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.
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