Agentic AI Explained for Business and Technology Leaders

Description

95% of AI proof of Concepts fail! Most AI does not fail because the model is weak. It fails because the system was never designed for agency. In this video, I explain what agentic AI really is, why so many AI proof of concepts break down in production, and what business and technology leaders need to understand before giving AI real decision-making power. We cover the difference between context and cognition, why context collapse drives hallucination, and why the real unit of design in agentic systems is authority, not automation. I also introduce the three pillars for building agentic systems that scale with trust: 1. Agentic design. 2. Agentic governance. 3. Agentic architecture. If you want to move beyond demos and understand how to build AI systems that hold up in the real world, start here. Subscribe to follow the series. Video content 0:00 Introduction 0:19 Why AI breaks in production 0:42 Why most AI proof of concepts fail 0:55 Production changes everything 1:19 Context vs cognition 1:54 Why hallucinations really happen 2:08 The fix: governable systems 2:25 Components do not decide. Systems do. 2:42 Why agentic architecture matters 2:44 An agent is defined by what it is allowed to decide 2:51 From instructions to boundaries 3:40 The 3 pillars of agentic AI 3:43 Agentic design 4:05 Agentic governance 4:35 Agentic architecture 5:01 Why prompts are not enough 5:18 Authority, not automation 5:29 Next video: agentic design 5:47 Outro #AgenticAI #EnterpriseAI #AIArchitecture #aigovernance