Production-Ready E-commerce Price Tracker API: A Xano AI Challenge Submission

Suriya Kumar — Sun, 14 Dec 2025 08:19:53 +0000

🚀 The Xano AI-Powered Backend Challenge: Human Refinement

I recently took on the Xano AI Challenge to build a production-ready e-commerce price tracker backend. While the Xano AI assistant provides an excellent starting foundation, the core logic for real-world functionality—like live price scraping and production-level security—required Human Refinement.

This post details how I refined the AI-generated backend to create a robust and functional Price Tracking API.

1. The AI Foundation

The Xano AI successfully established the foundational elements:

Database Schema: It created the necessary tables, including a product table and a product_price_history table for tracking price c… [1:29 pm, 14/12/2025] Baby: --- tags: [xano, xanoai, backend, webscraping, ] ---

🚀 The Xano AI-Powered Backend Challenge: Human Refinement

This post details how I refined the AI-generated backend to create a robust and functional Price Tracking API.

1. The AI Foundation

The Xano AI successfully established the foundational elements:

Database Schema: It created the necessary tables, including a product table and a product_price_history table for tracking price changes over time.
Core Endpoints: It generated basic CRUD (Create, Read, Update, Delete) endpoints for managing product data.

2. Human Refinement #1: The Live Web Scraping Logic

The major gap left by the AI was the ability to fetch live prices from external e-commerce websites. I solved this by creating a dedicated, public endpoint.

New Endpoint: GET /get_prouct_details_live
Web Scraping Implementation: Within the Function Stack, I added an External API Request function. This function takes an e-commerce URL as input.
- Logic: It uses the input url to fetch the raw HTML content of the product page.
- Data Storage: A subsequent Database Request function saves the extracted price and a current timestamp into the product_price_history table.

3. Human Refinement #2: Production Readiness & Security

To ensure the API is "production-ready," I implemented essential security and validation features:

Input Validation: The endpoint requires a mandatory url input of type text. This ensures the API cannot be called without a target URL.
Rate Limiting: To prevent misuse and secure the infrastructure, I implemented Rate Limiting on the get_prouct_details_live endpoint. This prevents any single user from making excessive scraping requests in a short period.

🔗 Try the API (Swagger Documentation)

You can view the full documentation and test the get_prouct_details_live endpoint using the links below.

Resource	Link
API Base URL	https://x8ki-letl-twmt.n7.xano.io/api:Kqp69FYE
Swagger Docs (OpenAPI)	(https://x8ki-letl-twmt.n7.xano.io/api:Kqp69FYE#/get_product_details_live/get_prouct_details_live%2Fget_product_details_live%7CGET URL-)

This challenge was a fantastic opportunity to merge the power of AI-generated architecture with the necessary detailed logic of Human Refinement to build a truly production-ready backend.

AI Agent on Kaggle

Suriya Kumar — Thu, 04 Dec 2025 07:48:51 +0000

✍️ Final Article Draft: English Translation
Title: Building an AI Agent on Kaggle: A Journey Through the Confounding Challenges of a Generative Agent

Introduction: Stepping into the World of AI Agents When I first embarked on the AI Agents Intensive program, I realized that Agents were not just theoretical concepts but powerful frameworks capable of solving real-world problems. For our Capstone Project, we chose to build a Smart Content Generator Agent. Its primary objective was to reduce the time spent on creating compelling advertising copy for social media. This article will detail the challenges I faced in designing our Agent—such as structuring the prompt and dealing with performance issues—and the profound lessons I learned in guiding an Agent to produce accurate and high-quality content.
Agent Architecture and Tools Our Agent's architecture is straightforward yet highly focused on the goal.
- Agent’s Goal: To analyze the provided product features and desired tone, and instantly generate an engaging social media post.
- The Engine (LLM): We utilized Google's Gemini-2.5-Flash as the core LLM. Its high speed and efficiency made it ideal for a short-duration hackathon project.
- Tools: We made the conscious decision not to integrate any external tools (like Web Search or a Calculator). Since all necessary information for the ad copy was provided via the Prompt, avoiding tools significantly improved the Agent's Performance (Latency).
Challenges and Solutions Making an AI Agent perform exactly as expected is the most critical part of development. Here are the key challenges I encountered and the solutions I implemented: | The Challenge | The Solution | |---|---| | Output Consistency: The Agent often failed to maintain the desired 'Humorous' or 'Formal' tone, instead defaulting to a generic voice. | System Prompt Refinement: I provided a much clearer System Prompt at the beginning, defining the Agent as a "High-level Advertising Consultant." | | Input Control: The Agent sometimes omitted critical input, such as the product name or the key features, in the final ad copy. | Forced Formatting: I strictly mandated in the prompt that "the output must contain the product name and all three specified features." | | Performance (Latency): Initial tests using larger models resulted in wait times exceeding 15 seconds per output. | Model Selection: Choosing Gemini-2.5-Flash, which strikes the perfect balance between speed and quality, reduced the time-to-output to under 5 seconds. |
Future Vision Building on the success of this prototype, I plan to enhance the Agent further in the future:
- Multimodality: Currently, the Agent only generates text. In the future, I intend to integrate its ability to generate corresponding images using models like Gemini 2.5 Pro, enabling it to create complete text-and-image social media posts simultaneously.
- Memory: I plan to add a Session Memory feature. This will allow the Agent to recall Brand Guidelines after learning them once, ensuring high Consistency in tone and style across multiple outputs.
- Self-Improvement: Finally, I plan to develop a Feedback Loop to automatically adjust the Agent’s prompt based on user feedback, enabling the Agent to continuously improve its own output quality.
Conclusion and Acknowledgements The AI Agents Intensive Capstone Project was not just a competition; it was an invaluable lesson in understanding and deploying Generative AI Agents. Despite the challenges, we successfully harnessed the power of Gemini to create an efficient and accurate AI Copywriter. This experience has given me great confidence to build larger, more complex multi-tool Agents in the future. Finally, I extend my heartfelt gratitude to the Kaggle Team and the Google Mentors for their guidance during this 5-day Intensive. This experience marks a significant turning point in my AI journey.

DEV Community: Suriya Kumar

Production-Ready E-commerce Price Tracker API: A Xano AI Challenge Submission

🚀 The Xano AI-Powered Backend Challenge: Human Refinement

1. The AI Foundation

🚀 The Xano AI-Powered Backend Challenge: Human Refinement

1. The AI Foundation

2. Human Refinement #1: The Live Web Scraping Logic

3. Human Refinement #2: Production Readiness & Security

🔗 Try the API (Swagger Documentation)

AI Agent on Kaggle