Production-Ready AI Agents with Python: From Foundations to Agentic Systems

Course Overview

This comprehensive course is designed for developers, data professionals, and aspiring AI engineers who want to build production-grade AI systems and agents from first principles. By moving beyond the hype, you will learn how to design, build, and deploy reliable, deterministic, and scalable agentic workflows using pure Python.

What You'll Learn

• Python Essentials for AI: Set up your development environment with modern tools like VS Code, UV, and Ruff, and master core programming concepts.
• Agent Architecture: Understand the 7 foundational building blocks of reliable agents: intelligence, memory, tools, validation, control, recovery, and feedback.
• Advanced RAG & Data Extraction: Implement hybrid retrieval pipelines combining BM25, dense embeddings, Reciprocal Rank Fusion (RRF), and re-rankers.
• Model Context Protocol (MCP): Connect your AI applications to external data sources and systems using open-source protocols.
• Systematic LLM Evaluation: Build robust evaluation metrics, unit tests, and LLM-as-a-judge frameworks to prevent application failure.

Prerequisites

• Basic understanding of computer science concepts. No prior Python experience is required for the foundation modules, but familiarity with APIs and Large Language Models (LLMs) is helpful for advanced chapters.

Tools & Technologies

• Language: Python
• Package Managers: Pip, UV
• Libraries & Frameworks: Pydantic, FastAPI, Celery, Pandas, Jinja, Instructor, LangChain
• Vector Databases & Search: BM25, Cohere Re-ranker, OpenAI Embeddings