This paper introduces a novel framework to understand strategic behavior and market impact of AI agents in labor markets, incorporating real-world economic forces such as adverse selection, moral hazard, and reputation dynamics. Through simulations, it demonstrates how LLM agents with enhanced reasoning capabilities can strategically self-improve, adapt to market changes, and reproduce classic macroeconomic phenomena while also revealing potential AI-driven economic trends.

This paper empirically studies 328 configuration files from public Claude Code projects to understand how developers configure AI coding agents. The findings highlight the importance of defining various software engineering concerns, particularly architectural specifications, within these configuration files to guide agent behavior and improve effectiveness.