Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named
For users, patients, and citizens who are affected by autonomous AI agents, the paper names a structural problem that existing AI literacy frameworks fail to address.
The paper identifies 'agentic literacy debt' as a growing societal deficit caused by deploying autonomous AI agents without corresponding literacy infrastructure, showing through healthcare, finance, and equity examples that the gap is already consequential.
Autonomous AI agents now plan, decide, and act on behalf of users across healthcare, financial services, and workplace contexts, often without step-by-step human approval. Existing AI literacy frameworks were built for a world in which humans evaluate AI outputs and decide whether to act; they have no vocabulary for the user who has delegated decision-making authority to an agent whose actions may not be observable, reversible, or controllable. This paper names the resulting problem agentic literacy debt: the accumulating societal deficit that grows when agentic AI systems are deployed at scale without corresponding literacy infrastructure. The debt compounds through three reinforcing channels (normalization of opaque delegation, multi-agent ecosystem complexity, and institutional path dependence), and it is incurred by the organizations that deploy agents but paid by the users, patients, and citizens on whose behalf the agents act. Evidence from healthcare, financial fraud, and global equity contexts suggests the gap is already consequential. The problem is structural, not a temporary lag that curriculum reform will close. It demands a reframing of AI literacy as a governance capability, not an evaluative one.