NetTraceArcade | Cybersecurity, AI & Cloud

AI agents become far more useful when they can work with real data. In this step-by-step tutorial, we build an AI agent using Google's Agent Development Kit (ADK) and Gemini that can securely access and analyze structured data in BigQuery through the BigQuery MCP Server , and then deploy it to Google Cloud Run . Watch the full walkthrough below, then use this post as a reference while you follow along. 📌 Introduction Large Language Models are powerful, but on their own they can't see your business data. To build agents that answer real questions about sales, customers, or operations, they need a safe way to connect to data sources and tools. This tutorial shows one way to do that: an ADK agent powered by Gemini uses the BigQuery MCP server to query structured data, and the whole application runs as a live service on Cloud Run. It is based on the Google Cloud codelab "Build and Deploy AI Agents with Gemini and BigQuery MCP server in Cloud Run." 🎯 Wha...

In this hands-on Google Cloud Skills Boost lab, we build and deploy a customer-facing AI agent using the Google Agent Development Kit (ADK) , Retrieval-Augmented Generation (RAG) , and Cloud Run . The result is an intelligent coffee shop assistant that gives personalized recommendations by combining an AI agent with a knowledge base. The video below shows the full walkthrough, including the build and deployment errors I ran into along the way and how I fixed them. 📌 Introduction AI agents are quickly moving from demos to real customer-facing applications. Instead of a chatbot that only answers from its training data, an agent can reason, use tools, and pull in your own business information to give relevant answers. This lab walks through that idea end to end: build the agent locally, connect it to a knowledge base, and deploy it to Google Cloud Run so it runs as a live web service. 🎯 What You'll Learn Build an AI agent using Google ADK Understand how Retrieval-A...