Beyond Hallucinations: Governing Agentic AI Through Identity, Delegation, and Execution Control

Artificial intelligence governance has traditionally focused on model-centric risks such as hallucination, bias, and output reliability. However, the emergence of agentic AI systems—capable of taking actions across tools, APIs, and enterprise workflows—shifts the risk boundary from generation to execution.

This paper argues that existing governance frameworks are insufficient for managing agentic systems because they fail to operationalize four critical dimensions: identity, delegation, execution surface, and containment. While current models may produce accurate outputs, the real risk lies in what an AI agent is authorized to do, how it inherits permissions, and how its actions propagate across interconnected systems.

We introduce a structured framework that evaluates agentic AI systems based on: (1) identity clarity—who the agent represents and under what authority; (2) delegation scope—the level of decision-making autonomy granted; (3) execution surface—the breadth of tools and systems the agent can access; and (4) containment mechanisms—the ability to halt, audit, and reverse actions in real time.

Our analysis highlights a critical governance gap: most enterprise deployments prioritize capability over control. As agents interact with multiple tools, risks compound through permission drift, opaque decision chains, and limited rollback capabilities. Traditional “human-in-the-loop” approaches often function as post-action oversight rather than real-time control.

To address this, we propose measurable controls and a maturity model for agent deployment, enabling organizations to transition from passive AI usage to actively governed, accountable systems.

As AI continues to evolve from assistants to operators, governance must evolve accordingly—from managing outputs to controlling actions.