AI & Machine Learning💻 Technical CourseLearnAspire Certified
Your First AI Agent: Build, Test, and Deploy a Tool-Calling Agent with Python and the OpenAI API
“Understand every single line of your first AI agent.”
From zero to a working, deployed AI agent — one concept, one step at a time
Beginner13h7 modules21 exercises28 quiz Qs✓ Verified Apr 2026
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999
One-time · Lifetime access · Certificate included
7-day money-back guarantee
- ✓7 modules of content
- ✓21 practical exercises
- ✓28 quiz questions
- ✓Capstone project
- ✓LearnAspire certificate
Learning Outcomes
What you'll learn
→Explain what an AI agent is and how it differs from a plain chatbot, in plain language
→Set up a Python environment from scratch: virtual env, packages, API key, project files
→Write Python functions as agent tools and describe them using JSON schemas
→Implement the full agent loop: send message, detect tool call, execute function, return result, get final answer
→Run your agent, read the debug output, and understand exactly what the model is deciding at each step
→Write pytest tests for your tool functions
→Wrap your agent in a FastAPI endpoint and run it as a local HTTP API
The day after you finish
The day after this course, you can open a blank folder, write a new Python tool function, add its JSON schema, drop it into the agent loop, and watch the model call it correctly — without looking anything up.
Who this is for
- Primary: IT professionals and junior developers who can write basic Python (loops, functions, dicts) but have never called an LLM API, never used any AI library, and have no idea what tool calling or an agent loop is.
- Secondary: Anyone curious about AI agents who wants to understand exactly how they work by building one from scratch.
Prerequisites
- Can write basic Python: loops, functions, dictionaries, if/else
- Comfortable running commands in a terminal
- No prior AI, ML, LangChain, or OpenAI experience required
Curriculum
7 modules · full breakdown
🤖 Part of: AI Engineering Path
Step 1 — Foundations
→Step 2 — Core Skills
→Step 3 — RAG
→Step 4 — LangGraph RAG
→Step 5 — Agent Systems
→Step 6 — Production
→Step 7 — MCP
→Step 8 — Enterprise
🏆
Capstone Project
Personal Utility Agent — Working HTTP API
A personal utility agent (get_current_time, calculate_math, save_note) running as a FastAPI server. POST /chat accepts a natural-language request, routes to the right tool, executes it, and returns an answer. All tool functions have passing pytest tests.