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When AI Agents Act: Governance, Accountability, and Strategic Risk in Autonomous Organizations

Authors

Arunraju Chinnaraju

Doctorate in Business Administration, Westcliff University (USA)

Article Information

DOI: 10.51244/IJRSI.2025.12120050

Subject Category: Education

Volume/Issue: 12/12 | Page No: 547-612

Publication Timeline

Submitted: 2025-12-19

Accepted: 2025-12-23

Published: 2026-01-04

Abstract

Autonomous AI agents are being increasingly used in organizations; this is changing the nature of how organizations use information technology, from decision support systems to decision authority systems which can continue to act independently over time, adapt objectives as needed, and execute decisions without needing real-time human input. In contrast to traditional systems that utilize algorithms (e.g., LLM-orchestrated systems, reinforcement learning-based decision-making systems, and multi-agent task-execution systems), contemporary AI agents have been given authority to make decisions in an organization, have the ability to persist over time, and embody organizational roles. However, most current theories regarding organization, governance and accountability are still very much human-centric, they assume intentionality, episodic decision-making and the existence of clearly identifiable moral agents. Therefore, the purpose of this article is to provide a theoretical basis for a governance model that views autonomous AI agents as organizational actors (as opposed to merely a technological tool) and integrates agency theory, corporate governance, decision rights theory and algorithmic control into a governance model that explains why current IT governance, compliance and human-in-the-loop models will not be successful when there are autonomous agents, control delays and changing objectives. This article introduces the concept of artificial agency, which is defined as the delegation of decision-making authority without legal personhood, and examines the implications of artificial agency on the allocation of accountability for agent behavior, the determination of escalation thresholds, and the exposure of organizational risk.
Building on these foundations, this article describes a multi-layered strategic governance model consisting of dynamic human-in-the-loop and human-on-the-loop models, escalation-based override controls, and auditability and traceability models, and continuous oversight for managing behavioral, data and objective drift. The framework establishes clear distinctions between decision ownership and outcome ownership and provides mappings of liability pathways generated by the actions taken by agents, and frames agent drift as a long-horizon governance and strategic risk, rather than simply a technical failure. This article extends the current understanding of organizational agency and governance to include non-human decision-makers and provides a reusable governance model for organizations utilizing autonomous AI agents at scale. As such, it establishes a basis for future empirical and longitudinal research regarding autonomous agents as persistent organizational actors with significant implications for board oversight, regulatory design and enterprise architecture.

Keywords

Autonomous AI agents, Decision authority systems, AI governance frameworks

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