A Multi-Evaluator Behavioural Governance Framework for LLM Agent Deployment

RASeV‑X is a behavioural governance framework for Large Language Model (LLM) agents, designed to provide real‑time, auditable, and regulatorily aligned safety decisions at the moment of action execution. The framework introduces the RASeV Engine (Reasoning, Action, Safety, and Evidence Validation), which intercepts agent actions synchronously and evaluates them using nine independent behavioural and safety evaluators. These evaluators cover reasoning depth, evidence grounding, tool‑use safety, temporal consistency, multi‑agent coordination, policy compliance, jailbreak resistance, goal integrity, and prompt‑injection detection. The paper presents the formal architecture of RASeV‑X, the conceptual specification of each evaluator, and a clause‑level mapping to the EU AI Act 2024 and NIST AI RMF 2.0. A synthetic benchmark of 1,200 agent action traces is used to assess evaluator performance, demonstrating that RASeV‑X can deliver sub‑150 ms decision latency in benchmarked deployment configurations. This publication is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The implementation of the RASeV‑X Engine — including evaluator logic, scoring mechanisms, and system architecture — is proprietary and remains the exclusive intellectual property of the author.