TITLE:
Toward Autonomous AI Organizations: The Hierarchical Agentic AI Governance Architecture (HAAGA)
AUTHORS:
Benjamin Quito
KEYWORDS:
Agentic AI, Autonomous AI Organizations, AI Governance, Decision Escalation, Procedural Bias, Organizational Governance, Organizational Memory, Information Systems
JOURNAL NAME:
Intelligent Control and Automation,
Vol.17 No.4,
October
14,
2026
ABSTRACT: As agentic artificial intelligence evolves from task-oriented automation to persistent organizational participation, existing multi-agent frameworks offer limited guidance on enterprise governance, institutional continuity, and accountability. This conceptual paper proposes the Hierarchical Agentic AI Governance Architecture (HAAGA), which organizes an Autonomous AI Organization across Governance, Administration, Operations, and Organizational Memory. An Autonomous AI Organization is distinguished from a persistent multi-agent system by an enduring mission and identity; durable departments, roles, and decision rights; formal escalation and final adjudication; institutional governance; controlled organizational memory; continuity across agent replacement; and auditable accountability. Decisions remain at the lowest level, with formally assigned authority and validated competence, while unresolved disagreements, insufficient authority, or requests for guidance trigger escalation from local to supervisory and executive levels. The Board of AI Governors and Executive AI are organizational roles that may be implemented by autonomous agents, human officials, or hybrid arrangements; the sponsoring legal organization and a designated human officer retain accountability for consequential decisions. HAAGA preserves departmental recommendations, evidence, reasoning, objections, and revisions in a controlled, versioned decision record that informs future cases. Preserved dissent improves reviewability but does not by itself create independent perspectives when departments share models, data, tools, or policy constraints. HAAGA therefore offers a theory-informed organizational specification whose effectiveness, scalability, and bias-mitigation properties require prototype implementation and empirical evaluation.