When AI Hallucination Becomes Action

Abstract

A hallucination in a language model may produce a false statement, a nonexistent citation, or a plausible explanation unsupported by evidence. In an AI agent, the same class of failure can pass through memory, planning, tools, credentials, and other agents until it changes external state. The problem is therefore no longer limited to answer quality. It becomes a systems problem involving authority, execution, propagation, evidence, and accountability. This article develops a precise and practical account of that transition. It distinguishes hallucination from adjacent failure modes, identifies where unsupported claims can enter agentic workflows, explains why a second model is not automatically an independent verifier, and proposes a layered control model. Through the public SGAEIA research framing, the article argues that no assertion made by an agent about facts, authorization, execution, or compliance should grant authority or serve as its own proof. When an answer can cause an action, factuality must become a governed system property.

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