Introduction to Creating AI Agents in Python (Part 1: Concepts)
Key Takeaways:- Learn practical applications and examples of AI agents.
- Understand the architecture and design principles behind AI agents.
- Explore Python code examples and best practices for creating AI agents.
Description
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 this introductory session, Richmond Alake, Staff Developer Advocate for AI and ML at MongoDB, walks you through the basics of creating AI agents in Python. You’ll explore real-world examples of how AI agents are used, gain insight into their architecture, and learn best practices for building them. Through hands-on code examples, you’ll also discover common pitfalls to avoid when developing AI agents. Whether you’re new to AI or looking to expand your Python skills, this session will give you the tools to start creating AI agents with confidence.
This is part one of a two part session.
Presenter Bio
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.