A study characterizes “agentic flooding” as surges in demand for government services enabled by AI agents. These systems can help people apply for benefits, interpret complex policies and submit public comments, but they can also let large numbers of users generate and file requests cheaply enough to strain unprepared public agencies. The researchers collected 84 potential flooding cases across 11 jurisdictions and posit that the phenomenon is already widespread. They identify large language models’ low-cost text generation as the principal mechanism behind many potential cases. The study’s risk matrix finds that government services are most exposed in the near term when they are both financially attractive and complex, creating incentives to automate applications or other interactions. The researchers map potential government responses and say existing precedents suggest governments can stop most flooding incidents. The fastest measures, including fees and other sources of friction, may reduce equitable access to public services. The study recommends near-term mitigations intended to manage demand without relying on measures that make public services harder to access.