Exploring GPT-Based Multi-agent Collaboration for Automated Market Analysis

The rapid advancement of large language models (LLMs) has opened new possibilities for business analytics and market intelligence. While traditional single-model systems such as ChatGPT can analyze text and summarize insights, they lack collaborative specialization and workflow coordination. This study explores a no-code experimental framework using a multi-agent system built upon GPT to perform automated market analysis. The experiment compares a baseline single-LLM configuration with a three-agent structure composed of a Data Agent, an Analysis Agent, and an Auditor Agent. A dataset of 200 publicly available product reviews was used to evaluate performance across quantitative metrics (accuracy, precision, recall, F1-score) and qualitative metrics (report structure, insight quality, information coverage). Results show that the multi-agent workflow produced clearer, more structured market reports with marginally higher accuracy and significantly improved interpretability (accuracy = 0.86 vs. 0.81; macro-F1 = 0.86 vs. 0.81). This exploratory research highlights the potential of GPT-based agents for business decision support and demonstrates a reproducible no-code approach accessible to non-technical practitioners.