AI Agents with MongoDB
Learn how to build and deploy AI agents using MongoDB. Understand orchestration, data storage strategies, and how to integrate AI models with real-time databases.
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Upon completion of the AI Agents with MongoDB skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Create a multi-tool AI Agent that leverages MongoDB
Learn how to build an agent and multiple tools that leverage data stored in your MongoDB database.
Understand how tools are called by agents
Define the decision-making capabilities of your agent to ensure it can easily leverage multiple tools.
Define memory for an AI Agent
Describe the role of long-term and short-term memory. Leverage MongoDB to implement memory for your agent.
Note: The AI models used in this learning content, including LLMs and embedding models, are constantly evolving and may have been updated since its creation.
Parker Faucher | University Curriculum Engineer
Parker is a Curriculum Engineer on the Education team at MongoDB. Prior to joining MongoDB, he helped maintain a world class developer bootcamp that was offered in multiple universities. He is a self taught developer who loves being able to give back to the community that has helped him so much.
Emily Pope | Lead Curriculum Designer
Emily Pope is a Lead Curriculum Designer at MongoDB. She loves learning and loves making it easy for others to learn how and when to use deeply technical products. Recently, she's been creating AI and vector search content for MongoDB University. Before that, she's created learning experiences on databases, computer science, full stack development, and even clinical trial design and analysis. Emily holds an Ed.M. in International Education Policy from Harvard Graduate School of Education and began her career as an English teacher in Turkiye with the Fulbright program.
Manuel Fontan Garcia | Senior Technologist, Education
Manuel is a Senior Technologist on the Curriculum team at MongoDB. Previously he was a Senior Technical Services Engineer in the Core team at MongoDB. In between Manuel worked as a database reliability engineer at Slack for a little over 2 years and then for Cognite until he re-joined MongoDB. With over 15 years experience in software development and distributed systems, he is naturally curious and holds a Telecommunications Engineering MSc from Vigo University (Spain) and a Free and Open Source Software MSc from Rey Juan Carlos University (Spain).
Benjamin Flast | Director, Product Management
Ben is a Director of Product at MongoDB focused on Search, Vector Search, and various AI Integrations. He's been at MongoDB for the past 5 years and is excited about the new wave of real-time AI powered applications that are emerging. With a deep interest in Large Language Models, Embedding Models, and agentic experiences, Ben loves to stay on the pulse of new and emerging AI capabilities.
When not immersed in the world of AI, Ben enjoys hitting the slopes skiing, playing strategy games, and a bit of city gardening. He's based in Brooklyn, New York, is very excited about the number of AI startups popping up in the city.
John McCambridge | Curriculum Engineer
John is a Curriculum Engineer on the University team at MongoDB. Before his work as a Curriculum Engineer, he was an instructor and teaching assistant for coding boot camps at UT (Austin), and UCLA. Additionally, he worked as a QA engineer for a startup called Coder and spent five years at Apple Inc. John is a passionate software engineer and educator who enjoys taking complex topics and making them digestible for the community.
Davenson Lombard | Senior Software Engineer
Davenson Lombard is a Senior Software engineer at MongoDB on the Education Team. Prior to that, Davenson was a Technical Services Engineer at MongoDB and a Customer Success architect at Confluent. Davenson holds a Bachelor in Electrical Engineering from Concordia University in Montreal.
Joel Lord | Lead, Curriculum Eng/Technologist
Joel Lord is a curriculum engineer at MongoDB who is committed to empowering developers through education and active community involvement. With more than twenty years of experience in software development, developer advocacy, and technical education, he combines extensive expertise with a dedication to making complex topics more understandable.
Holding a Bachelor of Science in computational astrophysics from Université Laval, Joel started his career in web development before he focused on assisting others in their learning journeys. At MongoDB, he develops educational materials designed to equip developers with the skills to build improved applications, drawing on his wide-ranging experience as a speaker at global conferences.
When he is not working, Joel enjoys stargazing in remote camping areas, experimenting with inventive brewing methods in his garage, and offering emotional support to his two cats, who often appear as guests during his Zoom meetings.
If you've been following AI developments, you've probably noticed the growing buzz around AI agents. These intelligent systems are revolutionizing how we approach problems by leveraging large language models, or LOMs, and tools to perform highly personalized tasks.
Think of having a digital assistant that truly understands your specific needs.
Developing AI agents isn't just a technical exercise. It's a fundamental step in automating and solving complex problems across industries.
Agents have the potential to enhance scalability and decision making in ways we couldn't imagine just a few years ago. From virtual assistants that manage your calendar to health care diagnostic tools that analyze medical data and autonomous vehicles navigating complex environments, to personalized learning systems that adapt to individual needs. AI agents are making a significant impact everywhere we look, and this is just the beginning. What's exciting is how straightforward it is to develop AI agents. Today, companies of all sizes can implement powerful AI solutions with relative ease, opening doors to innovation across every sector. So where does MongoDB fit into this landscape?
MongoDB's flexible document model and powerful query capabilities make it an ideal database for AI agents that need to store, retrieve, and analyze diverse types of data. When we integrate MongoDB with AI agents, we create systems that can think, remember, and learn from experiences. Let me give you a quick example. Imagine a customer service agent that can instantly search through your MongoDB database to find the exact information a user is asking about.
When a customer asks, what's the status of my order? One, two, three, four, five. Your agent can query MongoDB for that specific order document and provide real time updates. Or perhaps you want an agent that can analyze trends across thousands of documents in your collection to deliver meaningful insights.
These are simple examples, but they give you a basic idea of what's possible. Throughout this skill, we'll learn about agents and build our own agents that leverage MongoDB's capabilities.
We'll break this down into manageable steps to ensure you gain practical skills. We'll start by defining what an agent is and exploring use cases. Understanding these fundamentals will give us a solid foundation to build upon. Next, we'll set up the agent by installing all the dependencies.
After that, we'll create tools for our agent to use. These tools will interface between the agent and MongoDB, allowing it to perform specific actions with data. Next, we'll put together the agent by connecting all the tools into a cohesive system that can understand requests and take appropriate actions.
Finally, we'll implement memory so the agent has the ability to retain, retrieve, and use information from past interactions and experiences.
In this skill, you'll learn concepts through detailed videos and hands on labs. Then you'll be ready to take on a short skill check to demonstrate your knowledge. After passing the test, you'll receive an official Credly badge to share on LinkedIn so you can show off your knowledge and skills. Let's get started.
