36 articles
This article describes building event-driven data agents that detect and investigate anomalies using BigQuery, Pub/Sub, and Vertex AI.
This guide presents a structured approach to migrating on-premises load balancer configurations to Google Cloud's Application Load Balancer.
This post walks through deploying a LLM (DeepSeek) on Google Kubernetes Engine using managed DRANET and GKE Inference Gateway with NVIDIA B200 GPUs.
Google Cloud NGFW Enterprise introduces domain and SNI-based URL filtering with wildcard support to overcome the limitations of IP-based firewall rules in cloud environments.
This article explains how Envoy proxy addresses the networking and policy enforcement challenges unique to agentic AI systems.
This post introduces Google Cloud database labs that demonstrate how to prepare and use data layers for production-ready AI applications.
This article describes Part 2 of building Dev Signal, a multi-agent system on Google Cloud that transforms community signals into technical content using ADK, MCP, and Vertex AI.
This article explains the concept of an efficient frontier for LLM inference and five practical techniques to reach it.
UC Berkeley student research reveals how the next generation of developers is building genuine AI literacy rather than dependency.
This post explores building distributed AI agents using the orchestrator pattern with Google's Agent Development Kit (ADK) and Agent-to-Agent (A2A) protocol.
This post introduces Dev Signal, a multi-agent system built with Google ADK that uses MCP servers to discover Reddit questions, research answers via Google Cloud docs, and generate technical blog posts with custom visuals.
Google announces the preview of multi-cluster GKE Inference Gateway for scalable AI/ML inference workloads across multiple GKE clusters and regions.
This article covers architectural best practices for building resilient LLM applications on Vertex AI to minimize 429 (Resource Exhausted) errors.
Google is hosting the Gemini Live Agent Challenge, a hackathon for developers to build multimodal AI agents on Google Cloud.
This post demonstrates how to run cost-effective multi-tenant AI workloads on GKE by combining GPU time-sharing with vCluster isolation.
This post introduces an enterprise AI architecture framework arguing that data strategy and AI strategy are inseparable in 2026, using Google Cloud's database services as the foundation.
