A standard AI chatbot answers one question at a time. An agent breaks a goal into steps, decides which tool or system to use for each step, executes them, and adjusts the plan when a step fails or returns an unexpected result. That loop of plan, act, observe, and revise is what separates “agentic” from “generative.”
In practice, agentic systems show up as things like an AI that reads an invoice, checks it against a purchase order in a separate system, flags a discrepancy, and drafts an email, without a human clicking through each step. The capability is real, but so is the risk: an agent that can act is an agent that can act wrongly, at speed, across more systems than a single model ever touched.
The deployment question that actually matters is not “can this be agentic” but “which of these steps should stay under human authority.” Most failed agentic rollouts skipped that question and automated the wrong step first.