The text discusses the spectrum of AI systems, highlighting that deterministic and agentic approaches exist on a continuum rather than being mutually exclusive. Deterministic AI follows predictable processes, while agentic AI has greater autonomy to adapt and plan in response to changing contexts. Real-world implementations often combine both approaches to balance reliability with flexibility. The development of effective AI systems begins with understanding desired outcomes and considering factors such as user experience, accessibility, and integration within existing workflows. A key principle for implementing AI is to start with simple, deterministic foundations and add complexity only where it improves outcomes, using modular design and specialized components with single responsibilities. Understanding the conditions under which agents work best is also crucial, including well-defined tasks, tools, and meaningful error messages. Ultimately, success in AI implementation comes from methodically matching technological approaches to actual problems rather than following industry hype.