AI Agent Team in Claude Code for Audience Quote Collection: Reconnaissance, Gathering, and Verification

The article explores the architecture of an AI agent system implemented using Claude Code. The primary task of this team is the automated search, collection, and verification of verbatim quotes from a target audience across open sources. The author describes the process of building an autonomous pipeline that not only finds information but also independently validates each link, filtering out errors and outdated data. The material provides technical implementation details, including task execution limits, and the results of testing the system with 13 agents. Special attention is given to analyzing common errors encountered during development and the methods used to resolve them. This case study demonstrates the practical application of LLMs for automating routine data collection and analysis tasks, highlighting the importance of quality control when working with autonomous agents.