Research Summary: Characterizing Agentic Flooding of Government Services

The integration of AI agents, particularly large language models (LLMs), is enhancing public accessibility to government services by simplifying interactions and information access. However, this increased accessibility carries a significant risk of 'agentic flooding,' where surges in demand generated by AI agents could overwhelm and strain unprepared government services. Initial research identifies 84 potential cases across 11 jurisdictions, suggesting this is an occurring phenomenon, predominantly driven by the low-cost generation of text by LLMs. Further analysis is underway to identify the services most vulnerable to this type of flooding.

Why it matters

This development highlights a critical intersection of technological advancement and operational resilience in public services. Understanding and mitigating 'agentic flooding' is essential for maintaining efficient governance and preventing service disruptions as AI adoption expands, ensuring that technological progress genuinely improves, rather than compromises, public service delivery.

Key insights

• AI agents are actively improving public interaction with government services.
• The enhanced accessibility provided by AI agents can lead to surges in demand, termed 'agentic flooding'.
• Agentic flooding poses a risk of straining or overwhelming government services if not adequately managed.
• A dataset of 84 potential flooding cases across 11 jurisdictions indicates that this phenomenon is likely widespread.
• Large language models (LLMs) are identified as the primary driver for agentic flooding due to their cost-effective text generation capabilities.
• Research is actively evaluating which specific government services are most exposed to the risk of agentic flooding.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.16603