Agentic AI describes software in which a model helps decide what steps to take toward a goal, often using tools and feedback. The amount of autonomy can be small or large. An agent is still an application with code, state, permissions, and operating limits; it is not a person or an unrestricted worker.
For example, a support assistant may search documentation, inspect a ticket, and decide whether it needs another search before drafting a reply. The application controls which tools exist and whether any action can change customer data. A model’s suggestion is not authority to execute it.
This path starts with terminology, Python, and AI application fundamentals. It then teaches tool loops, memory, planning, durable execution, approvals, and operations. You will compare agents with simpler workflows so you learn when autonomy is useful and when it adds unnecessary complexity.
Exercise: describe an assistant that only answers and an assistant that can propose a ticket update. List the extra state and controls needed for the second.
Check: you can explain the difference between generating text and making decisions that lead to actions.