AI agents are becoming a vital tool for automating tasks and enhancing decision-making across industries. However, understanding how to create these agents from scratch in Python can be daunting without the right foundation. From knowing where AI agents can be applied to understanding their underlying architecture, learning these fundamentals is key to building effective and efficient agents that deliver real-world value.
In the second part of this series, Richmond Alake, Staff Developer Advocate for AI and ML at MongoDB, teaches you how to put the concepts you learned yesterday into practice. You'll build an AI agent from scratch and test its capabilities.
This is part two of a two part session.
Presenter Bio
Richmond AlakeStaff Developer Advocate for AI and ML at MongoDB
Richmond Alake is a Developer Advocate at MongoDB, where he focuses on AI/ML technologies. He creates educational content for developers and MongoDB users, helping them build AI solutions leveraging MongoDB’s capabilities, including vector search and large language models (LLMs). Richmond has a strong background in AI, computer vision, and software development. He has also written numerous technical articles and teaches AI-related courses. Prior to MongoDB, he was a Machine Learning Architect at Slalom Build, a Program Lead at Emeritus, and a Computer Vision Engineer at Loveshark.
