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Turning a simulation idea into a working application means assembling assets, connecting physics and rendering, and checking that the scene behaves as intended. Developers are combining frontier AI models with NVIDIA Omniverse libraries to help carry out that work — building applications for exploring scenarios, investigating failures and improving designs.

Developers direct AI agents through natural-language instructions, review results and guide changes. Omniverse libraries provide GPU-accelerated physics, rendering and sensor simulation capabilities.

Explore the projects below to see frontier AI models such as GPT-6 Astra at work, and check back for new examples from NVIDIA teams and developers across the ecosystem.

Build a Humanoid Simulator for a Warehouse Environment 🔗

Explore a gamified, physics-based control of a humanoid robot in first- and third-person view.

Before automating warehouse tasks, developers need an interactive simulation environment to explore task behavior and evaluate how the work gets done. Frank DeLise, Omniverse product manager at NVIDIA, used Astra to turn a SimReady warehouse and humanoid robot into an interactive simulator with first- and third-person views.

DeLise directed Astra to connect NVIDIA Omniverse libraries for physics ( ovphysx ), scene updates ( ovstage ), rendering ( ovrtx ) and the user interface ( ovui ). He also used Astra with SimReady ( simready-foundation ) to create the physical scene in simulation. Astra then generated animation and application code to bring those capabilities together.

Learn how to prepare and validate SimReady robot assets with frontier AI models and NVIDIA Omniverse libraries.

Connect an Autonomous-Driving Testing Workflow 🔗

Changing a scene, sensor or driving model can significantly aff...

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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 approve them.

Amazon Bedrock AgentCore payments removes that burden. It gives agents a managed way to pay for services on demand, with spending limits enforced by the infrastructure rather than by the model. In this post, we look at how Incarna used AgentCore payments to let its agents pay BlockRun for model inference one request at a time. BlockRun is a pay-as-you-go inference router serving more than 90 models from more than 15 providers over x402 , each call quoted and settled independently. AgentCore payments works with x402-compatible endpoints, including Amazon Bedrock inference endpoints. With the service, the Incarna team cut the work of adding x402 payment support from months to days, and put an end-to-end pay-per-inference flow into production.

The challenge: Paying for inference by the request

Paying per inference is a high-frequency, low-value pattern. An agent might make hundreds of small purchases in a single session, each worth a fraction of a cent. Card networks weren’t built for sub-cent payments. Building your own rails means solving several hard problems at once. You must decide where the money is held and how each payment is signed, support emerging payment protocols such as x402, and keep an autonomous agent from overspending.

What AgentCore payments provides

Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. AgentCore payments is a managed capability of Amazon Bedrock AgentCore t...

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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 them might need access to the same cluster. Without a well-designed multi-tenant (multi-team) architecture, organizations face uncontrolled resource consumption, weak isolation between teams, an inability to attribute shared GPU costs to the teams that incur them, and administrative overhead that slows down innovation.

Amazon SageMaker HyperPod is a purpose-built AI service that simplifies the management of large-scale compute clusters for gen AI workloads. It provides resilient, optimized clusters orchestrated by Amazon Elastic Kubernetes Service (Amazon EKS) or Slurm, so organizations can run distributed training, interactive development, and model inference at scale. At the same time, it automatically handles node health monitoring, fault recovery, and cluster lifecycle management.

In this post, we present a reference architecture for building a multi-tenant environment on Amazon SageMaker HyperPod with EKS. This architecture uses AWS IAM Identity Center for centralized authentication, per-team SageMaker AI domains for a tailored user experience, Kubernetes namespaces for workload isolation, HyperPod Task Governance for fair resource allocation, and namespace-level cost allocation for per-team spend visibility and chargeback. By the end of this post, you will have a clear blueprint for multiple teams to efficiently share a single HyperPod EKS cluster.

Architectu...

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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 for most tasks.

Amazon Bedrock gives you Haiku 5.5 capabilities while keeping your data within AWS infrastructure with Regional data residency. It works with the AWS controls your team already uses, including AWS Identity and Access Management (IAM) for access, AWS CloudTrail for audit, Amazon CloudWatch for monitoring, and Amazon Bedrock Guardrails. Usage appears on your AWS bill.

Claude Platform on AWS gives you direct access to Anthropic’s native platform experience and capabilities through the AWS Management Console. Build, test, and deploy with the same APIs, features, and console experience you’d get working with Anthropic directly, unified with AWS billing and authentication.

This post covers Claude Haiku 5.5’s improvements, practical guidance on when to choose Haiku 5.5, and how to get started on Amazon Bedrock.

What makes Claude Haiku 5.5 different

Claude Haiku 5.5 is Anthropic’s most capable Haiku model, across coding, tool use, computer use, and agentic tasks. It’s also the first Haiku model with effort controls, so you can tune cost against intelligence for each task instead of picking one setting for an entire workload.

The improvements stand out on quick and repeatable work at scale. For coding tasks, it acts as a subagent routing requests, reviewing code and classifying long documents. For knowledge work, Haiku 5.5 pulls key information from small-to-medium documents, does initial scans, and answers quick questi...

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