2025-12-11 –, Machine Learning & AI
This hands-on tutorial will guide participants through the process of building intelligent automation solutions using the Google Gemini API, LangChain, and LangGraph. We will focus specifically on the powerful concept of generative AI (GenAI) agents, demonstrating how to construct autonomous workflows that tackle everyday, tedious tasks. Through a series of practical, real-world examples, attendees will learn to design, implement, and deploy LLM-powered agents to streamline their work.
Learning Objectives:
Upon completion of this tutorial, participants will be able to:
- Understand the core concepts of LLM agents and their applications in automation.
- Interact with the Google Gemini API for various generative tasks.
- Utilize LangChain for prompt engineering, model integration, and tool utilization.
- Employ LangGraph to orchestrate complex multi-step agent workflows.
- Design and implement GenAI agents for practical productivity automation.
- Debug and iterate on agentic solutions.
Technical Requirements:
- Laptop with Python 3.9+ installed
- Google Cloud Project with Gemini API enabled (instructions for setup will be provided)
- Jupyter Notebook or a preferred IDE
Tutorial Structure (Hands-on Examples):
The tutorial will be driven by 2-3 practical examples, each building upon the concepts introduced previously. For each example, we will:
- Define the Problem: Clearly articulate the "boring" task we aim to automate.
- Outline the Agentic Approach: Describe how an LLM agent will solve the problem, including the necessary tools, steps, and decision points.
- Code Implementation: Live coding and guided exercises to build the agent using Python, Gemini, LangChain, and LangGraph.
- Demonstration and Discussion: Showcase the working solution and discuss potential improvements and extensions.
Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia, industry, and consulting. Bruno earned his PhD in the Physics of Complex Systems in 2008 and his work focuses on applying advanced techniques to real-world scenarios, bridging the gap between academic research and business solutions. Bruno has developed and taught courses on Machine Learning, Generative Artificial Intelligence, Blockchain Analytics, and Data Mining. He provides specialized consulting services, helping companies leverage data science, machine learning, and blockchain analytics to solve complex problems.