AI Agent Development with PythonPublished 7/2026
MP4 |
Video: h264, 3840x2160 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 9h 58m |
Size: 9.86 GB
Learn to create AI assistants, research agents, tool-using agents, and multi-agent applications.
What you'll learnBuild AI assistants from scratch using Python and Ollama.
Understand the architecture of modern AI agents and autonomous workflows.
Create planning agents that can break complex goals into actionable tasks.
Build tool-using AI agents that interact with Python functions, APIs, and external services.
Implement Retrieval-Augmented Generation (RAG) to build chatbots that answer questions from private documents.
Add short-term and long-term memory to AI agents using SQLite and vector databases.
Build autonomous research agents that search, analyze, and summarize information.
Design and develop multi-agent systems where specialized AI agents collaborate to solve complex tasks.
Learn prompt engineering techniques for reliable and structured AI agent responses.
Use Pydantic to generate structured outputs and validate AI responses.
Build production-ready AI applications with Streamlit.
Learn LangGraph fundamentals for orchestrating agent workflows and state management.
Implement logging, error handling, retries, and human approval workflows.
Organize AI applications into clean, modular, and reusable Python projects.
Gain the confidence to build your own AI agent applications for business, education, healthcare, hospitality, finance, and more.
RequirementsBasic knowledge of Python programming, including variables, functions, loops, and classes.
Familiarity with using a code editor such as Visual Studio Code is recommended.
A computer running Windows, macOS, or Linux.
No prior experience with AI agents, machine learning, LangGraph, or Generative AI is required.
No knowledge of OpenAI APIs is required-we will build everything using free local models with Ollama.
A willingness to build projects by writing code alongside the instructor.
Internet access for downloading Python packages and open-source AI models.
DescriptionThis 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.
Who this course is forPython developers who want to build practical AI-powered applications.
Software engineers interested in Generative AI and autonomous AI systems.
Developers who want to learn AI Agents, RAG, tool calling, and multi-agent workflows.
Students and recent graduates looking to build an impressive AI project portfolio.
IT professionals who want to transition into AI application development.
Backend developers interested in integrating Large Language Models into real-world software.
Freelancers and consultants who want to build AI solutions for clients.
Startup founders and entrepreneurs looking to develop AI-powered products.
Technical professionals who want hands-on experience with modern AI agent frameworks.
Anyone interested in learning by building complete, real-world AI projects instead of just studying theory.
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