☕ How I Built My First AI Agent
A Journey from Idea to Execution
“Ek AI agent banate hain jo Hitesh Choudhary jaise baat kare!” – This casual thought over evening chai turned into one of my most fulfilling coding experiments so far.
In this article, I’ll walk you through how I built my first AI agent – from ideation to writing code, from hitting errors to watching it finally work. If you're someone who loves building things and is curious about AI, this story is for you.
🎯 The Idea: Making Learning More Personal
I’ve always been a fan of Hitesh Choudhary – his teaching style, his energy, his humor, and how he simplifies complex topics. One day, I thought:
“What if we could talk to an AI version of Hitesh sir? Like a coding mentor who’s always available?”
And boom – the seed was planted.
My goal:
Build a chat-based AI assistant that talks like Hitesh sir, explains like him, and guides like a real mentor.
🛠️ Tech Stack I Used
To make this happen, I picked the following tools:
- Python – for quick prototyping
- Streamlit – to build a fast, clean chat UI
- Gemini (Google Generative AI) – as the brain behind the bot
- Dotenv – to keep API keys secure
- Custom Prompt Engineering – to shape the AI’s desi mentor personality
📜 The Persona Prompt: Breathing Life into the Agent
The magic ingredient was the persona_prompt.py file.
I crafted a detailed prompt in Hinglish that described:
- Hitesh sir’s tone (desi, fun, slightly sarcastic)
- His real-world explanation style
- His typical phrases (e.g., “Namaskar doston”, “Chai leke aao”, “Ab samjho…”)
- His motivational and teaching tone
This gave the AI its unique personality.
💬 Building the Chat UI (Like WhatsApp!)
I didn’t want it to feel robotic. So, I used Streamlit and added:
- A modern chat UI with custom CSS
- User + AI chat bubbles (styled like WhatsApp)
- A typing animation instead of boring “loading…”
- Color-coded messages (purple for user, orange for AI)
- And of course, some ☕🚀💻 emojis for flavor
🧠 Making the AI Smarter: Handling Repeated Questions
Sometimes users repeat questions with slight changes. To manage that:
- I added a hashing mechanism to detect repeated intent
- The AI then gives a different type of response:
- Step-by-step explanation
- Real-world analogy
- Problem–solution format
- Industry insights
This kept the experience fresh and helpful.
🐞 Bugs, Errors & “Kuch toh gadbad hai!”
The journey wasn’t smooth:
- API errors (
401 unauthorized,model not found) - Streamlit throwing
experimental_rerunerrors - Blank chat messages
- Typing animations breaking randomly
But each bug taught me something new. And that’s the beauty of building.
🔐 Handling Secrets & Pushing to GitHub
Good practices I followed:
- Used
.envto store secrets (like Gemini API key) - Added
.venvto.gitignoreto keep the repo clean - Added a
requirements.txtfor easy setup - Wrote a clean
README.mdso others can understand and run it
🧪 Testing the Final Product
I asked the AI:
“React kya hota hai?”
It responded in Hinglish, with real-world analogy and ended with:
“Ab samjha? Agar nahi, toh batao – ek aur example se samjhata hoon!”
That’s when I knew… It worked!
❤️ What I Learned
- Prompt engineering is more important than model tuning
- Good UI/UX makes dev tools more engaging
- Small touches (like typing animations) leave a big impact
- Most importantly: Execution > Perfection
🚀 What’s Next?
I plan to:
- Add voice support
- Let users pick from different mentor personas
- Deploy it as a full website to help learners 24x7
🙌 Final Thoughts
This wasn’t just a side project. It felt like I built a small part of mentorship into software.
If you’ve ever wanted to build your own AI agent, here’s my advice:
Start small. Focus on the personality. Let AI do the rest.
Aur haan… Chai leke baithna. Kaam mazedar hoga. ☕😉
💬 Want to Try the Hitesh AI Agent?
Check it out Live - Click Here Source Code - Github
App can take time for initial chat as it is deployed using free hosting by streamkit
