Agentic AI is not a chatbot
If your AI still needs someone to prompt it, it's not agentic. Here's the mental model that actually matters, and how to tell when you need it.
Every vendor pitch in Q1 2026 used the word "agentic." Most of them didn't know what it meant.
A chatbot waits for you. You ask a question, it answers, the loop ends. Useful for some things — mostly not the ones that matter.
An agent loops on its own. It reads context, picks an action from a menu of tools, takes the action, checks its work, and decides whether to keep going. No one has to prompt it. That's the actual difference.
When you need an agent
You need an agent when the work has these three shapes at once:
- Multi-step — pulling from one place, writing to another, reconciling a third.
- Bursty — volume is high but uneven. A human could do it but not cost-effectively.
- Forgiving — small errors are recoverable, or you can route exceptions to a human.
Sales triage fits. Invoice-to-cash fits. Support ticket enrichment fits. Drug discovery and legal memos don't — the cost of a wrong step is too high.
The part people skip
An agent without evaluation is just a Rube Goldberg machine. You need a way to measure whether the thing is pulling its weight — agreement-with-human scores, recovery rate on exceptions, dollar-impact per run. If you can't measure it, you can't trust it in production, and you definitely can't hand it off to ops.
The tell
Ask a vendor: "What happens if the agent gets it wrong on row 437 of a 10,000-row batch?" If the answer is a live demo of the happy path, you're looking at a chatbot with extra steps.