Enterprise AI agents are evolving from conversational assistants into autonomous digital actors that can interpret goals, invoke tools, access enterprise data, orchestrate workflows, make recommendations, and execute business actions. This shift introduces a new governance challenge because conventional AI controls are often designed for isolated models rather than goal-directed agents that operate continuously across applications, APIs, identities, business processes, and external services. This paper proposes an Enterprise AI Agent Governance Framework that integrates enterprise architecture, responsible AI, identity and access management, business data governance, integration governance, security engineering, model risk management, human oversight, observability, auditability, and lifecycle controls. The framework defines governance layers for agent purpose, authority, identity, tools, data, memory, prompts, models, actions, approvals, runtime monitoring, incident response, and retirement. It also introduces policy-based decision gates, risk-tiering, action boundaries, multi-agent governance, evidence logging, human-in-the-loop controls, and measurable key performance and risk indicators. The proposed approach enables enterprises to scale agentic AI while preserving accountability, security, compliance, transparency, operational resilience, and business trust.

Sanjeeve Kumar Gajadi, "Enterprise AI Agent Governance Framework" International Journal of Scientific Research and Engineering Development, V9(5): Page(356-360) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.