There's a lot of loose talk right now about AI "agents," so it helps to pin down what actually separates agentic AI from the tools that came before it.
The core shift: older AI tools did one job - classify a document, generate text, predict a number - and left the decision to a person. Agentic AI closes that gap. Give it a goal (say, reduce cart abandonment by 15%), and it pulls the relevant data, tests a few approaches, adjusts, and reports back, without a person greenlighting every step.
Why it's not RPA: RPA runs a fixed script - click here, copy this, paste there - and breaks the moment an input falls outside what it expects. Agentic AI has a goal and a set of tools, and it decides which tool to use and when, based on what it finds. That's why it can handle a messy inbox instead of needing a clean, standardized form.
Where it's already working:
- Inventory systems reordering stock and adjusting pricing on real-time demand
- Fraud systems freezing suspicious transactions the moment they happen
- Support systems resolving tickets end-to-end, escalating only what needs a human
- Scheduling agents booking and confirming appointments across calendars
The part that actually matters: guardrails. An agent with no spending cap can burn through a budget fast. One with no escalation rule can approve the same refund twice. The failure mode is almost never a wrong answer - it's a confident agent running unsupervised. Defining what it can decide alone, and what gets flagged for a person, is the real engineering work.
Starting a deployment? Pick one decision, not ten. Define it precisely, set the escalation rule, connect only the systems it needs, and watch its first 100 decisions closely before expanding scope.
Full piece, with more detail on deployment: Agentic AI: The Next Frontier of Autonomous Systems
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