Learn AI Agents Build Intelligent AI Agents from Scratch

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Learn AI Agents  Build Intelligent AI Agents from Scratch
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 32m | Size: 836.12 MB
Learn Agentic AI from Scratch and Build Practical AI Agents with Python, OpenAI, Gemini, Memory, Tools, and LLMs.

What you'll learn
Understand Agentic AI, AI Agents, Generative AI, LLMs, and how intelligent agents think, plan, act, and respond.
Build practical AI Agents with Python using modern LLM APIs and understand the complete AI Agent development workflow.
Use OpenAI API and Gemini API with Python to send prompts, receive AI responses, and create intelligent AI applications.
Add memory, reasoning, planning, tools, actions, and context to build smarter and more capable Agentic AI systems.
Build real-world AI Agents for research, translation, coding, debugging, email writing, summarization, and other tasks.
Learn function calling, structured JSON output, external APIs, error handling, Prompt Engineering, and AI cost optimization.
Requirements
A computer with Python installed
Basic Python knowledge
VS Code or another code editor
Interest in Artificial Intelligence
Description
Welcome toAgentic AI & AI Agents with Python, a practical and beginner-friendly course designed to help you understand AI Agents from scratch and build real AI Agents using Python and modern Large Language Model APIs.
Artificial Intelligence is rapidly moving beyond simple chatbots. ModernAgentic AI systems can understand instructions, reason about problems, create plans, use tools, remember information, interact with APIs, perform actions, observe results, and complete multi-step tasks.
In this course, you will learn how these systems work and, more importantly, how tobuild an AI Agent yourself using Python.
You do not need advanced Artificial Intelligence, Machine Learning, or Data Science knowledge. The course starts with fundamental concepts such asArtificial Intelligence, Machine Learning, Generative AI, LLMs, and AI Agents, and gradually moves toward practical AI Agent development.
You will understand the complete AI Agent workflow
Input → Reasoning → Planning → Action → Memory → Output
You will also study the important Agent Loop
Think → Plan → Act → Observe → Repeat
Instead of learning only theory, you will build multiple practical AI Agent projects including aMath Agent, Grammar Agent, Translator Agent, Summarizer Agent, Research Agent, Travel Planner Agent, Email Writing Agent, Code Generator Agent, Debugging Agent, Blog Writing Agent, Resume Agent, and Meeting Notes Agent.
Finally, you will combine the concepts into a completePersonal AI Assistant capable of answering questions, remembering conversations, working with multiple tools, generating code, summarizing information, translating text, writing emails, and generating ideas.
What You Will Learn
- UnderstandArtificial Intelligence (AI) from a beginner level
- Understand the basic idea ofMachine Learning
- UnderstandGenerative AI
- Understand what aLarge Language Model (LLM) is
- UnderstandAgentic AI
- Understand what anAI Agent is and how it works
- Understand the difference between anAI Assistant and AI Agent
- Compare traditional programs with intelligent AI Agents
- Understand how AI Agents reason, plan, act, and observe
- Understand the completeAI Agent lifecycle
- Understand modernAI Agent architecture
- Work with prompts, system prompts, user prompts, and context
- Understandshort-term and long-term memory
- Add conversation memory to an AI Agent
- Understand planning and reasoning
- Implement AI Agent decision-making concepts
- Understand AI Agent tools, actions, environments, and observations
- Build anAI Agent loop
- Create Python virtual environments
- Organize an AI Agent Python project
- Work with modernLLM APIs
- Use theOpenAI API with Python
- Understand working with theGemini API
- Send prompts to an LLM and receive responses
- Create anAI Agent class in Python
- Give an AI Agent a role or personality
- Save AI Agent conversation history
- Test and improve AI Agent responses
- Build practical task-specific AI Agents
- ApplyPrompt Engineering to AI Agents
- Implement error handling
- Understand API rate limits
- Optimize AI API costs
- Generatestructured JSON output
- UnderstandFunction Calling
- Connect AI Agents with external tools
- Connect external APIs with AI Agents
- Improve AI Agent performance
- Debug AI Agent applications
- Build multi-step AI workflows
- Understand autonomous AI Agent concepts
- Build a completePersonal AI Assistant
- Explore modern Python AI Agent frameworks and libraries
Important Sections (Modules)
Section 1 - Welcome to AI Agents
Section 2 - AI Agents Fundamentals
Section 3 - AI Agent Components
Section 4 - Building Your First AI Agent
Section 5 - Building Practical AI Agents
- Math Agent
- Grammar Agent
- Translator Agent
- Summarizer Agent
- Research Agent
- Travel Planner Agent
- Email Writing Agent
- Code Generator Agent
- Debugging Agent
- Blog Writing Agent
- Resume Agent
- Meeting Notes Agent
These projects help you understand how the sameAgentic AI architecture can be adapted to solve many different real-world problems.
Section 6 - Advanced AI Agent Features
Section 7 - Real World Project: Personal AI Assistant
Section 8 - AI Agent Career, Libraries & Next Steps
The course focuses first on understanding AI Agent development rather than hiding everything behind frameworks. Once you understand the fundamentals, these frameworks become much easier to explore.
No previous AI, Machine Learning, Generative AI, LLM, or AI Agent knowledge is required.
Why Take This Course?
Many AI courses immediately introduce large frameworks, which can make it difficult for beginners to understand what is actually happening behind the scenes.
This course follows a simpler learning path
Learn → Understand → Build → Improve → Apply
You first understand the fundamentals ofArtificial Intelligence, Generative AI, LLMs, and AI Agents.
Then you understand
Prompts → Context → Memory → Reasoning → Planning → Tools → Actions
After that, you start building practicalAI Agents with Python.
This approach helps you understand not onlyhow to build an AI Agent, but alsohow and why the Agent works.
Build Your Agentic AI FoundationAI Agents and Agentic AI are becoming an important part of modern Artificial Intelligence development.
Whether you want to become anAI Developer, Python Developer, Generative AI Developer, AI Automation Developer, AI Agent Developer, or eventually explore areas such asmulti-agent systems or AI voice agent applications, understanding the fundamentals of AI Agents gives you a strong foundation.
Please note that the course focuses on Python-based text and tool-using AI Agents; a dedicatedAI Voice Agent is not one of the projects in the current curriculum.
Start learningAgentic AI and AI Agents with Python and build your own practical intelligent Agents from scratch.
Who this course is for
Python beginners who want to start learning AI Agent development.
Python developers who want to build practical AI Agents and Agentic AI applications.
Students and beginners who want a step-by-step introduction to AI Agents.
AI and Generative AI enthusiasts interested in LLMs, autonomous agents, memory, and tools.
Data scientists interested in expanding their skills into Agentic AI and AI Agent development.
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