When “Close Enough” Becomes a Liability: The Real Test for Agentic AI in Risk Management – The AI Journal

The article examines how agentic AI systems face critical limitations when precision is required in risk management applications, where approximate solutions can create significant liability issues. It argues that “close enough” performance standards, which may be acceptable in other domains, become problematic when autonomous AI agents make consequential decisions affecting financial, operational, or regulatory outcomes. The piece highlights the gap between current agentic AI capabilities and the accuracy demands of high-stakes risk management scenarios.

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