ai
Characterizing Agentic Flooding of Government Services
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 19 Aug 2026
- Confidence
- High
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Research & Evidence, Technology & Data, Policy & Regulation, Risk & Compliance, Operations & Delivery
Executive summary
What happened, and why should leadership care?
The integration of AI agents, particularly Large Language Models (LLMs), is enhancing public interaction with government services by simplifying applications, policy comprehension, and opinion submission. This improved accessibility, while beneficial, introduces a novel risk termed 'agentic flooding,' where AI-driven surges in demand could overwhelm unprepared government services. Research suggests this phenomenon is already occurring widely across various jurisdictions.
Why this matters
Why is this strategically important?
The proliferation of AI agents in public interaction with governmental bodies presents both opportunities for enhanced service delivery and significant risks of operational overload. Understanding and mitigating 'agentic flooding' is critical for maintaining service continuity, resource allocation efficiency, and public trust in digital government initiatives. Failure to address this could lead to service disruptions and increased operational costs.
Key insights
What should be noted from the evidence?
- AI agents facilitate public interaction with government services, improving accessibility for tasks like applying for benefits and understanding policies.
- This increased accessibility can lead to 'agentic flooding,' defined as surges in demand that strain government services.
- Agentic flooding is likely occurring widely today, primarily driven by the inexpensive generation of text by LLMs.
- A dataset of 84 potential flooding cases across 11 jurisdictions indicates the current prevalence of this issue.
- A risk matrix is being developed to evaluate the exposure of specific government services to agentic flooding.
Evidence and confidence
How far can this assessment be trusted?
High confidence. Named institution, original document retained and analysis corroborated.
Analysis is prepared editorially by Aziz Shuaib Ausi. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication