Production-Ready AI Agents with Python: From Foundations to Agentic Systems
Master Python for AI development, build reliable AI agents from first principles, implement high-performance hybrid search RAG pipelines, and establish systematic LLM evaluations for production-grade 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
Course Content
Python Foundations and Development Environment Setup
Setting Up a Modern Python Workspace with VS Code and UV
Managing Secrets and Enforcing Code Quality with Ruff
The Core Building Blocks of Reliable AI Agents
The Seven Foundational Building Blocks of Reliable AI Agents
Implementing Agents from Scratch in Pure Python
Building a Complete Single-File AI Coding Agent
Data Preparation, Extraction, and Hybrid Search in RAG
Document Parsing, Extraction, and Chunking with Docling
Implementing Production-Grade Hybrid Search
Building Agentic Workflows and Model Context Protocol (MCP) Integration
Designing Effective Agent Workflows: From Chaining to Routing
Model Context Protocol (MCP): Connecting LLMs to Data Sources
Systematic LLM Evaluations and Production Deployment
Systematic LLM Evaluations: Metrics, Unit Tests, and LLM-as-a-Judge
Production Deployment and the AI Automation Stack
This course includes
- 6h 8m on-demand video
- 11 lessons across 5 chapters
- 5 interactive quizzes
- Access on mobile and desktop
- Source playlist on YouTube
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