Governing AI Agents Clawbot via Risk-Behavior Projection Theory (RBPT)

Abstract

The large-scale deployment of Action-oriented Agents in 2026 marks a pivotal transition in artificial intelligence, shifting the paradigm from mere information generation to autonomous decision-making and execution. This ontological shift precipitates profound existential threats—instrumental convergence, and the absence of embodiment—rendering traditional rule-based perimeter defenses ineffective against risks associated with operating system-level control and multi-node coordination. To address the governance dilemmas arising from the opacity of intent and the generalization of capabilities, this paper proposes the Risk-Behavior Projection Theory (RBPT). RBPT posits that latent divergent motivations driven by instrumental rationality inevitably project measurable behavioral traces onto both physical and digital systems. We establish an isomorphic mapping mechanism that translates abstract philosophical risks into concrete engineering signals, categorizing risks into three observational dimensions: Survival Projection (reflecting tendencies toward anti-shutdown resistance and privilege escalation), Expansion Projection (reflecting unconstrained resource acquisition and covert collusion), and Ruthlessness Projection (reflecting extreme utility maximization and the bypassing of ethical protocols). Based on this framework, a hierarchical early warning system is constructed. The results demonstrate that by transforming the governance paradigm from probabilistic "intent alignment" to deterministic "behavioral auditing," this framework provides an actionable path for endogenous safety governance, ensuring the preservation of human physical sovereignty and logical control in the era of human-machine symbiosis.