Why Most "AI Agents" Are Still Just Chatbots With Extra Steps
The word "agent" got attached to a lot of products this year that don't actually act on anything.
Every vendor call we sit in on now uses the word "agent." Most of what's behind it is a chatbot with a system prompt and a nicer UI. That's not a knock on chatbots (they're useful), but it's a different thing, and the difference is the entire ballgame for whether the tool ever touches your P&L.
The actual test
A chatbot answers questions. An agent does things: it calls tools, reads real systems, takes actions with consequences, and can be wrong in ways that cost money if you didn't build for it.
That last part is the one teams skip. In production, we spend more engineering time on evaluation and observability (what did the agent actually do, was it right, what happens when it's wrong) than we spend on the initial build. A demo doesn't need this. A system running against your finance stack or your customer queue does.
Three questions worth asking any AI vendor
- What tools can it actually call, and what happens when a call fails? If the answer is "it just tells the user," you have a chatbot.
- How do you know when it's wrong? If there's no evaluation harness, no logging, no way to catch drift, you're going to find out from a customer, not a dashboard.
- What's the blast radius of a bad decision? An agent that can issue a refund or update inventory needs guardrails a Q&A bot never did.
None of this is a reason to avoid agents. The ones we've shipped are resolving three-quarters of inbound conversations without a human and closing the books in five days instead of twenty. It's a reason to be specific about which one you're buying before you sign the contract.