What is an RPA vs Agentic AI?

RPA bots are deterministic: they were recorded or coded against a specific interface and execute the same click-path every time, with no model of what the underlying task means. Agentic AI, by contrast, is built on a reasoning model (typically an LLM) that receives a goal, observes the current state of a system or document, and decides dynamically what action to take next — including recovering from an unexpected screen, an ambiguous field, or a partially completed process. The practical difference shows up in maintenance and coverage. RPA needs a human to re-record or re-code the bot whenever the target system changes even slightly, and it can only handle the exact cases anticipated in advance. Agentic AI generalizes across variation because it interprets the interface or data at run time rather than replaying a script, at the cost of being probabilistic rather than perfectly deterministic. The two aren't strictly rivals — most mature automation stacks use agentic AI for judgment and adaptation, and dispatch to RPA-style deterministic scripts for the narrow sub-steps that are genuinely fixed and don't benefit from reasoning, combining reliability where it's earned with flexibility where it's needed.