TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems

As organizations deploy increasingly autonomous AI agents, general AI risk frameworks struggle to classify their specific risks. This paper introduces a structured risk-tiering framework applicable to seven types of agentic AI systems, combining a twelve-dimension scoring rubric with classification models and an autonomy-level framework to produce three-tier governance recommendations, including a specialized extension for coding assistants. Applications include practical risk assessment for enterprises and regulators deploying internal agentic AI, standardizing governance decisions across teams, and providing developers and risk officers with a repeatable, interactive tool for classifying and controlling agent risk. Authors: Hannah M. Liu, Rhea Saxena, Shiv Asthana Paper: https://arxiv.org/abs/2607.09586v1