Umar Faruq Zubairu
Google Developer Expert
Gombe, Nigeria
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I am a technologist focused on bridging complex AI strategy with real-world implementation. As a Google Developer Expert in Cloud & AI and Google Cloud Champion Innovator, I design scalable, GenAI-powered solutions that help African developers and businesses move from ideas to production-ready apps. As CEO of Ummatore Technologies, I lead the development of InsightVox, an AI-powered voice analytics notebook. I also lead GDG Gombe, mentor emerging talent, and have delivered workshops, talks & keynotes across multiple African DevFest events.
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Developer Superpowers: Building AI Apps Effortlessly with Firebase Studio & Gemini Code Assist
Imagine a development experience where AI not only inspires your ideas but also actively assists you in building them. This session is designed for developers seeking to acquire advanced skills in creating AI-powered applications. We'll dive into how Firebase Studio provides a low-friction, integrated platform for your app's infrastructure, handling everything from authentication to databases. Simultaneously, Gemini Code Assist will transform your coding process, offering smart suggestions, completing code, and even generating entire functions based on your prompts. Discover how this synergy streamlines the whole development lifecycle, reducing boilerplate, accelerating features, and letting you focus on the creative aspects of bringing your AI innovations from a simple prompt to production.
Build & Deploy Serverless AI Pipelines on Google Cloud with Antigravity
Take your AI agents to the cloud! You will use Google Antigravity to autonomously architect, build, and deploy a serverless event-driven document processing pipeline. Together with the AI, you will build a system that ingests files from Cloud Storage, processes them with Cloud Run and Gemini, and stores metadata in BigQuery.
Key Takeaways:
1. AI-Assisted Architecture: Use Antigravity's Planning mode with Gemini models to outline complex Google Cloud architectures and task lists.
2. Infrastructure Generation: Instruct the agent to generate shell scripts that provision Cloud Storage buckets, Pub/Sub topics, and BigQuery datasets.
3. Microservice Deployment: Oversee the AI as it writes and deploys a Python-based Cloud Run application integrated with Gemini on Vertex AI.
4. Automated Verification: Use Antigravity’s Walkthrough artifacts to test and validate the end-to-end data pipeline automatically.
Building and Deploying a Multi-Agent "Zoo Tour Guide" on Cloud Run with Google ADK
Dive into the world of intelligent, tool-using AI agents by building a multi-agent system from scratch! In this hands-on session, we will focus on the code and architecture required to build a "Zoo Tour Guide" agent using the Google Agent Development Kit (ADK). Instead of relying on a single generic AI, attendees will learn how to build a specialized team of agents, including a "Researcher" to find facts and a "Formatter" to polish the final answer. We will explore how to give these agents access to the outside world by connecting them to external tools like the Wikipedia API, and how to orchestrate their interactions for a seamless user experience.
Key Learning Points:
1. Structuring for the ADK: Learn how to properly structure a Python project and set up a development environment using uv for ADK deployment.
2. Implementing Tool-Using Agents: Discover how to equip your AI with external capabilities, such as integrating the LangchainTool to allow your agent to query Wikipedia for general world knowledge.
3. Managing Agent Memory: Understand how to use ToolContext to capture and save user prompts into the agent's short-term memory (state), allowing data to be shared seamlessly between different agents in the workflow.
4. Designing Multi-Agent Workflows: Master the use of SequentialAgent to act as a "back-office manager," automatically passing shared memory and executing specialized sub-agents in a reliable, fixed sequence.
Making Your Google ADK Agents Talk to the World
You’ve built a brilliant AI agent, but it can’t just live in a local testing browser forever. In this session, we’ll explore how to make your Google Agent Development Kit (ADK) creations truly connect with external users and systems by moving beyond the default adk web interface. Attendees will learn how to spin up a local REST API using the adk api_server command, enabling web applications, mobile app backends, and microservices to communicate with their agents over standard HTTP requests. We will also dive into programmatic execution in Python, demonstrating how to natively embed agents into custom applications or data pipelines using the Runner class and InMemorySessionService. Finally, we'll look at using adk run for terminal-based execution, perfect for server environments without a GUI or automated CI/CD workflows. Whether you are building an independent API service or seamlessly embedding AI into an existing app architecture, this workshop will give you the deployment skills to take your agents from isolated prototypes to real-world solutions.
Building and Customizing Your First AI Agent with Google's ADK
Step into the world of AI agents with the Google Agent Development Kit (ADK). In this foundational workshop, developers will learn how to set up their local Python environment, install the ADK, and securely configure API keys using either Google AI Studio or Vertex AI. We will dive deep into the four core parameters that define every agent's identity: model (the reasoning engine), name (the unique identifier), description (for multi-agent routing), and instruction (the behavioral blueprint). Through hands-on exercises, participants will learn how to transform a generic default assistant into a highly specialized persona, such as a patient algebra tutor, and instantly test its behavior using the local adk web visual interface. You will leave this session with a fully functional agent and the foundational skills to guide LLM behavior.
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