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Master Agentic AI Within 2Hours-From Zero To Pro Master
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Master Agentic AI Within 2Hours-From Zero To Pro Master
Understand how AI agents think, then build them step by step in Python: tools, memory, RAG, multi-agent and safety
Created byJeetS | 10X Dev with AI
Last updated 10/2026
English
What you'll learn
- Explain what an AI agent is and how it differs from a chatbot or a fixed workflow.
- Describe the agent loop and the ReAct pattern: reasoning, acting, observing, and repeating
- Call an LLM from Python and request reliable, structured JSON responses
- Build tools and connect them to an agent using tool calling.
- Write a complete Python agent loop with clear stop conditions and error handling.
- Add short-term memory, long-term memory, and RAG to help agents use information from documents
- Design a simple multi-agent system, choose an appropriate pattern, and build a researcher-and-writer team
- Apply guardrails, human approval, testing, evaluation, monitoring, and cost controls when preparing an agent for real-world use
- Build a customer-support agent for BrightCart, a fictional online shop, as a practical capstone project.
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Course content
9 sections • 25 lectures • 1h 51m total length
Requirements
- No specific technical background required. Python and LLM experience can be helpful, but the course is designed for a wide audience. If you’re new to coding, self-study labs cover the essential technical and programming foundations. The main requirement is patience and a willingness to learn.
- No prior AI or machine-learning knowledge is needed
- No programing experience needed. You will learn everything you need to know
- API spending is optional. You can complete the entire course without spending anything on APIs. If you choose to use frontier AI models, a typical budget is under $5, though costs vary with usage. You can spend more to access additional capabilities if you wish.
Description
Everyone is talking about AI agents. But what is an agent, really? And how do you build one?
This course answers both questions in about two hours. Every idea is explained in simple English, with a diagram, and then shown in working Python code. Every code demo in this course was run, and you see the real output.
**Part 1: Foundations**
- What "agentic" means, and how an agent differs from a chatbot.
- The agent loop: think, act, observe, repeat.
- When you should use a simple workflow instead of an agent.
**Part 2: The LLM inside the agent**
- Tokens, context window and effort, in four minutes.
- System prompts and roles.
- Structured output: make the model reply in clean JSON.
**Part 3: Build agents in Python**
- Set up Python and store your API key safely.
- Give the agent tools: functions it can call.
- Write the agent loop yourself, line by line, and stop infinite loops.
- Add memory, RAG (search your own documents), planning and self-reflection.
**Part 4: Multi-agent systems and frameworks**
- Multi-agent patterns: sequential, manager-worker and parallel.
- Build a researcher agent and a writer agent that work together.
- A clear tour of the popular frameworks and MCP, so you know what to learn next.
**Part 5: Production and capstone**
- Guardrails, prompt injection and human approval.
- Testing, monitoring and controlling cost.
- Capstone: build a customer-support agent for BrightCart, a fictional online shop.
All demos use a fictional shop and sample data. Each lesson has a quiz, the code files and a downloadable PDF guide.
## Learning objectives (8)
1. Explain what an AI agent is and how it differs from a chatbot and from a fixed workflow.
2. Describe the agent loop and the ReAct pattern of reasoning and acting.
3. Call an LLM from Python and get reliable structured (JSON) output.
4. Build tools and connect them to an agent with tool calling.
5. Write a complete agent loop in Python with stop conditions and error handling.
6. Add short-term memory, long-term memory and RAG to an agent.
7. Design and build a simple multi-agent system, and choose a suitable pattern.
8. Apply guardrails, human approval, evaluation and cost control before shipping an agent.
## Requirements
- A computer with internet. Windows, macOS or Linux.
- Basic Python (variables, functions, lists, dictionaries). Section 3 shows the full setup.
- An API key for an LLM provider. Running all demos costs very little, usually under 1 US dollar.
- No prior AI or machine-learning knowledge is needed.
## Target audience
- Beginners who want to understand AI agents clearly, from zero.
- Python developers who want to build their first real agents.
- Working professionals and students who want a short, practical course instead of a 20-hour one.
## Level
All levels.
## Keywords
agentic ai, ai agents, build ai agents, python ai agents, llm agents, tool calling, function calling, rag, retrieval augmented generation, multi-agent systems, react pattern, ai agent memory, mcp model context protocol, langgraph, crewai, ai agent for beginners, generative ai, ai automation
*This course uses an AI-generated voice based on the instructor's own voice.*
Who this course is for:
- This course is for anyone curious about AI agents and eager to learn how to build them. Programming experience is helpful, but not required: the course is designed to support learners from a wide range of backgrounds. You’ll progress from understanding how agents work to building them in Python. Along the way, you’ll learn to connect language models to tools, add memory and retrieval, create simple multi-agent systems, and apply practical safeguards such as human approval, evaluation, and cost controls.