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How AI actually ships: enterprise case studies, engineering lessons from production, adoption data and ROI evidence.

How AI actually ships: enterprise case studies, engineering lessons from production, adoption data and ROI evidence.

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Introducing Gemma 4 models on Amazon Bedrock
Practice

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 i…

AI Agent Failure Detection and Root Cause Analysis with Strands Evals
Practice

AI Agent Failure Detection and Root Cause Analysis with Strands Evals

In this post, we walk you through calling the detector functions to diagnose real agent failures. You learn how to interpret their structured output: categorize…

Build context-rich research agents with Deep Agents and Bedrock AgentCore
Practice

Build context-rich research agents with Deep Agents and Bedrock AgentCore

In this post, you'll build a competitive research agent that demonstrates this pattern end to end. This walkthrough targets developers building multi-step AI wo…

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

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 Perspective…

Phil Ozuah on enhancing ambulatory care and accessibility
Practice

Phil Ozuah on enhancing ambulatory care and accessibility

The president and CEO of Montefiore Einstein talks about technology in healthcare, opportunities to close gaps in patient access, and his own path to leadership…

Collective action, collective success: A CEO’s role in transformations
Practice

Collective action, collective success: A CEO’s role in transformations

Successful transformations require everyone in the organization to move in the same direction. That can only happen when CEOs directly address collective-action…

The health system CEO imperative: Turning AI’s promise into performance
Practice

The health system CEO imperative: Turning AI’s promise into performance

Health systems aren’t short on AI, they’re short on impact from AI. Capturing real value requires CEO-led transformation and a fundamental rethink of how care a…

From silicon to softmax: Inside the Ironwood AI stack
Practice

From silicon to softmax: Inside the Ironwood AI stack

As machine learning models continue to scale, a specialized, co-designed hardware and software stack is no longer optional, it’s critical. Ironwood, our latest …

Announcing Ironwood TPUs General Availability and new Axion VMs to power the age of inference
Practice

Announcing Ironwood TPUs General Availability and new Axion VMs to power the age of inference

Today’s frontier models, including Google’s Gemini, Veo, Imagen, and Anthropic’s Claude train and serve on Tensor Processing Units (TPUs). For many organization…

ADK architecture: When to use sub-agents versus agents as tools
Practice

ADK architecture: When to use sub-agents versus agents as tools

At its simplest, an agent is an application that reasons on how to best achieve a goal based on inputs and tools at its disposal. As you build sophisticated mul…

Easy AI workflow automation: Deploy n8n on Cloud Run
Practice

Easy AI workflow automation: Deploy n8n on Cloud Run

n8n is a powerful yet easy-to-use workflow and automation tool for multi-step AI agents, and many teams want a simple, scalable, and cost-effective way to self-…

Achieve better AI-powered code reviews using new memory capabilities on Gemini Code Assist
Practice

Achieve better AI-powered code reviews using new memory capabilities on Gemini Code Assist

The best feedback during a code review is specific, consistent, and understands the history of a project. However, AI code review agents today are often statele…

Running high-scale reinforcement learning (RL) for LLMs on GKE
Practice

Running high-scale reinforcement learning (RL) for LLMs on GKE

As Large Language Models (LLMs) evolve, Reinforcement Learning (RL) is becoming the crucial technique for aligning powerful models with human preferences and co…

Supporting Viksit Bharat: Announcing our newest AI investments in India
Practice

Supporting Viksit Bharat: Announcing our newest AI investments in India

Editor's note: This blog has been translated into Bengali, Hindi, Marathi, Tamil, and Telugu. India’s developer community, vibrant startup ecosystem, and leadin…

Introducing Agent Sandbox: Strong guardrails for agentic AI on Kubernetes and GKE
Practice

Introducing Agent Sandbox: Strong guardrails for agentic AI on Kubernetes and GKE

Google and the cloud-native community have consistently strengthened Kubernetes to support modern applications. At KubeCon EU 2025 earlier this year, we announc…

How Lightricks trains video diffusion models at scale with JAX on TPU
Practice

How Lightricks trains video diffusion models at scale with JAX on TPU

Training large video diffusion models at scale isn't just computationally expensive — it can become impossible when your framework can't keep pace with your amb…

BigQuery under the hood: How Google brought embeddings to analytics
Practice

BigQuery under the hood: How Google brought embeddings to analytics

Embeddings are a crucial component at the intersection of data and AI. As data structures, they encode the inherent meaning of the data they represent, and thei…

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

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

In this post, we explore how Rocket Close built a solution using Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, a…

From PDFs to insights: Architecting an intelligent document processing pipeline with AWS generative AI services
Practice

From PDFs to insights: Architecting an intelligent document processing pipeline with AWS generative AI services

This post outlines the development of a cost-effective and scalable intelligent document processing pipeline on AWS, powered by Amazon Bedrock and its features.…

Build a meeting prep and follow-up assistant with Amazon Quick and Cisco Webex MCP servers
Practice

Build a meeting prep and follow-up assistant with Amazon Quick and Cisco Webex MCP servers

This post shows how to build a custom meeting prep and follow-up assistant using Amazon Quick and Cisco Webex MCP servers. From a single prompt, the agent finds…

Introducing the Open Knowledge Format
Practice

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. Whil…

Built from the inside out: How AWS Professional Services became a frontier team first
Practice

Built from the inside out: How AWS Professional Services became a frontier team first

AWS Professional Services (AWS ProServe) compressed engagement timelines from months to days, not by adding artificial intelligence (AI) tools to an existing pr…

Using AI Right Now: A Quick Guide
Practice

Using AI Right Now: A Quick Guide

Which AIs to use, and how to use them…

Against "Brain Damage"
Practice

Against "Brain Damage"

AI can help, or hurt, our thinking…

The Bitter Lesson versus The Garbage Can
Practice

The Bitter Lesson versus The Garbage Can

Does process matter? We are about to find out.…

GPT-5: It Just Does Stuff
Practice

GPT-5: It Just Does Stuff

Putting the AI in Charge…

Mass Intelligence
Practice

Mass Intelligence

From GPT-5 to nano banana: everyone is getting access to powerful AI…

On Working with Wizards
Practice

On Working with Wizards

Verifying magic on the jagged frontier…

Real AI Agents and Real Work
Practice

Real AI Agents and Real Work

The race between human-centered work and infinite PowerPoints…

An Opinionated Guide to Using AI Right Now
Practice

An Opinionated Guide to Using AI Right Now

What AI to use in late 2025…