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

2025-08-20

AI Summary

As AI agents increasingly emulate human network behavior, the human-agent boundary blurs, exacerbating challenges in trust attribution, accountability, and regulatory oversight. Method: This study proposes a six-stage “Network Behavior Lifecycle” model to systematically differentiate fundamental disparities between humans and agents—particularly in decision-making mechanisms and execution efficiency. It introduces the A4A (Agent-as-a-Function) paradigm and the HABD five-dimensional quantitative model (Human-Agent Behavioral Discrepancy), and designs a dynamic cognitive governance architecture with a meta-governance protocol stack. Validation employs behavioral modeling, red-blue adversarial experiments, and intent-behavior consistency assessment within cybersecurity contexts. Contribution/Results: The framework delivers a novel theoretical foundation and implementable technical pathway for AI agent behavior governance, enabling verifiable, accountable, and trustworthy human-agent collaboration systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Modeling agent-human behavioral differences in networked environments
• Addressing trust and accountability challenges in AI governance
• Proposing frameworks for secure human-agent collaboration

Innovation

• Network Behavior Lifecycle model with 6 stages
• Agent for Agent paradigm examining behavioral distinctions
• Human-Agent Behavioral Disparity model across 5 dimensions