Today, AI agents are no longer science fiction, but a necessity for businesses operating 24/7. I recently went from an idea to building my own AI agent solution for e-commerce (specifically for the pharmaceutical industry) and want to share the technical insights of this process.
Why an AI Agent?
The main problem in modern e-commerce is "abandoned carts" due to a lack of immediate customer response. Implementing an AI agent, integrated with an inventory management system (PIM) and CRM, allows automating up to 70% of routine queries: stock checks, consultations, and reservations.
The Tech Stack
For my MVP, I chose proven tools that allow for rapid scaling:
Framework: Next.js (React).
Hosting: Vercel (thanks to seamless GitHub integration).
Messaging: Telegram Bot API.
Styling: Tailwind CSS.
Technical Challenges & Solutions
1. Telegram as an Interface
Choosing Telegram as the communication channel was an obvious decision due to its accessible API.
Implementation: I used @BotFather for initialization and a simple integration via Next.js API routes. The main challenge here was correctly handling incoming requests (webhooks) and securely storing CHAT_ID for administration.
2. Design: The Dark Mode Struggle
Tailwind CSS is a great tool, but sometimes it creates style conflicts, especially when switching between light and dark themes.
Solution: Using modifiers like dark:text-white and dark:bg-gray-800 for components (e.g., for Accordion or product cards). It is crucial to enforce contrast so that the text remains readable regardless of the background.
3. Security: JSON Web Tokens (JWT)
During development, there is a temptation to "simplify" security. However, using JWT_SECRET is essential.
Tip: Generate a random string (at least 64 characters) via the terminal (openssl rand -base64 32 or similar commands) and store it exclusively in your .env environment variables. Never hardcode secrets in your repository.
4. SEO & OG (Open Graph)
Your product won't be seen if your link looks "empty" when shared on social media.
OG Checklist:
Size: 1200 x 630 px.
A clear title conveying value (e.g., "AI Agent: Automating Sales 24/7").
Branding (logo) in the corner.
Tech-focused visualization.
Lessons for Developers
Don't overcomplicate the MVP: Launch the core functionality first, then add complex integrations.
UX matters: Even if it's "just an AI chat," a high-quality frontend builds customer trust.
Use automation tools: Platforms like Vercel allow you to focus on code rather than infrastructure.
Conclusion
Building an AI agent is not just about training neural networks; it’s about quality engineering: API setup, security, user interface, and user optimization. My project is currently at Online Zone, and I plan to continue improving its architecture.
Have you encountered similar challenges when building your AI projects? Share your thoughts in the comments below!
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