Cloud Computing

Let’s be honest: building an AI agent that works once is easy. Building an AI agent that works reliably in production, integrated with your existing React or Node.js application? That’s a whole different ball game. (TL;DR: Want to jump straight to the code? Check out the Course Creator Agent Architecture on GitHub.) We’ve all been […]

Cloud environments with dozens of microservices are now easier to manage than ever, and modern DevOps teams are well-equipped to balance rapid deployments with operational stability — even as monitoring tools surface thousands of daily signals. AWS DevOps Agent is a fully managed autonomous AI Agent that resolves and proactively prevents incidents, continuously improving reliability

A common pattern for applications that read frequently from a database is to offload read-heavy workloads to a read replica. This allows applications to scale without impacting critical write operations on the primary database instance. However, these read-heavy workloads can easily exceed the capacity of a single read replica. While developers can manually implement multiple

Backend developers and architects building high-throughput, low-latency applications increasingly rely on Valkey, an open-source, high-performance key-value datastore that supports a variety of workloads such as caching and message queues. At Google Cloud, we offer a fully managed version as part of Google Cloud Memorystore, and today, we’re excited to announce the general availability (GA) of

Introduction  Google Threat Intelligence Group (GTIG) has identified a new iOS full-chain exploit that leveraged multiple zero-day vulnerabilities to fully compromise devices. Based on toolmarks in recovered payloads, we believe the exploit chain to be called DarkSword. Since at least November 2025, GTIG has observed multiple commercial surveillance vendors and suspected state-sponsored actors utilizing DarkSword

My team’s mission is to accelerate the developer journey from writing code to running secure AI workloads on Google Cloud. To help developers succeed, we focus on identifying their most pressing questions and building demos that provide straightforward, easy-to-implement solutions. Recently, I was struck with inspiration when the new Developer Knowledge MCP server was released.

The world of artificial intelligence is moving fast, and so is the need to serve models reliably and at scale. Today, we’re thrilled to announce the preview of multi-cluster GKE Inference Gateway to enhance the scalability, resilience, and efficiency of your AI/ML inference workloads across multiple Google Kubernetes Engine (GKE) clusters — even those spanning

The era of agentic AI is fundamentally changing enterprise infrastructure needs. As organizations build systems capable of dynamic reasoning and autonomous execution, the underlying infrastructure must evolve as well. Scaling these agentic workloads alongside massive mixture-of-experts (MoE) architectures demands a deeply optimized co-engineered stack. To meet these demands, we’ve built the Google Cloud AI Hypercomputer,