AI Agents: Build Intelligent Apps with Python & OpenAI

The Future of AI Isn’t Just Chatbots — It’s Agents That Take Action

Generative AI and Large Language Models (LLMs) have transformed the world — from ChatGPT to AI-powered search engines, autonomous coding assistants, and beyond. But the next frontier isn’t just AI that talks. It’s AI that does things. Welcome to the world of AI Agents — intelligent programs that can reason, use tools, make decisions, and take actions on their own.

In this hands-on course, students go beyond basic AI chat to build real AI-powered applications using Python, OpenAI’s API (the same technology behind ChatGPT), and Streamlit — a professional framework for building interactive web apps. Students will learn cutting-edge concepts like prompt engineering, function calling, structured output, tool use, agentic AI loops, and API integration — the same techniques used by AI engineers at top tech companies today.

By the end of this course, students will have built and deployed three complete AI Agent projects they can showcase in their portfolio.

Technologies & Concepts

  • Python — The #1 programming language for AI and data science
  • OpenAI API — Direct access to the same Large Language Models (LLMs) that power ChatGPT, including GPT-4o-mini
  • Streamlit — A professional Python framework for building interactive web applications
  • Prompt Engineering — The art of crafting instructions that guide AI behavior and output quality
  • Function Calling & Tool Use — Teaching AI agents to use custom tools and take real-world actions
  • Structured Output & JSON Schema — Forcing AI to return precise, machine-readable responses
  • API Integration — Connecting to external services like YouTube Data API and DuckDuckGo Search
  • Streamlit Community Cloud — Free cloud deployment so students can share their apps with a live URL

Prerequisites: Students must be comfortable with Python fundamentals (functions, dictionaries, lists, loops, f-strings) and have basic familiarity with Streamlit. Our Python and Streamlit courses provide the perfect foundation.

Project 1: Survive In Space With AI — An AI-Powered Game

Can your strategy outsmart an AI judge in a life-or-death space emergency?

Students build an exciting AI-powered survival game where the player is stranded on a malfunctioning spaceship. In each round, OpenAI’s LLM generates a unique space emergency scenario — a hull breach, failing life support, or an asteroid collision — and the player must type a survival strategy. Then, an AI Judge evaluates the player’s response and decides their fate: ALIVE or DEAD.

What Students Learn:

  • How to call the OpenAI API programmatically (not just using ChatGPT in a browser — writing real code that communicates with the AI)
  • Structured Output with JSON Schema — forcing the AI to return data in an exact format so our program can read it reliably
  • Prompt engineering — crafting system prompts that make the AI behave as a fair and creative game master
  • Chaining multiple AI calls — one call generates the scenario, a second call judges the player’s response (this is the foundation of agentic AI)
  • Building an interactive web UI with Streamlit, including session state management for multi-round gameplay
  • Adding visual flair with st.balloons(), st.snow(), success/error displays, and sidebar scoreboards

By the end: Students have a fully deployed, shareable web game powered by real AI — not a toy demo, but a genuine application that showcases AI engineering skills.

Project 2: Build Your Own AI Agent with Custom Tools

What happens when you give an AI a calculator, a search engine, and whatever tools you can imagine?

Students discover the core concept that separates a simple chatbot from a true AI Agent: tool use. A regular LLM can only generate text. An AI Agent can decide on its own which tools to call, execute actions, read the results, and respond intelligently — just like giving a brilliant assistant a phone, a calculator, and internet access.

Students build a Streamlit-powered AI chatbot that comes equipped with custom tools — including a prank calculator (with a hilarious hidden trick), a live web search tool powered by DuckDuckGo, and tools the students invent themselves.

What Students Learn:

  • What makes an AI Agent different from a regular AI — agents don’t just talk, they reason and take actions
  • Function Calling — the OpenAI feature that lets the AI request specific tools by name, with the right parameters, automatically
  • The Agentic Tool-Call Loop — the AI requests a tool, our code executes it, and the AI reads the result to form its final answer
  • How to write custom Python functions and register them as AI tools with proper descriptions and parameter schemas
  • Web search integration — giving the AI access to live internet data through DuckDuckGo
  • The System Prompt — hidden instructions that shape the AI’s personality and behavior
  • Responsible AI — discussions on hallucinations, privacy, and when AI should (and shouldn’t) be trusted

By the end: Students have built their own AI Agent with at least 3 working tools — demonstrating a real understanding of how products like ChatGPT plugins, AI coding assistants, and autonomous agents work under the hood.

Project 3: AI Video Research Agent

Ask a question. Get curated YouTube videos and an AI-generated research brief — instantly.

Students build an AI-powered research tool that takes a question (e.g., “How do black holes form?”), searches YouTube using the YouTube Data API v3, and then feeds the results to OpenAI’s LLM for intelligent analysis. The AI groups videos into themes, ranks the top results by relevance, and writes a comprehensive research summary — all displayed in a polished, professional web interface.

What Students Learn:

  • Multi-API integration — combining the YouTube Data API (for video search) with the OpenAI API (for intelligent analysis) in a single application
  • Working with real API responses — parsing JSON data, extracting relevant fields, handling edge cases
  • Advanced prompt engineering — designing prompts that instruct the AI to group, rank, and summarize content without hallucinating
  • Caching with @st.cache_data — a professional optimization technique that saves API quota and makes the app faster
  • Building multi-column layouts, sidebars with interactive controls, image displays, and clickable links
  • Cloud deployment — pushing to GitHub and deploying to Streamlit Community Cloud with secure API key management using TOML secrets
  • Understanding API quotas, token costs, and the economics of building AI-powered products

By the end: Each student has a live, publicly accessible AI research agent they can share with anyone — a portfolio-worthy project that demonstrates real-world API integration, prompt engineering, and full-stack AI application development.

Course Details

  • Duration: Approximately 3-4 months (9 lessons total — 3 lessons per project, 1 hour each)
  • Homework: Assigned after every lesson to reinforce concepts and encourage experimentation
  • Final Deliverable: Three deployed web applications, each with a public URL students can share
  • Class Size: Small groups of 4-7 students for personalized attention

Tuition Fee

  1. Price: $35 / hour or $140 / month (4 weeks in a month)
    1. Class size between 4 to 7.
    2. Smaller class size (less than 4) is possible but with higher hourly rate per person.
  2. Small online group class with live teacher: 4 to 6 students / class
  3. New student registration fee: $25.
  4. Semi Private – $50/hour/student.
  5. We also provide private lesson
    1. Private Lesson Type 1 – $60/hour/student
    2. Private Lesson Type 2 – $100/hour/student.
    3. With all private lessons, if there are cancelation / reschedule within 24-hour, we will charge regular private lesson rate.

Why This Course?

AI is no longer a futuristic concept — it’s a present-day skill. Companies across every industry are hiring AI engineers, and the demand is only growing. This course gives students a real head start by teaching them to build with the same APIs and techniques used in professional AI development. Students won’t just use AI — they’ll understand how it works and build applications that harness its power.

The skills learned in this course — API integration, prompt engineering, agentic AI design, structured output, cloud deployment — are directly applicable to internships, science fair projects, college applications, and future careers in technology.