack. Leaders … Read more
Today, we are announcing new Ray capabilities on Amazon SageMaker HyperPod that integrate Ray with the HyperPod purpose-built infrastructure for foundation model training and serving. Ray is an open-source framework that data scientists use to scale distributed Python workloads across clusters of GPUs, from distributed training with Ray Train to model serving with Ray Serve. … Read more
Organizations across industries struggle with managing institutional knowledge, the collective wisdom and experience accumulated over years of operations. This “tribal knowledge” often disappears when key personnel leave, creating knowledge gaps that impact efficiency and innovation. Traditional documentation methods have proven inadequate, often resulting in outdated or inaccessible information when it’s needed most. In this post, … Read more