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AI Agents for Beginners: Build Alex, your AI HR Assistant
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 52m | Size: 2.5 GB
Learn 6 building blocks of AI agents: LLM, Prompts, ReAct, Tools, Memory and Planning.
What you'll learn
Build an AI agent from scratch in Python using the OpenAI API, without hiding behind LangChain or similar frameworks
Explain how an AI agent differs from a chatbot, and why the ReAct think-act-observe loop is the core control flow
Call an LLM from Python and work with messages, tokens, temperature, and model responses with confidence
Write system prompts that set persona, rules, output format, and guardrails for reliable agent behavior
Implement a ReAct agent loop in Python with stop conditions and max iterations so the agent can finish a multi-step task
Add tools with OpenAI function calling so the agent can search the web and fetch page content in real time
Add short-term memory so the agent keeps job, background, and conversation context across follow-up questions
Use chain-of-thought and structured planning to break a complex goal into ordered steps the agent can execute
Assemble all six building blocks into a Job Application Coach that researches companies and drafts tailored materials
Treat the finished agent as a reusable framework you can extend with new tools, prompts, and use cases
Requirements
Beginner Python is enough: variables, functions, lists, and running a Jupyter notebook
No prior AI, machine learning experience required
Description
Most AI-agent tutorials start with a framework. A few lines of code later, you have an “agent”—but it can still be hard to understand what is actually happening underneath.
This course takes the opposite approach.
You will learn how AI agents work from first principles, build each of the 6 components yourself in Python, and then combine everything into a completeJob Application Coach - Alex!.
Learn Agents One Building Block At A Time
Block 1 — LLM Brain
Call an LLM and understand messages, responses, tokens, temperature, and conversation history.
Block 2 — System Prompt
Learn how instructions define your agent's role, task, behavior, output format, and guardrails.
Block 3 — Agent Loop (ReAct)
Build the coreThink → Act → Observe loop yourself in plain Python and see how an agent moves from one step to the next.
Block 4 — Tools
Connect Tools using OpenAI function calling so your agent can search the web and use external information. First we will build Mock tools then add Tavily search capability.
Block 5 — Memory
Give your agent conversation and session memory. This will teach you the importance of memory management for Agents.
Block 6 — Planning
Teach your agent to recognize complex requests, break them into steps, and execute those steps in sequence.
Then Build The Complete Agent
Finally, you combine all six blocks into a workingJob Application Coach that can research jobs and companies, analyze job fit, review resumes, help draft cover letters, and prepare users for interviews.
More importantly, you will understand the architecture behind it.
Best part - You can make Alex your own!
After the course, you can change the system prompt, add new tools, improve memory, or adapt the same architecture to build agents for completely different use cases.You will all the code.
Who this course is for
Beginners who want to understand AI agents from the inside, not only click through a framework tutorial
Python learners who have written basic scripts and now want a practical AI project
Developers and analysts who have used ᑕᕼᗩTGᑭT and want to build agents that use tools and take multiple steps
Business-oriented professionals who want an intuitive understanding of how AI agent systems are designed.
Career changers and students who want a complete, working agent they can show and then customize
Anyone who has felt lost in LangChain or LangGraph and wants the underlying building blocks first
Not for people looking for a no-code ᑕᕼᗩTGᑭT walkthrough, or an advanced multi-agent production architecture course
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