"AI agent" gets used loosely enough that it's worth being precise about what one actually does well. In customer support specifically, the useful version is narrower — and more useful — than the hype suggests.
Where an agent genuinely helps
Agents are strong at anything repetitive and well-defined: answering FAQ-style questions from your own documentation, qualifying an inbound lead against a fixed set of criteria before it reaches a salesperson, routing a WhatsApp or email enquiry to the right person, or reading an inbound document and filing the data it contains. These are tasks with a clear right answer, drawn from information you already have.
Where a human still needs to be involved
An agent shouldn't be the final word on anything genuinely ambiguous — a pricing exception, a complaint that needs judgment, a customer who's clearly upset and wants to be heard by a person. The useful design pattern is an agent that handles the routine 80% and hands off the remaining 20% cleanly, with full context, rather than one that tries to handle everything and quietly gets the hard cases wrong.
A simple way to decide if a process fits
If you can write the steps a competent new hire would follow — check the order status, look up the account, reply with the standard answer — an agent can very likely do it today. If the steps require weighing something that isn't written down anywhere, it's a job for a person, at least until there's enough real data to define the rule properly.
Have a process like this?
Book a discovery call