Agentic and non-agentic AI systems represent fundamentally different approaches to artificial intelligence with distinct architectural, operational, and performance characteristics, which can significantly impact how they are deployed and maintained in production environments. Agentic AI systems, characterized by their ability to make independent decisions and adapt dynamically to achieve goals, are contrasted with non-agentic systems that follow predetermined workflows and produce predictable outputs. The choice between these approaches hinges on the complexity and adaptability required by the task, as agentic systems excel in environments where the path to the goal cannot be predefined and require reasoning and tool selection, whereas non-agentic systems are suited for well-defined tasks with consistent, reproducible outcomes. The operational challenges for agentic systems include managing non-deterministic behaviors, complex reasoning chains, and ensuring observability, which can be addressed by specialized tools like Galileo that provide infrastructure for decision tree tracking and runtime protection. Understanding the differences between these AI systems helps organizations choose the right approach based on the nature of their tasks and the need for adaptability, consistency, and compliance.