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Safeguard your agentic AI applications with the Amazon Bedrock Guardrails InvokeGuardrailChecks API

Today, we’re announcing a new API with Amazon Bedrock Guardrails. With this API, you can apply individual safeguards, also referred to as safety checks, at any point in your agentic AI applications without creating guardrail resources. The new InvokeGuardrailChecks API gives you the flexibility to invoke supported safeguards at any turn in the agentic loop …

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How Siemens “slices the elephant,” advancing agentic workflows for industrial software development

For technology companies like Siemens, software is the nervous system of factories, energy grids, and transportation networks worldwide. As a global leader in industrial AI, industrial software, and industrial automation, Siemens brings decades of domain expertise across factory and process automation, energy infrastructure, and intelligent transportation — expertise that no off-the-shelf AI solution can replicate. …

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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, …

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Cloud CISO Perspectives: The 4 lessons that guided AI Threat Defense

Welcome to the first Cloud CISO Perspectives for June 2026. Today, we introduce Chris Betz as the new CISO of Google Cloud. For his first Cloud CISO Perspectives, Chris shares four key lessons we learned about using AI to the defender’s advantage while building AI Threat Defense. As with all Cloud CISO Perspectives, the contents …

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Building Supercharger: How Rocket Close optimized title operations with agentic AI

Rocket Close is a Detroit-based title agency and appraisal management company within Rocket Companies that provides title insurance, property valuation, and settlement services. As demand for mortgages and loans grew, title operations became a bottleneck in the homebuying process. Time-intensive, state-specific title examinations, combined with manual research and fragmented systems, slowed throughput and made it …

Introducing the Open Knowledge Format

As foundation models continue to improve, the lack of relevant context often limits what they can do, especially as they are used to build agentic systems. While these models can help you write code, summarize documents, or analyze a dataset, they still need the right information to produce accurate and actionable results.  That’s why today, …

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Extract Data with On-demand and Batch Pipelines Dynamically

Many companies have large volumes of paper or electronic documents that contain untapped business intelligence. With the advancement of generative AI, various large language models can be used to accurately extract relevant data from these documents. This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on …

Powering the next era of Confidential AI

At Google Cloud, we’re committed to providing the most advanced, secure, and private infrastructure for the most demanding AI workloads, and partnering with a broad and diverse range of organizations to help them meet their AI workload needs. We are thrilled to collaborate with Apple on its expanded Private Cloud Compute (PCC) systems announced this …

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How frontier teams are reinventing AI-native development

Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x. Six engineers. Seventy-six days. A project scoped for 30 developers over 12 to 18 months, delivered within a quarter. That is not hypothetical. It’s what happened when …

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Scale Robot Reinforcement Learning with NVIDIA Isaac Lab on Amazon SageMaker AI

Physical AI is moving from research into production. Robots are increasingly trained in high-fidelity simulation before being deployed to factories, warehouses, and logistics centers, because training in the real world is slow, expensive, and often unsafe, while GPU-accelerated simulation can compress months of learning into hours. This shifts the challenge to compute. Reinforcement learning (RL) …