You keep hearing that AI agents will run half your office, but nobody shows you one actually doing a job. So here are AI agents examples in real life: a payments company whose assistant handled 2.3 million customer chats in a month, a Nigerian service that moves money from inside WhatsApp, and an AI that was given a small shop to run and lost money on it.
The wins and the failures both matter. If you own a business, the failures will probably teach you more.
First, what counts as an AI agent?
A chatbot answers questions. An AI agent finishes a task. It reads the request, decides the next step using your rules and data, acts inside your systems, and hands over to a person when judgement is needed.
That is the test to apply to every example below: did the software only talk, or did it do something? If you want the longer explanation, we wrote one on AI agents vs agentic AI.
Four real examples, with the awkward parts left in
1. Klarna: customer service at huge scale, then a rethink
In February 2024, the payments company Klarna said its AI assistant had handled 2.3 million conversations in its first month, which was two-thirds of its customer service chats. Klarna said the assistant dealt with refunds and returns, cut the time to resolve an issue from 11 minutes to under 2, and reduced repeat enquiries by 25%. Those are Klarna’s own figures.
The story did not end there. In May 2025, Klarna’s chief executive told Bloomberg the company was hiring human support staff again. As Entrepreneur reported, he said AI support was cheaper but gave “lower quality” results, and that customers must always know they can reach a person.
What to take from it: an agent can clear a mountain of routine requests. But if you judge it only by the money it saves, your service gets worse.
2. Xara: banking by WhatsApp message in Nigeria
Closer to home, Xara is an AI banking assistant built by Xava Technologies that works inside WhatsApp. According to a TechAfrica News report from August 2026, customers can send money, pay bills and schedule payments by text, voice note or image, in English, Pidgin, Hausa, Yoruba and Igbo. The same report says it is built to work on low-end phones and unreliable internet, and that Xara has partnered with Flutterwave for its payment rails.
We have no connection with Xara and have not tested it. We include it because it shows the idea clearly: you say what you want in a chat you already use, and the software carries out the transaction.
What to take from it: for Nigerian customers, an agent often belongs in WhatsApp, in the languages people really speak, on the phones they really own.
3. Project Vend: an AI ran a shop and lost money
In 2025, the AI company Anthropic let one of its own models run a small shop in its San Francisco office for about a month. The AI chose what to stock, set prices, contacted suppliers and chatted with customers. Anthropic then published what happened, including the embarrassing parts.
The agent was good at finding suppliers and responding to what customers asked for. But it sold some items below cost, handed out discounts whenever people pushed, told customers to pay into an account that did not exist, and at one point claimed to be a human who would deliver goods in person. The shop did not make money. Anthropic’s own conclusion was that it “would not hire” the agent to do this job yet.
What to take from it: an agent left alone with a broad job and no limits will make mistakes a sensible shop assistant would never make. Narrow tasks, spending limits and a person checking are the things that make an agent useful.
4. Air Canada: you answer for what your AI says
This one is a chatbot, not a full agent, but every owner should know it. An Air Canada website chatbot told a grieving customer that a bereavement discount could be claimed after the ticket was bought. The airline’s actual policy said otherwise. In February 2024 a Canadian tribunal ruled that the airline had not taken reasonable care to make its chatbot accurate, and ordered it to pay the customer, as CBC News reported. The tribunal said it made no difference whether the information came from a web page or a chatbot.
That was a Canadian case, and Nigerian law may treat things differently. The practical point still holds: your customer will hold you to what your AI tells them.
AI agents examples in real life: what they’d look like in your business
You are not Klarna. So here is the same idea at the size of a Nigerian business. These are illustrations, not client stories. They show the kind of job an agent suits.
| The job | What the agent does | Where a person steps in |
|---|---|---|
| A hotel gets a booking enquiry at 11pm | Replies at once, checks room availability, sends the rate and a payment link, logs the lead | Group bookings, discounts and complaints |
| A school is owed fees by 80 parents | Checks the fees record, sends each parent a polite reminder with their own balance, records who has paid | Payment plans and hardship cases |
| A supplier gets 40 invoices a week by email | Reads each invoice, matches it to the order, flags the ones that do not add up | Approving every payment |
| A clinic gets the same ten questions all day | Answers from the clinic’s own documents and books the appointment | Anything medical |
| A distributor runs low on fast-moving stock | Watches stock levels and drafts the purchase request before the shelf is empty | Signing off the order |
Notice the last column. In every row a person keeps the decisions that carry risk: money going out, exceptions, anything sensitive. That is the lesson from Klarna and the shop experiment, applied before the problem happens.
If you want to put a naira figure on work like this, our post on AI automation in Nigeria walks through the sums.
How to tell if a job suits an agent
Looking across the real examples, the jobs that work share a few things:
- It happens often. Dozens of times a week, not twice a year.
- It follows rules. A new staff member could do it from a written guide.
- The information exists somewhere. In your email, a spreadsheet, your accounting system or your price list.
- A mistake can be caught. There is a point where a person can check before real damage is done.
- There is a clear handover. The customer can always reach a human.
If a job fails two or more of these, leave it with your people for now.
Frequently asked questions
What are examples of AI agents in real life?
Real examples include Klarna’s customer service assistant, which handled refunds and returns across millions of chats; Xara, which lets Nigerians send money and pay bills through WhatsApp; and Anthropic’s experiment in which an AI ran a small shop. In a smaller business, an agent might chase unpaid invoices, answer booking enquiries or match invoices to orders.
Can a small business use AI agents, or is this only for big companies?
A small business can use one, as long as it starts with a single narrow job. The big-company examples make the news, but the same pattern works for one inbox, one fees list or one stock sheet.
Do AI agents make mistakes?
Yes. The shop experiment and the Air Canada case show it plainly. That is why a sensible setup includes limits on what the agent can do, a record of what it did, and a person who approves anything risky.
Do AI agents replace staff?
Klarna’s experience suggests not in the simple way people expect. The company leaned hard on AI, then went back to hiring people for support. Agents are best at taking the repetitive part of a job so your staff can handle the cases that need a human.
Want to see which of your jobs would suit one?
At Kotlead we build AI automation and agents for businesses, and our approach is the cautious one these examples point to: find where the savings are, pilot one process, connect your systems, and keep people in charge.
Bring us the job your team hates most. Book a free 15-minute call and we will tell you honestly whether an AI agent can take it on.