Build Production AI Agents with Python: LLMs, RAG & MCP

About this course

This course contains the use of artificial intelligence.

Stop building AI agent demos that fall apart in front of real users. In this hands-on course you’ll build, test, and deploy a real production-grade AI agent in Python — from the raw LLM API all the way to a live service.

Most “AI agent” tutorials end at a clever prompt and a toy that breaks the moment it meets the real world. This course is different: you build one real codebase, lecture by lecture, until you have a complete agent that plans, uses tools, remembers, stays safe, and runs in production.

You’ll start from the completion API and write the agent loop yourself — so you actually understand how agents work instead of copy-pasting a framework. Then you’ll add everything that turns a prototype into a product:

Tools & function calling — real abilities, and how to design tools that workMCP (Model Context Protocol) — connect your agent to any system with reusable servers

Memory & RAG — stop your agent forgetting and ground answers in your own data

Planning & multi-agent patterns — plus when a single agent is the smarter choice

Guardrails & human-in-the-loop — block prompt injection and gate risky actions

Evaluation & testing — eval datasets, LLM-as-judge scoring, and catching regressions in CIObservability, cost & performance — trace every run and cut your token bill

Deployment — wrap the agent in FastAPI, containerize with Docker, and ship it live

You’ll finish with a capstone: a complete reference agent and a step-by-step plan to adapt it to your own domain in a week.

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