A hands-on, open-source curriculum where you build a complete AI Codebase Analyst from scratch.This is the Official Companion Lab for the book Production-Ready AI Agents.
Price: FREE / OPEN SOURCE
✅ You have a folder full of half-finished Jupyter notebooks that will never see the light of day.✅ You're tired of AI projects that are just brittle, proof-of-concept scripts.✅ You understand the algorithms, but struggle to turn them into secure and reliable applications.✅ You feel stuck in "tutorial hell," learning magic tricks but never learning how to build the stage.
Your final project will be a complete, portfolio-ready application that is:---
🧠 Knowledgeable: Ingests and reasons over entire code repositories using a RAG pipeline.
✅ Reliable: Produces predictable, machine-readable JSON output using Pydantic.
🛡️ Secure: Hardened with security guardrails and verified with automated tests.
💪 Resilient: Engineered to handle real-world failures gracefully with automatic retries and caching.
✨ Interactive: Features a polished, user-friendly web UI built with Chainlit.
🔬 Observable: Fully traceable from end-to-end using LangSmith.
🚀 Deployable: Packaged in a single Docker container, ready to ship.
This project teaches you the tools and models being used in production today to build reliable AI systems.
⚙️ LangGraph (Core Framework):
We build our agent as an explicit state machine (a graph), not a fragile agent chain. This gives you maximum control, reliability, and the ability to handle complex reasoning.↔️ OpenRouter (LLM Access):
Instantly access and experiment with a huge variety of models from OpenAI, Anthropic, Google, and Mistral through a single, unified API.🔬 LangSmith (Observability):
You can't fix what you can't see. We treat observability as a first-class citizen, giving you deep insights into every step of your agent's execution from day one.🖥️ Chainlit (User Interface):
Go from a backend script to a professional, interactive web UI in minutes, not days. Perfect for rapid prototyping and building polished internal tools.📦 Docker (Deployment):
The industry standard for packaging applications. We finish by containerizing the entire system, making it portable and ready for cloud deployment.
Each lesson is a tangible upgrade to your project, instilling the iterative process of professional AI engineering.
🏗️ Module 1: Build the Core Engine (Lessons 1-3)
Lay a professional foundation with an observable workspace, give your agent memory with a state machine, and grant it skills by giving it tools.📚 Module 2: Create the Knowledge Base (Lessons 4-5)
Construct a long-term memory for the agent with a vectorstore (RAG Ingestion) and teach it how to research that knowledge base (RAG Retrieval).🛡️ Module 3: Harden for Production (Lessons 6-8)
Forge the three pillars of a production system: build Reliability with structured outputs, Security with guardrails, and Resiliency with robust error handling.🚀 Module 4: Ship the Final Product (Lessons 9-10)
Move from the command line to the browser by building a professional chat UI, then package the entire application for one-command deployment with Docker.
This guided project is the perfect fit if you are a:---🎯 Python Developer who wants to move beyond basic scripts and build production-grade AI applications.🎯 Data Scientist looking to turn their models and notebooks into robust, deployable applications.🎯 Software Engineer looking to add practical, in-demand AI engineering skills to their resume.🎯 Aspiring AI Engineer who is tired of fragmented tutorials and wants a single, focused project to master modern tooling.---
Prerequisites: You'll get the most out of this if you are already comfortable with Python and understand basic API concepts. This is not a beginner's introduction to programming.
✅ 10-Lesson Curriculum: A detailed, step-by-step roadmap from git init to docker push.✅ Full Source Code: Access to the build-production-ai-agents repository with solution tags for every lesson.✅ Community Access: Join the #course-help channel in the AI Builders HQ Discord to debug with peers.
You can keep hacking on fragile notebooks, or you can build a system that scales.
Want the theory?
Order the Official Textbook on Amazon
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