My journey with the Google Cloud GenAI Academy APAC Edition Cohort 3, organized by Hack2Skill in collaboration with Google Cloud, was my first proper learning experience with Google Cloud and AI agents. Before this program, I had worked with generative AI and built AI-based projects, but this academy gave me an opportunity to understand how AI agents can connect models with tools, data, applications, and cloud infrastructure.
The academy was not just about watching videos or reading theory. It included hands-on workshops, codelabs, and quizzes, followed by an Ideathon Prototype Submission where we had to turn what we learned into a working project.
I received my Gen AI Master Certificate from the Google Cloud GenAI Academy APAC Edition Cohort 3.
The program was divided into three main tracks:
- Track 1 — Build and Deploy a Customer-Facing AI Agent
- Track 2 — Turn Business Data into Strategic Decisions
- Track 3 — Automate Daily Operations with a Productivity Agent
Each track included workshops, hands-on codelabs, and quizzes. The biggest learning for me was seeing how an AI agent can move beyond a simple chatbot and actually work with data, tools, and cloud services.
Track 1 — Build and Deploy a Customer-Facing AI Agent
The first track introduced me to building an AI agent for customer-facing applications.
One of the hands-on codelabs focused on deploying a RAG AI Agent in Streamlit using Google ADK and Cloud Run.
The example was based on a coffee shop. The goal was to build an AI Barista agent that could answer customer questions and make recommendations based on a menu dataset.
The codelab introduced several important concepts:
- Google Agent Development Kit (ADK) for building the AI agent
- Gemini as the underlying AI model
- Retrieval-Augmented Generation (RAG) to ground the agent's responses in external information
- A menu dataset containing coffee items, tags, and allergens
- Streamlit for creating the chat interface
- Cloud Run for deploying the application
What I found interesting was that the agent was not simply generating answers from the model's general knowledge. It could retrieve information from the menu data and use that information to provide more relevant recommendations, including allergen-related information.
This was my first practical experience seeing how an AI agent, RAG, an application interface, and cloud deployment can work together as one system.
Track 2 — Turn Business Data into Strategic Decisions
The second track focused on using AI agents with business data.
The codelab introduced building and deploying AI agents with Gemini and the BigQuery MCP Server on Cloud Run.
This track helped me understand how an AI agent can interact with structured data instead of only responding to normal text prompts.
The technologies and concepts included:
- Agent Development Kit (ADK)
- Gemini
- BigQuery
- BigQuery MCP Server
- Model Context Protocol (MCP)
- Cloud Run
The idea of MCP was especially interesting to me. Instead of keeping an AI agent isolated, MCP provides a standardized way for AI applications and agents to connect with external tools and data sources.
With the BigQuery MCP Server, an AI agent can access and analyze structured business data stored in BigQuery.
This showed me another side of AI agents: they can act as an interface between users and complex data systems, helping turn raw data into useful information and decisions.
Track 3 — Automate Daily Operations with a Productivity Agent
The third track focused on using an AI agent to automate operational tasks.
The codelab demonstrated a personal agent running on a Cloud Run service, using a coffee shop manager scenario.
The agent could analyze business data and perform tasks through a chat interface.
One of the interesting parts was the use of Cloud Run sandboxes. The agent could dynamically write and execute Python code inside a secure, isolated environment to solve analytical problems.
For example, the coffee shop manager could have the agent analyze Point-of-Sale data together with a university graduation schedule. The agent could then identify possible operational bottlenecks, such as relationships between drink complexity and cashier staffing.
The agent could generate recommendations for:
- Staffing
- Inventory
- Operational planning
It could also prepare operational TODOs, while requiring explicit permission before making changes.
This track helped me understand that productivity agents are not limited to answering questions. They can analyze information, execute code, and assist with real operational workflows.
From Learning to Building — The Ideathon
After completing the learning tracks, the academy moved from learning concepts to actually building a prototype.
The Ideathon Prototype Submission challenged us to bring an idea to life using Google AI and Google Cloud technologies.
The submission required a working prototype along with a social post explaining the solution, technical approach, and use of Google Cloud tools.
The evaluation criteria focused on four areas:
- Authenticity — an original and genuinely built solution
- Usability — an intuitive and accessible experience
- Stability — a reliable working prototype
- Security — appropriate practices for authentication, data protection, and cloud infrastructure
For my submission, I built Reflectora-AI.
Reflectora-AI — A Personal AI Journal
Reflectora-AI is a personal AI-powered journaling application designed to help users reflect on their thoughts and turn them into useful insights.
Instead of treating journaling as just storing text, I wanted to explore how generative AI could help users interact with their own journal entries in different ways.
The application includes different AI modes such as:
- Reflect — explore thoughts and provide reflective responses
- Summarize — turn longer journal entries into concise summaries
- Brainstorm — generate ideas based on the user's thoughts
- Action Plan — turn reflections into practical next steps
- Ask Gemini — interact with the AI about the journal content
The project also focuses on user authentication and personal data management.
For the prototype, I worked with Google technologies including Google AI Studio, Gemini, and Google Cloud services, while exploring concepts around authenticated AI applications and cloud deployment.
The project github repository is available here.
I also created a project demonstration video showing the prototype and how it works.
What I Learned
The biggest takeaway from the academy was understanding that AI agents are more than chat interfaces.
Through the three tracks, I saw different ways an agent can:
- Interact with customers and retrieve information.
- Connect with structured business data.
- Execute code and assist with operational workflows.
- Work with tools and external systems.
- Be deployed as real applications using cloud infrastructure.
The academy also gave me more confidence in exploring Google Cloud and agentic AI development.
Before the program, concepts such as ADK, MCP, RAG, Cloud Run, and AI agents were areas I was still exploring. After completing the hands-on tracks and building my own prototype, these technologies became much more practical and understandable.
I am now looking forward to continuing my journey with Google Cloud, Gemini, AI agents, and cloud-based application development and building more practical projects along the way.
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