Building an App with Code Agents and MongoDB
Learn to build and improve an application using MongoDB Agent Skills, the MongoDB MCP Server, and Atlas Vector Search. Confidently apply AI to evaluate, optimize, and extend real applications while staying in control as the human in the loop. Build and wire up the Schema Design, Query Optimizer, and AI Integrator agent skills to redesign document schemas around real access patterns, eliminate slow queries with targeted indexes, and add semantic search powered by Voyage AI and Vector Search, giving you a production-ready application backed by documented decisions and the observability habits to verify their impact.
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Upon completion of the Building an App with Code Agents and MongoDB skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Understand AI-Assisted Application Development with MongoDB
Explore how AI coding agents support the full development lifecycle, including schema design, query optimization, and feature extension, to take an app from proof of concept to production-ready.
Use MongoDB MCP Server and MongoDB Agent Skills
Connect AI coding agents to a live MongoDB deployment using the MongoDB MCP Server and apply Agent Skills as part of a modern, structured development workflow.
Design, Optimize, and Extend a MongoDB Application
Use MongoDB Agent Skills to evaluate and redesign a data model, diagnose and improve slow queries, and add semantic search using MongoDB Vector Search, moving the application from proof of concept to production quality step by step.
Develop Disciplined AI-Assisted Development Habits
Build the habit of critically reviewing agent output, documenting prompts, recommendations, and decisions, and using that documentation to plan for production observability.
Daniel Curran | Senior Manager, Curriculum Designer
Daniel Curran is a Senior Manager, Curriculum Designer at MongoDB, where he designs hands-on learning experiences that help developers build practical MongoDB skills through labs, skill badges, and technical courses. His recent work includes curriculum on memory for AI applications, building apps with code agents, and data resilience, with a focus on making complex topics clear, useful, and immediately applicable for learners.
Joel Lord | Lead Curriculum Engineer
Joel is a Lead Curriculum Engineer at MongoDB, focused on helping developers build better applications through accessible educational content. He started his career in software nearly 25 years ago and only paused briefly to pick up a B.Sc. in computational astrophysics from Université Laval. Since then, he’s worked across software development, developer advocacy, and technical education. Outside of work, he enjoys stargazing, homebrewing, and providing emotional support to his two cats, who frequently make guest appearances on Zoom.
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.
Manuel Fontan Garcia | Staff Technologist
Manuel is a Staff Technologist on MongoDB’s Curriculum team, where he focuses on AI-assisted curriculum development and GenAI education. He has contributed to MongoDB University’s AI and Search training portfolio across topics including vector search, RAG, AI agents, memory, and Voyage AI integrations. He has also led the development of Curriculum AI tooling to improve the speed, consistency, and quality of content creation and maintenance. Previously, Manuel was a Senior Technical Services Engineer on MongoDB’s Core team. Between his two tenures at MongoDB, he worked for a little over two years as a Database Reliability Engineer at Slack and later at Cognite before rejoining the company. With more than 15 years of experience in software development and distributed systems, Manuel is naturally curious and holds an MSc in Telecommunications Engineering from the University of Vigo, Spain, and an MSc in Free and Open Source Software from Rey Juan Carlos University, Spain.
Sarah Evans | Senior Curriculum Engineer
Sarah Evans is a Senior Curriculum Engineer at MongoDB, where she designs and develops technical learning experiences for MongoDB University. With a background that bridges curriculum design and hands-on technical expertise, she specializes in translating complex database and data modeling concepts into clear, accessible content for developers and technology professionals. Sarah is passionate about practical, engaging technical education and brings a deep interest in AI-driven development to her work helping learners navigate the intersection of modern databases and intelligent application design.
Welcome to this Builder Badge on Building an App with Code Agents! I’m Aaron Becker, and I’m a Curriculum Engineer at MongoDB.
If you’re using MongoDB for the first time or an experienced MongoDB user who’s just starting to experiment with AI Agents, this skill badge is for you!
I'll walk you through everything you need to take the data layer of an application from POC to production-ready, using the right AI tools, patterns, and MongoDB-powered infrastructure to get you there.
AI coding agents have changed what's possible for developers. Tasks that used to take days of research and trial and error like designing a data model, diagnosing slow queries, wiring in a vector search feature can now move in hours.
Having those agents connected directly to your MongoDB deployment can reduce the time between recognizing what needs to be done and actually doing it.
As with most tools, though, the value you get tends to reflect how well you've learned to use it.
In this builder badge, you'll take a POC e-commerce app and use AI coding agents, guided by MongoDB Agent Skills, to prepare the data layer to scale, step by step and decision by decision.
Agent skills are opinionated workflows that guide the agent to complete tasks reliably by following established best practices, patterns, and step-by-step workflows.
The agent skills that you'll use to complete this badge are designed to follow MongoDB best practices as they work. These skills are available for you to use outside of this builder badge in your own environment.
Think of each agent skill as a knowledgeable collaborator that can analyze, propose, and explain tradeoffs.
Your job is to engage with the agent's output critically: read it, weigh the options, and make decisions.
You’ll put this into practice across four tasks.
First, you'll use the MongoDB Schema Design agent skill to evaluate the current data model and propose alternatives.
Second, you'll use the MongoDB Query Optimizer agent skill to diagnose slow queries and implement improvements.
Third, you'll use the MongoDB Vector Setup agent skill to add semantic search using MongoDB Vector Search.
And fourth, you'll use your AI agent to plan for production observability, closing the loop on the decisions you've made.
Along the way you’ll be creating documentation to record the decisions you make.
This is an important step because developing that habit of reviewing, refining, and documenting is one of the core skills this badge is designed to build.
By the end of this badge, you'll have used AI-assisted workflows to take a POC and make targeted improvements to its data layer, with your architectural decisions documented and their impact measurable.
Once you complete all the content, you'll earn an official Credly badge you can share on LinkedIn as proof of your skills.
Let's get started.
