摘要：AI Agent 正使公众更容易与政府互动，例如协助其申请福利、理解复杂政策以及表达意见。尽管提升服务可及性具有积极意义，但由此产生的需求激增可能会给缺乏准备的政府服务带来压力。我们将这类激增称为政府服务的智能体洪泛（agentic flooding of government services，下称“洪泛”），并提出三项贡献。第一，基于我们收集的覆盖 11 个司法辖区的 84 个潜在洪泛案例数据集，我们认为洪泛很可能已在当今广泛发生，且主要通过大语言模型（LLM）以低成本生成文本而出现。第二，我们评估了哪些服务最容易暴露于洪泛风险。我们构建了风险矩阵来分析一项服务的暴露程度，并认为短期风险最高的是那些具有经济吸引力、但流程复杂的服务。最后，我们梳理了政府可能针对洪泛采取的应对措施。既有先例表明，这些措施可能足以阻止大多数洪泛案例；但部署速度最快的措施——例如收费等增加摩擦的手段——往往会以公共服务的公平可及性为代价。因此，本文最后提出近期行动建议，以帮助政府在不引发这一权衡的情况下缓解洪泛。

Abstract:AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although improving service accessibility is beneficial, any resulting surges in demand could strain unprepared government services. We term such surges agentic flooding of government services ("flooding") and provide three contributions. First, based on a collected dataset of 84 potential cases of flooding across 11 jurisdictions, we posit that flooding is likely occurring widely today, mostly through large language models (LLMs) generating text cheaply. Second, we evaluate what services are most exposed to flooding. We develop a risk matrix to analyze a service's exposure, and suggest that near-term risk is highest for financially attractive, but complex services. Finally, we map possible government responses to flooding. Precedent suggests these responses will likely be sufficient to stop most cases of flooding, but the fastest to deploy - friction-inducing measures like fees - often trade off equitable access to public services. Accordingly, we close by recommending near-term actions that may allow governments to mitigate flooding without invoking this trade-off.