Autonomous artificial intelligence (AI) agents are no longer advisory. They execute transactions, orchestrate multi-agent pipelines and modify enterprise data with minimal human intervention. Yet the governance frameworks organisations rely on were built for a different artefact: one that recommends rather than acts. This paper argues that delegation, not architecture, is the primary variable that governance frameworks for agentic AI must address and that existing frameworks, including NIST AI RMF and the EU AI Act, do not adequately operationalise governance at the delegation level. A multi-corpus bibliometric analysis of 795 peer-reviewed publications (2020-2026) provides evidence of three structurally isolated scholarly communities (AI ethics governance, MLOps operationalisation and agentic enterprise integration) developing in parallel without convergence, leaving the high-autonomy, high-accountability quadrant theoretically underserved. Grounded in three complementary theoretical pillars (IT Governance theory, Socio-Technical Systems theory and IS Artefact Delegation theory), we derive the Delegated Agentic Governance Model (DAGM): a conditional governance matrix that assigns governance requirements to each level of autonomy delegated to AI agents across three tiers (Advisory, Operational, Autonomous). We introduce Generative AI governance debt as a prerequisite construct, articulate seven design principles and derive three falsifiable propositions linking delegation-governance alignment to enterprise failure rates. The DAGM provides AI managers with an immediately actionable governance readiness instrument and establishes the theoretical foundation for a research agenda on delegation-calibrated AI governance across finance, healthcare and manufacturing.
Gluszak et al. (Sat,) studied this question.
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