Is Agentic AI truly different from advanced RPA or just a new marketing buzzword for automation?

All answers to this question.

The distinction lies in the decision-making core. While RPA follows a rigid "if-this-then-that" logic tree, Agentic AI leverages Large Language Models to handle ambiguity. An agent doesn't just execute a script; it perceives an environment, reasons through a goal, and selects the right tools dynamically. In my experience implementing these in 2023, the biggest shift was seeing the system handle edge cases that would have crashed any traditional bot. It’s about moving from "doing" to "thinking and doing" without a human needing to map out every single possible sub-task.

   Answered 2024-08-20 by Jennifer Miller

That’s a fair point, Jennifer, but don't you think the "autonomy" is still heavily gated by the quality of the system prompt? If the prompt isn't perfect, doesn't the agent just hallucinate a path that leads to operational failure?

   Answered 2024-09-22 by Robert Taylor

• Robert, you've hit the nail on the head regarding prompt engineering. The autonomy is real, but it requires "constitutional" guardrails. We use frameworks like ReAct to ensure the agent critiques its own plan before execution. This "human-in-the-loop" oversight is what makes it enterprise-ready rather than just a rogue script running on a server.

     Commented 2024-09-25 by Jennifer Miller

Essentially, RPA is a digital worker following instructions, while Agentic AI is a digital collaborator that understands the "why" behind the task.

   Answered 2024-09-27 by Linda Williams

• Exactly, Linda! The goal-persistence in agentic systems is the game-changer. It doesn't just stop when it hits an error; it tries to find a workaround to reach the objective

     Commented 2024-09-28 by Sarah Jenkins