{"slug": "building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy", "title": "Building End-to-End GenAI Agents on Google Cloud: My Google Gen AI APAC Academy", "summary": "A developer completed the Google Gen AI APAC Academy Cohort 3, building and deploying generative AI agents on Google Cloud. The projects included a customer-facing Coffee Barista RAG agent, a Data Analytics Agent using Gemma 4 and BigQuery MCP Server, and a Coffee Shop Manager Productivity Assistant, all deployed on Cloud Run. The developer gained hands-on experience with RAG, MCP, and production-oriented workflows.", "body_md": "Over the past few weeks, I had the opportunity to participate in the Google Gen AI APAC Academy – Cohort 3, where I worked through hands-on codelabs focused on building and deploying Generative AI agents on Google Cloud.\n\nWhat made this experience valuable for me was that it wasn't just about experimenting with prompts or calling an LLM API. I got to work with AI agents, RAG, cloud deployment, data access, MCP, and production-oriented workflows.\n\nI completed three tracks, each focusing on a different real-world use case.\n\n☕ Track 1 — Customer-Facing Coffee Barista RAG Agent\n\nThe first project focused on building a customer-facing Coffee Barista agent using Google ADK and Cloud Run.\n\nThe goal was to create an agent capable of working with external knowledge rather than relying only on the model's existing knowledge.\n\nThe major concept I explored here was Retrieval-Augmented Generation (RAG).\n\nInstead of simply asking the model to generate an answer, the system can retrieve relevant information and provide that context to the model before generating the response.\n\nThe overall idea can be represented as:\n\nUser\n\n↓\n\nAI Agent\n\n↓\n\nRetrieve Relevant Information\n\n↓\n\nContext + Model\n\n↓\n\nGenerated Response\n\nWorking through this track helped me understand how retrieval can make AI applications more useful for domain-specific scenarios.\n\nI also deployed the application using Cloud Run, giving me practical exposure to taking an AI application beyond the local development environment.\n\n📊 Track 2 — Data Analytics Agent\n\nThe second track moved into a more data-oriented use case.\n\nI built a Data Analytics Agent using Gemma 4, BigQuery MCP Server, and Direct VPC Egress.\n\nThis track was particularly interesting because the agent wasn't just generating text. It was designed around interacting with data.\n\nOne of the important concepts I explored was MCP (Model Context Protocol) and how it can provide a structured way for AI systems to interact with external tools and data sources.\n\nThe high-level workflow was:\n\nUser Question\n\n↓\n\nAI Agent\n\n↓\n\nMCP / BigQuery\n\n↓\n\nData Retrieval\n\n↓\n\nAnalysis\n\n↓\n\nNatural Language Response\n\nThis changed the way I think about AI applications.\n\nAn LLM by itself is powerful, but connecting it to reliable external data and tools makes it much more useful for real-world applications.\n\nI also got exposure to Gemma 4 and cloud networking concepts through Direct VPC Egress.\n\n☕ Track 3 — Coffee Shop Manager Productivity Assistant\n\nThe third track focused on building a Coffee Shop Manager Productivity Assistant and deploying it on Cloud Run.\n\nThis project helped me look at agents from a productivity perspective rather than just a conversational one.\n\nThe interesting part was thinking about how an AI system can assist with operational workflows and provide useful interactions for a specific user role.\n\nThis reinforced an important idea for me:\n\nAn AI agent becomes significantly more useful when it is designed around a specific workflow and given access to the right tools and context.\n\n🧠 What I Learned\n\nThis academy gave me hands-on exposure to several concepts that I had previously understood mostly from theory.\n\nI started with the mindset of:\n\nPrompt → LLM → Response\n\nThe projects showed me a broader architecture:\n\nUser\n\n↓\n\nAgent\n\n↓\n\nReasoning / Decision\n\n↓\n\nTools / Data / Retrieval\n\n↓\n\nAction\n\n↓\n\nResponse\n\nThe ability to connect an AI model with tools, data, and application logic is what makes agent-based systems interesting.\n\nThe Coffee Barista project helped me understand why retrieval is important when an application needs to work with specific knowledge.\n\nInstead of expecting the model to know everything, we can provide relevant information as context.\n\nWorking with the BigQuery MCP Server introduced me to another approach for connecting AI agents with external capabilities.\n\nIt made me think more about tool-enabled AI systems rather than isolated LLM applications.\n\nBuilding something locally is only one part of the process.\n\nDeploying the agents using Cloud Run gave me practical exposure to taking AI applications into a cloud environment.\n\nThe biggest takeaway for me was that building useful GenAI applications isn't only about choosing the right model.\n\nIt involves thinking about:\n\nModels\n\nAgents\n\nRetrieval\n\nTools\n\nData\n\nAPIs\n\nNetworking\n\nDeployment\n\nReliability\n\nUser experience\n\nThat's the direction I want to continue exploring.\n\n🚀 What's Next?\n\nThis experience has strengthened my interest in building GenAI systems rather than just GenAI demos.\n\nI'm particularly interested in exploring how agents can maintain context, use external tools, work with structured and unstructured data, and operate reliably as part of larger software systems.\n\nThe Google Gen AI APAC Academy gave me an opportunity to move from simply learning about these concepts to actually building with them.\n\nA big thank you to Google Cloud and Hack2skill for creating this hands-on learning experience.\n\nI'm excited to keep building, experimenting, and going deeper into Generative AI and AI systems engineering. 🚀", "url": "https://wpnews.pro/news/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy", "canonical_source": "https://dev.to/venkata_reddykummitha_d5/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy-49mn", "published_at": "2026-08-28 17:10:57+00:00", "updated_at": "2026-08-28 17:50:26.885564+00:00", "lang": "en", "topics": ["generative-ai", "ai-agents", "ai-tools", "ai-infrastructure", "mlops"], "entities": ["Google Cloud", "Google ADK", "Cloud Run", "Gemma 4", "BigQuery MCP Server", "Google Gen AI APAC Academy"], "alternates": {"html": "https://wpnews.pro/news/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy", "markdown": "https://wpnews.pro/news/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy.md", "text": "https://wpnews.pro/news/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy.txt", "jsonld": "https://wpnews.pro/news/building-end-to-end-genai-agents-on-google-cloud-my-google-gen-ai-apac-academy.jsonld"}}