AI Agents for Beginners: Build Alex, your AI HR Assistant

Course Overview

This course teaches the six fundamental building blocks of AI agents: LLM, Prompts, ReAct, Tools, Memory, and Planning. You will build an AI agent from scratch in Python using the OpenAI API, gaining a deep understanding of how AI agents work from first principles. The course culminates in creating a complete Job Application Coach named Alex that can research jobs, analyze fit, review resumes, draft cover letters, and prepare users for interviews.

Key Takeaways

• Build an AI agent in Python without relying on frameworks like LangChain.
• Understand the difference between AI agents and chatbots, focusing on the ReAct think-act-observe loop.
• Call and manage LLMs with control over messages, tokens, temperature, and responses.
• Write system prompts to define agent persona, rules, output format, and guardrails.
• Implement a ReAct agent loop with stop conditions and max iterations for multi-step tasks.
• Add tools via OpenAI function calling for real-time web search and content fetching.
• Incorporate short-term memory to maintain context across conversations.
• Use chain-of-thought and structured planning to break complex goals into executable steps.
• Assemble all components into a reusable Job Application Coach AI agent.

Prerequisites

Basic Python knowledge including variables, functions, lists, and running Jupyter notebooks. No prior AI or machine learning experience is required.

Target Learners

• Beginners wanting to understand AI agents beyond framework tutorials.
• Python learners seeking a practical AI project.
• Developers and analysts familiar with ChatGPT who want to build multi-step AI agents using tools.
• Business professionals seeking an intuitive understanding of AI agent design.
• Career changers and students wanting a complete, customizable AI agent project.
• Those confused by LangChain or LangGraph looking to learn underlying building blocks first.