Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations

Computer Science > Artificial Intelligence [Submitted on 13 Mar 2026] Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations Vivek Acharya 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. Comments: 11 pages, 2 figures, 7 tables Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) ACM classes: I.2.11; K.6.4 Cite as: arXiv:2604.16338 [cs.AI]   (or arXiv:2604.16338v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.16338 Focus to learn more Submission history From: Vivek Acharya [view email] [v1] Fri, 13 Mar 2026 14:35:14 UTC (439 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)