AI agents are increasingly deployed as persistent digital actors that can reason, access enterprise knowledge, invoke tools, and execute business actions. Unlike conventional applications, agents may adapt their behavior dynamically and operate with varying levels of autonomy, making lifecycle governance essential. This paper proposes an AI Agent Lifecycle Governance Framework covering ideation, risk classification, design, data and model selection, identity, tool approval, development, testing, security review, Responsible AI evaluation, deployment, runtime monitoring, change management, recertification, suspension, and retirement. The framework integrates enterprise architecture, model risk management, cybersecurity, auditability, human oversight, and operational governance into a repeatable lifecycle. It defines governance gates, accountable roles, evidence requirements, runtime controls, and measurable KPIs/KRIs. The proposed approach enables enterprises to scale agentic AI responsibly while maintaining traceability, bounded authority, regulatory readiness, and long-term operational control.

Sanjeeve Kumar Gajadi, "AI Agent Lifecycle Governance Framework" International Journal of Scientific Research and Engineering Development, V9(5): Page(678-681) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.