Agentic AI is not a smarter chatbot. It is a system that sets a goal, builds a plan, executes steps across tools and APIs, and persists until the task is done. The core technical stack combines large language models for reasoning with planning algorithms that maintain persistent state across actions. That last part is the break from generative AI: a standard LLM resets after each prompt. An agent does not.
The article draws a hard line between four operating modes, adapted from SAE autonomous vehicle levels: Observe-and-Suggest, Plan-and-Propose, Act-with-Confirmation, and Act-Autonomously. These are not a hierarchy. A single deployment can run different modes for different task types simultaneously. A scheduling agent might act fully autonomously while a financial agent stays locked in suggestion mode. The recruiting workflow example is worth reading in full: it shows exactly where RPA stops and agentic reasoning begins, using resume parsing and calendar conflict resolution as concrete cases.
The design and oversight implications are where this piece earns its length. Each autonomy mode carries distinct UX requirements: notification clarity at the observation layer, plan visualization at the proposal layer, audit trails at the confirmation layer. Product managers and UX practitioners who skip to the conclusion will miss the operational specifics that make implementation viable. Read the taxonomy section.
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