Agentic AI is not a smarter chatbot. It plans, executes, and persists toward a goal with minimal human intervention, and it is arriving in production systems now. The core distinction from a decade of predictive and generative tools: a generative model completes one request and resets. An agentic system maintains state, chains actions across APIs and LLMs, and adapts when steps fail. The article traces this directly against RPA, which is rules-based and brittle, following a script rather than writing one.
The piece proposes a four-level taxonomy borrowed from SAE autonomous vehicle standards and applied to digital UX: Observe-and-Suggest, Plan-and-Propose, Act-with-Confirmation, and Act-Autonomously. These are not a hierarchy. A single deployment might run level one for financial transactions and level four for calendar scheduling simultaneously. The recruiting and DevOps walkthroughs are worth reading in full because they show exactly where human oversight hands off to the agent at each level, and what the UX confirmation surface looks like at each boundary.
What makes this article worth your time is not the conclusion. It is the design and oversight implications attached to each level, written specifically for UX practitioners and product managers who will build these interfaces. The audit trail requirements at level three and the notification design constraints at level one are practical, not theoretical. If your team is scoping an agentic feature in the next two quarters, this taxonomy is a usable starting framework.
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