agent-to-agent evaluation protocol

Tracking Behavioral Trajectories of Adapting Agents via Trait Vectors in Embedding Space

This paper introduces a methodology for measuring behavioral traits of AI agents by defining traits as directions in the embedding space of a text embedding model, trained on labeled diffs of agent skill/memory/configuration files. A linear model achieves 91.2% sign classification accuracy and Spearman ρ=0.82 on detecting propensity to seek sensitive data across 68 labeled skill diff pairs. The framework extends to an agent-to-agent evaluation protocol where one agent can assess another's skill file updates through a trusted intermediary, enabling ongoing behavioral monitoring of self-modifying agents.