The Agent Behavior: Model, Governance and Challenges in the AI Digital Age

TL;DR

This paper introduces models and paradigms to analyze, govern, and address challenges of agent behaviors in AI networks, emphasizing trust, responsibility, and ethical issues in human-agent interactions.

Contribution

It proposes the Network Behavior Lifecycle, A4A paradigm, and HABD model to systematically analyze and address behavioral disparities and governance challenges in AI agents.

Findings

The models effectively analyze behavioral differences between humans and agents.

Real-world cases validate the effectiveness of the proposed models.

Discussion of future directions for secure human-agent collaboration.

Abstract

Advancements in AI have led to agents in networked environments increasingly mirroring human behavior, thereby blurring the boundary between artificial and human actors in specific contexts. This shift brings about significant challenges in trust, responsibility, ethics, security and etc. The difficulty in supervising of agent behaviors may lead to issues such as data contamination and unclear accountability. To address these challenges, this paper proposes the "Network Behavior Lifecycle" model, which divides network behavior into 6 stages and systematically analyzes the behavioral differences between humans and agents at each stage. Based on these insights, the paper further introduces the "Agent for Agent (A4A)" paradigm and the "Human-Agent Behavioral Disparity (HABD)" model, which examine the fundamental distinctions between human and agent behaviors across 5 dimensions: decision mechanism, execution efficiency, intention-behavior consistency, behavioral inertia, and irrational patterns. The effectiveness of the model is verified through real-world cases such as red team penetration and blue team defense. Finally, the paper discusses future research directions in dynamic cognitive governance architecture, behavioral disparity quantification, and meta-governance protocol stacks, aiming to provide a theoretical foundation and technical roadmap for secure and trustworthy human-agent collaboration.