Data Generation on Multiple Platforms to Build User Behavior Datasets for AI Agent Training

BUSINESS CHALLENGES

Our Client

DIGI-TEXX’s client is a technology institute in the United States, focusing on Artificial Intelligence (AI). 

In recent years, the client has recognized the immense value of understanding user behavior across different types of online platforms – from social networks to enterprise productivity tools. 

With the rapid digitalization of learning, working, and communication environments, online interactions now reflect how humans make decisions, consume information, and engage with technology.

To leverage this potential, the client has expanded its focus toward development in AI, aiming to build intelligent systems capable of understanding and replicating human digital behaviors.

A core part of this strategy involves creating human-like AI agents to simulate and generate realistic digital behavior.

The Concept of AI Agents

While Large Language Models (LLMs) (like GPT or Claude) are incredibly good at synthesizing information, generating text, and summarizing data, they are essentially tools that wait for a prompt.

AI Agents, however, are different. They are built on top of LLMs but are designed for autonomy. They function like digital humans:

• They think: Define a goal and map out steps to achieve it.
• They reason: Analyze the situation and make decisions.
• They execute: Execute the necessary steps without constant human input.

Crucially, AI agents learn and improve over time, becoming more reliable and autonomous with every task they complete.

Project Challenges

The client aimed to create and build a robust dataset of realistic user interactions across multiple digital platforms. The generated data would serve as training input for AI models designed to understand and predict how users navigate, click, and engage with digital content in real-world scenarios. However, several challenges emerged during the project:

• Cross-platform complexity: Each platform featured unique interfaces and interaction flows, requiring operators to adapt quickly while maintaining consistent and natural user behavior.
• Task clarity and execution consistency: Every interaction had to follow prewritten scripts precisely. Unclear task descriptions or minor deviations in execution could result in inconsistent recordings, leading the AI to misinterpret user intent or learn incorrect behavior patterns.
• Massive data volume: Endless hours of output data needed to be created and processed daily, demanding efficient coordination, task automation, and standardized output formats.
• Strict accuracy requirements: Even small variations in cursor movement, timing, or sequence could reduce the reliability of AI learning, requiring continuous monitoring and feedback loops.

Project Scope

The project included unique technical and operational challenges:

• Generate scenarios to simulate realistic user behaviors across both social and enterprise platforms.
• Utilize the client’s internal system to execute and record all predefined tasks under standardized procedures.
• Organize and annotate output data into structured datasets ready for AI model training and evaluation.
• Maintain data quality control to ensure every output meets the required accuracy, completeness, and format consistency.