AgentGuardian is a novel security framework that enhances AI agent safety by automatically learning context-aware access control policies from benign execution traces. It enforces these policies at the tool level and validates execution flow integrity, effectively detecting malicious inputs and mitigating hallucination-driven errors.

This vision paper explores how artificial intelligence (AI) agents can support Software Engineering (SE) researchers in formulating research problems, aiming to bridge the gap between academic contributions and industrial needs. It proposes integrating AI agents into the Lean Research Inception (LRI) framework to enhance problem definition and assessment.

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.