Critical cloud exposures pile up faster than security teams can close them. Cloud-native application protection platforms (CNAPP), cloud providers, and posture scanners generate more findings than any team can triage, and risk accumulates in the gap between detected and fixed. Most findings sit untouched, not because they’re hard to fix. Someone must investigate context, identify … Read more
Choosing the right model for a specific application requires a practical way to test it with representative inputs. Amazon SageMaker JumpStart provides access to a broad catalog of already pretrained models. In this post, we show how to deploy an available model from SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker … Read more
AI agents increasingly need to access web resources that are priced per request. For example, premium datasets charge by the query, research APIs meter by the call, and paywalled publishers gate content behind payment. When an AI agent encounters one of these resources, the server returns HTTP 402 Payment Required. The agent has no built-in … Read more
Managing AI model access across dozens or hundreds of AWS accounts creates operational friction when each workload account must subscribe and accept AWS Marketplace Private Pricing Offers (MPPOs) individually. Amazon Bedrock with managed entitlements simplifies multi-account access to third-party AI models so you can subscribe once from a central account and distribute access across your … Read more
Security analysts need fast answers about their cloud infrastructure without deep familiarity with domain-specific query languages. Orca Security’s tool covers over 900 cloud models (the resource types Sonar tracks, such as an Amazon Elastic Compute Cloud (Amazon EC2) instance or an Amazon Simple Storage Service (Amazon S3) bucket). Querying that data required precise model names, … Read more
This post is co-written with Kenneth Salmon, Edward Boswell, Jack Armstrong, Robert Bahn, and Nick Ciubotariu from ShipStation Global. Answering the same IT questions and clearing routine tickets drains time that IT teams could spend elsewhere. To reclaim it, the ShipStation Global Engineering team built Deckhand, an AI-powered IT help desk agent for Slack, on … Read more
SpaceXAI’s Grok 4.7 is now available on Amazon Bedrock, which offers a choice of foundation models (FMs) from multiple providers. According to SpaceXAI, Grok 4.7 is trained to work longer on more difficult tasks and knows when to check its own work. That matters most for agents, which carry a task through many steps and … Read more
Mixture-of-Experts (MoE) is a sparsely-activated architecture that scales large language model (LLM) capacity while keeping per-token compute low. Because its operations account for a large share of total compute time, inefficiencies there directly raise request latency and cost. Therefore, optimizing MoE operations through kernel engineering offers the largest opportunity for performance gains. Fused MoE is … Read more
Single-Region inference can throttle when demand exceeds local capacity. Teams in Thailand, Malaysia, Singapore, Indonesia, and Taiwan need access to the newest OpenAI models beyond that capacity. Amazon Bedrock offers OpenAI GPT-6 Sol and GPT-6 Luna through global cross-Region inference from all five AWS Regions, and GPT-6 Astra from Asia Pacific (Singapore). This post shows … Read more
Applications that route requests, select agent actions, or assign policy outcomes often need scores over a fixed set of choices instead of free-form text. Deploying these decision models with llama.cpp on Amazon SageMaker AI gives you a managed path for serving quantized models on CPU or GPU infrastructure. This post continues a series on specialized … Read more
This post shows how to deploy one large language model (LLM) across two GPU nodes on Amazon Elastic Kubernetes Service (Amazon EKS) with the multi-node Ray LLM Deep Learning Containers. It uses separate prefill and decode nodes behind one OpenAI-compatible endpoint. This kind of multi-node setup is common for tensor, pipeline, and data parallelism, as … Read more
A recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026 At AWS, we believe the fastest path to enterprise AI is giving builders real choice at every layer: model, runtime, and tooling. That belief shapes how we invest across Amazon Bedrock, AgentCore, and Strands. Amazon Bedrock continues expanding access … Read more
Building an AI agent for a demo and operating one for 40 million developers are different engineering problems. Postman set out to build Agent Mode, an AI-native way to work across API testing, documentation, discovery, and implementation. The team expected model quality and prompt design to be the hardest problems. The deeper challenges came from … Read more
When an AI agent runs, it often needs to buy something to finish a task: a model inference, an API response, access web content, or a call to another agent. These purchases are small and frequent, sometimes a fraction of a cent each, and they happen inside the agent’s loop with no person available to … Read more
Multiple teams within the same company increasingly need shared access to expensive GPU clusters for their generative AI operations, while maintaining isolation boundaries, resource fairness, and operational independence. Consider a data science team training large language models, a computer vision group running inference workloads, and a research team experimenting with new model architectures. All of … Read more
Today, we’re excited to announce the availability of Claude Haiku 5.5 on Amazon Bedrock and Claude Platform on AWS. According to Anthropic, Claude Haiku 5.5 is the fastest and most efficient model in the Claude 5.5 family, built for subagents and high-volume, cost-sensitive work. It also costs around 75 percent less than Claude Haiku 4.5 … Read more
Enterprise organizations are adopting Retrieval Augmented Generation (RAG) to unlock insights from company knowledge sources like Microsoft SharePoint, Google Drive, and Atlassian Confluence. However, these knowledge sources contain sensitive information governed by complex permission structures. Making sure that AI-generated answers respect those permissions is one of the hardest challenges in enterprise AI. In this post, … Read more
Agentic automation, software that reasons and adapts to complete tasks, is showing up on AI center of excellence (AI CoE) roadmaps. But the standard way companies justify automation investments, hours saved times labor cost minus build cost, was designed for rule-based tools like robotic process automation (RPA). That model misses most of the value agents … Read more
Building grounded AI on Amazon Bedrock, Qlik Answers tackles a common enterprise problem: employees aren’t short on data. They are short on a fast way to ask a real question of it and get an answer they can trust. Analysts spend hours searching through documents, dashboards, and institutional memory to answer things a colleague could … Read more
Reducing the time between incident detection, investigation, and remediation is a critical priority for organizations running production workloads on AWS. When an issue arises, on-call engineers often need to quickly diagnose the problem across application components, identify the root cause, and apply the fix, often in the middle of the night. AWS DevOps Agent, an … Read more
The biggest barrier to AI adoption isn’t awareness. It’s the gap between talking about AI and building with it. Professionals whose primary role isn’t writing code, but whose daily work increasingly depends on AI solutions, don’t need engineering backgrounds to start building AI capability. They need the right tools, structured support, and permission to fail. … Read more
Cornerstone OnDemand, Inc. (Cornerstone) is a global leader in workforce readiness solutions, serving 140 million users across 186 countries. The company built a multi-agent AI system that transforms database operations from reactive firefighting into proactive, self-orchestrating workflows. The system, called Orion AI, uses Amazon Bedrock and Strands Agents, an open source agent orchestration framework from … Read more
Off-the-shelf AI assistants answer individual questions well, but they fall short on a different axis: continuity. Ask a stateless assistant about your garden today and it has no idea that you mentioned your fast-draining raised beds three weeks ago, that you only use organic fertilizer, or that your petunias were struggling through a heat wave. … Read more
With generative AI adoption moving faster than the personal computer or the internet and global AI-related investment in 2025 representing $581.69 billion, organizations must position their workforce to use AI to power their operations while employing it responsibly. Researchers affiliated with the AI Adoption Initiative argue that the workforce relates to AI through an AI … Read more
Amazon SageMaker HyperPod gives machine learning (ML) teams access to large pools of accelerated compute for training and fine-tuning models. When several teams share one cluster, the technical setup is usually straightforward. The challenging part is governance. You must decide which teams can use the cluster, how much capacity each team gets, what happens when … Read more
We recently introduced the ability to create and manage Amazon SageMaker Spaces on Amazon SageMaker HyperPod EKS clusters directly from the Amazon SageMaker Studio UI. Data scientists and machine learning (ML) engineers can now launch JupyterLab and Code Editor environments on HyperPod clusters without leaving their browser or using command-line tools, reducing the time from … Read more
Airlines already have apps and websites where travelers check flights, pick seats, and manage bookings, and adding a natural voice layer opens those tasks to spoken requests. With this voice layer, a traveler can change a seat or check a delay by speaking, without leaving the app or navigating through screens. Building it requires careful … Read more
Coding and agentic workloads are asking more of AI models than ever: refactor a repository spanning hundreds of files, sustain a multi-hour agentic workflow without losing context, and reason through complex systems problems with tool use at every step. Meeting those demands with open-weight models has historically meant provisioning and operating your own inference infrastructure. … Read more
Engineers increasingly use coding assistance tools to accelerate their development workflows. Today, Amazon SageMaker AI optimized generative AI inference introduces the aws-ai-ml skill, available through the Agent Toolkit for AWS. This skill gives coding agents like Kiro, Claude Code, and Codex deep expertise in inference optimization and benchmarking. Install the skill, and your existing agent … Read more
Amazon Quick is Amazon’s agentic AI companion built for work. You build agents that reason over your data, call action connectors, and carry multi-step tasks to completion. Promoting those resources (chat agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account, the way you would any other application, has … Read more