This Video explains an architectural strategy for implementing and securing autonomous AI agents within large organizations, referred to as the **Agentic Enterprise**. This framework introduces the **Agent Development Lifecycle (ADLC)**, an extension of standard DevSecOps principles required to manage the adaptive and non-deterministic behavior inherent to Large Language Model-driven systems. A crucial technological component is the **Model Context Protocol (MCP)**, an open standard that enables agents to securely interact with enterprise tools and data, often governed through a centralized **MCP Gateway** to enforce policy and identity controls. The ADLC workflow mandates continuous control over agent systems through rigorous **governance**, comprehensive **observability** of reasoning traces, and specialized **security** testing to mitigate unique threats like prompt injection and data leakage. Ultimately, the document outlines necessary requirements for robust deployment, auditing, and risk management, especially for regulated industries such as **finance and healthcare**, ensuring agents remain compliant and reliable.