Public services are increasingly strained by LLM-written appeals for benefits

A new arXiv preprint by Chris Schmitz and colleagues warns that AI agents using large language models are increasingly generating mass appeals to government services, a phenomenon they call 'agentic flooding,' which could strain unprepared public agencies. The study, based on 84 potential cases across 11 jurisdictions, finds near-term risk is highest for financially attractive but complex services, and recommends mitigation strategies that avoid trade-offs with equitable access.

Computer Science Computers and Society Submitted on 17 Aug 2026 v1 https://arxiv.org/abs/2608.16603v1 , last revised 19 Aug 2026 this version, v2 Title:Characterizing Agentic Flooding of Government Services View PDF /pdf/2608.16603 HTML experimental https://arxiv.org/html/2608.16603v2 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. Submission history From: Chris Schmitz view email /show-email/64e03926/2608.16603 Mon, 17 Aug 2026 13:59:28 UTC 248 KB v1 /abs/2608.16603v1 v2 Wed, 19 Aug 2026 16:17:45 UTC 248 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .