为失控的AI代理乱象提供可量化的治理框架，助力企业降低风险提升效率。
Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations
- 构建五级成熟度模型，覆盖12个治理领域，融合国际标准。
- 实验证明高成熟度组织风险事件减少96.4%，任务完成率提升32.6%。
- 识别五类代理泛滥模式，配套成本模型，适合企业AI治理负责人。
企业快速采用自主型AI（能规划、推理并执行多步流程）引发严重的治理危机。组织面临不可控的代理泛滥：跨业务职能重复、无监管、相互冲突的AI代理大量滋生。行业调查显示，仅21%的企业具备成熟的自治代理治理模型，而40%的代理型AI项目预计到2027年将因治理不善和风险控制不足而失败。尽管该问题日益受重视，学术界仍缺乏正式且经实证验证的治理成熟度模型，无法将治理能力与可衡量的业务成果关联。本文提出“自主型AI治理成熟度模型”（AAGMM），一个基于NIST AI RMF和ISO/IEC 42001标准的五级框架，涵盖12个治理领域。同时，提出一种新的代理泛滥模式分类——功能重复、影子代理、孤立代理、权限蔓延、未监控委托链，并为每类建立可量化的成本模型。通过5个企业场景下750次模拟运行，评估不同成熟度等级在成本控制、风险事件率、运营效率和决策质量方面的表现。结果表明，各成熟度水平间差异显著（p < 0.001，效应量d > 2.0），Level 4-5组织相比Level 1，代理泛滥指数降低94.3%，风险事件减少96.4%，有效任务完成率提高32.6%。AAGMM为从业者提供可操作的治理路径，以最大化企业收益。
原文摘要 · Abstract (English)
The rapid adoption of agentic AI in enterprise business operations--autonomous systems capable of planning, reasoning, and executing multi-step workflows--has created an urgent governance crisis. Organizations face uncontrolled agent sprawl: the proliferation of redundant, ungoverned, and conflicting AI agents across business functions. Industry surveys report that only 21% of enterprises have mature governance models for autonomous agents, while 40% of agentic AI projects are projected to fail by 2027 due to inadequate governance and risk controls. Despite growing acknowledgment of this challenge, academic literature lacks a formal, empirically validated governance maturity model connecting governance capability to measurable business outcomes. This paper introduces the Agentic AI Governance Maturity Model (AAGMM), a five-level framework spanning 12 governance domains, grounded in NIST AI RMF and ISO/IEC 42001 standards. We additionally propose a novel taxonomy of agent sprawl patterns--functional duplication, shadow agents, orphaned agents, permission creep, and unmonitored delegation chains--each linked to quantifiable business cost models. The framework is validated through 750 simulation runs across five enterprise scenarios and five governance maturity levels, measuring business outcomes including cost containment, risk incident rates, operational efficiency, and decision quality. Results demonstrate statistically significant differences (p < 0.001, large effect sizes d > 2.0) between all governance maturity levels, with Level 4-5 organizations achieving 94.3% lower sprawl indices, 96.4% fewer risk incidents, and 32.6% higher effective task completion rates compared to Level 1. The AAGMM provides practitioners with an actionable roadmap for governing autonomous AI agents while maximizing business returns.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。