This course contains the use of artificial intelligence.

Learn how to build practical AI agents with Python using modern tools such as Ollama, LangGraph, Streamlit, Pydantic, ChromaDB, and Retrieval-Augmented Generation.

In this hands-on course, you will explore how agentic AI applications are designed, developed, and tested through real-world project examples. Instead of focusing only on basic chatbot interactions, this course introduces structured AI agent workflows that can use tools, retrieve information, follow instructions, and support human decision-making.

You will begin with the foundations of AI agent development, including how agents receive goals, manage prompts, produce structured outputs, and work with Python-based logic. You will build a personal AI assistant project and learn how to organize inputs, outputs, conversation history, and response formats.

Next, you will practice AI planning and task decomposition. You will learn how to break larger objectives into smaller steps, design reliable JSON outputs, and use Pydantic models to validate structured responses. These skills are useful for creating more predictable and maintainable Generative AI applications.

The course then introduces tool-using AI agents. You will create Python functions that an agent can call for tasks such as calculations, file reading, API interactions, data lookup, and controlled automation. You will also explore important engineering concepts such as tool permissions, validation, retries, error handling, and human oversight.

A major part of the course focuses on RAG with Python. You will learn how to process PDF documents, split text into chunks, create embeddings with Ollama, store data in ChromaDB, and retrieve relevant content using semantic search. These lessons demonstrate how Retrieval-Augmented Generation can help AI systems work with private documents and trusted knowledge sources.

You will also build an autonomous research agent project that demonstrates query generation, information gathering, summarization, gap identification, and structured report creation. You will learn how autonomous loops work and how to design controls that help keep AI workflows focused and manageable.

Later in the course, you will explore multi-agent systems using LangGraph. You will design specialized agents such as a planner, researcher, analyst, writer, reviewer, and supervisor. These agents will communicate through shared state and demonstrate how collaborative AI workflows can be orchestrated in a structured way.

Throughout the course, you will work with a simulated airline disruption example. This project shows how an AI workflow can review booking details, compare possible flight options, reference policy information, generate recommendations, and request human approval before any final action.

By the end of the course, you should have hands-on experience with Python AI agents, local LLMs, Ollama AI development, LangGraph workflows, vector databases, AI memory, RAG systems, tool calling, human-in-the-loop AI, and multi-agent collaboration.

This course is ideal for Python developers, AI beginners, automation specialists, software engineers, data professionals, entrepreneurs, and anyone interested in building practical local-first AI agent projects.