Authors: Rohit Agnihotri
Abstract: The deployment of agentic Artificial Intelligence (AI) systems (autonomous, goal-directed agents capable of multi-step action, tool use, and dynamic resource acquisition) across enterprise environments has outpaced the development of the governance infrastructure required to account for, audit, and regulate their actions. While conventional Identity and Access Management (IAM) frameworks were designed for human actors executing predictable, bounded operations, agentic systems operate with a degree of autonomy, adaptability, and action scope that renders existing accountability models structurally inadequate. This article presents a comprehensive analysis of identity, access, and auditability frameworks for agentic actions, with a focus on three interdependent governance dimensions: traceability (the capacity to reconstruct the complete causal chain of an agentic action), explainability (the generation of human-intelligible justifications for agent decisions and actions), and compliance (the systematic alignment of agentic behavior with regulatory requirements and organizational policy). We introduce the Agentic Identity and Auditability Framework (AIAF), a seven-pillar governance architecture that addresses the full lifecycle of agentic identity, action attribution, and accountability. Empirical analysis demonstrates that organizations deploying formal agentic auditability frameworks achieve 72–97% traceability coverage across seven audit layers compared to 5–71% without formal frameworks, and automate 58–83% of compliance evidence generation across applicable regulatory frameworks. We further develop a five-level Agentic Autonomy Classification Model aligned with SAE automation level conventions, providing a principled basis for calibrating governance intensity to agent autonomy. The article concludes with critical research directions in cryptographic action attribution, privacy-preserving audit architectures, and the governance of self-modifying agentic systems.
