As AI agents become embedded in public‑facing and operational systems, organizations face a critical gap: operators are expected to detect behavioral anomalies—drift, manipulation, context contamination, and tool misuse—without having been taught what “healthy” agent behavior looks like. Traditional security models assume humans can intuitively recognize anomalies. Agentic systems break that assumption. This framework introduces *Behavioral Literacy for Agent Operators*, an EIOC‑informed discipline for reading agent behavior as a security‑relevant signal. It defines the conceptual foundations of baseline behavioral signatures, drift patterns, manipulation indicators, context‑integrity phenomena, and escalation thresholds. This public record documents the theoretical architecture of the discipline. Operational training materials—including worksheets, scenario banks, assessment rubrics, and facilitator resources—are steward‑gated and provided separately by Soft Armor Labs to preserve security and canonical integrity.
Narnaiezzsshaa Truong (Sat,) studied this question.