AI Agent Development with Python

This course contains the use of artificial intelligence.
Welcome to AI Agent Development with Python, a hands-on course designed to help you build real-world AI agents, AI assistants, tool-using agents, research agents, and multi-agent applications from scratch.

In this course, you will learn how modern Generative AI applications are built using Python. Instead of only learning theory, you will build practical projects that show how AI agents can understand user requests, plan tasks, use tools, remember information, search documents, perform research, and collaborate with other specialized agents.

We will start with the foundations of AI agents and learn how they are different from simple chatbots. You will build your first Python AI assistant, understand prompts, conversation history, structured responses, and the basic agent loop. From there, you will move into agent planning, where your application can break larger goals into smaller, actionable steps.

Next, you will build tool-calling AI agents that can interact with Python functions, APIs, files, calculators, search tools, and business workflows. You will learn how to design tools, route user requests, handle tool responses, and create safer, more reliable agent behavior.

You will also learn how to build RAG applications using Retrieval-Augmented Generation. This means your AI agent will be able to answer questions from private documents, PDFs, policies, manuals, and knowledge bases. You will work with embeddings, vector databases, document chunking, semantic search, and memory systems so your agents can provide more useful and grounded responses.

As the course progresses, you will build an autonomous research agent that can gather information, organize findings, summarize insights, and generate useful reports. You will also learn how to create multi-agent systems, where different agents work together as a team—such as a planner agent, research agent, tool agent, analyst agent, writer agent, and reviewer agent.

This course uses Python, Ollama, local LLMs, Streamlit, RAG, agent workflows, and multi-agent architecture to help you understand how practical AI applications are designed and built. No OpenAI API is required, and you will learn how to work with local models using Ollama.

By the end of this course, you will understand how to design and build complete AI agent applications with Python. You will be able to create AI assistants, automate workflows, build document question-answering apps, develop research agents, and design multi-agent applications for real-world use cases.

This course is ideal for Python developers, software engineers, students, freelancers, IT professionals, and anyone who wants to learn Generative AI development, AI automation, LLM applications, RAG systems, and autonomous AI agents through hands-on projects.