Efficient and reliable extraction of data from heterogeneous digital sources is essential across various industries—including automotive, insurance, healthcare, finance, and more—where timely, accurate information underpins decision-making. Traditional automation approaches often struggle with the diversity and variability of web content, structured APIs, and semi-structured documents like PDFs, limiting operational agility and increasing manual effort. This study presents a modular agent system that leverages Large Language Models (LLMs) and a workflow orchestration framework to address these challenges. It demonstrates how to effectively integrate these advanced AI technologies for automating complex, multi-source data workflows—encompassing natural language query interpretation, adaptive tool selection, and real-time validation. Using vehicle evaluation as a case study, our empirical tests demonstrated a 37.5% reduction in data retrieval time, a 30% decrease in errors, and improved user satisfaction. Although tested specifically in an automotive context, the system’s modular and extensible design supports deployment in high-complexity environments, highlighting its potential for adaptation across various sectors—such as healthcare, manufacturing, finance, and government—where sophisticated and scalable data automation is essential.