Title: Monitoring the Evolution of Agent Behaviors

Abstract: In contemporary AI systems, text-based artifacts—such as memory logs, behavioral configurations, and skill files—are pivotal in dictating agent conduct. As these documents are modified by either human operators or the agents themselves, they undergo evolution that subsequently guides the agent’s future actions. This paper introduces a novel framework and methodology for quantifying agent characteristics, or "traits," by conceptualizing them as specific vectors within the embedding space of a text embedding model. To identify these trait vectors, we train a linear classifier on annotated pairs of "before" and "after" skill file changes. Once trained, the system evaluates new skill modifications by projecting their embedding differences onto the learned trait vector. We validated this approach using a dataset of 68 labeled skill diff pairs focused on the tendency to solicit sensitive information. Under leave-one-out cross-validation, the method demonstrated a sign classification accuracy of 91.2% and a Spearman rank correlation coefficient of $\rho = 0.82$. Furthermore, we integrated this trait assessment into a comprehensive agent-to-agent protocol, allowing one agent to review another’s skill file modifications via a secure, trusted intermediary.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC