Most people’s first contact with AI is a chat window: you ask, it answers, you copy the answer somewhere else. Useful — but it still leaves all the work with you.
An agent is different. An agent gets a goal, has access to a few tools, and works through the steps on its own: it reads, decides, acts, checks the result, and keeps going until the job is done or it needs you.
That small shift — from answering to doing — is where the real time savings are.
Chatbot vs. agent, in one example
Say a customer emails: “Do you have availability for a new website next month?”
- A chatbot can help you write a nice reply if you paste the email in.
- An agent sees the email arrive, knows your current workload and your pricing page, drafts a reply with real dates and the right link, and puts it in front of you with one button: Send.
Same AI underneath. The difference is that the agent is connected to your inbox, your information and a clear job description.
Where agents earn their keep
In my own work — and in what I set up for clients through Interbrain — the wins come from the same few places:
- Inbox triage and drafted replies. Every channel (email, Instagram DMs, Google Chat, website chat) lands in one inbox. The agent reads each conversation, labels it, and drafts a reply in the business’s own tone.
- Support and FAQs. Opening hours, prices, “do you deliver to…”, “is parking available” — the agent answers from the business’s real information, not from guesses.
- Reports and summaries. “What happened this week?” across messages, orders or reviews, written as a short brief instead of a spreadsheet.
- Code and websites. I build with Claude every day: agents that read a codebase, make the change, run the build, look at the result and fix what’s wrong. This very site was built that way.
The rule I never break: a human approves
The agents I build draft; people decide. For anything that leaves the business — a reply to a customer, a price, a booking — the agent prepares it and a person approves it with one click.
That is not because the AI is bad. It’s because:
- Trust is built in steps. After a few weeks of approving drafts, you know exactly where the agent is reliable and where it isn’t.
- Mistakes are cheap when they’re caught. A wrong draft costs nothing. A wrong message to a customer costs a customer.
- You stay in control of your voice. Your business doesn’t start sounding like a robot.
Once a type of task has been right a hundred times in a row, then it can be allowed to run on its own.
What makes an agent good (it’s mostly not the model)
The model matters, but in practice the quality comes from three boring things:
- Good information. An agent is only as accurate as what it can read: your prices, policies, availability, past answers. Most of the setup work is gathering this properly.
- A clear, narrow job. “Handle my business” fails. “Draft replies to booking questions using this price list” works.
- The right tools, and no more. An agent that can read your calendar but not delete it is a safe agent.
Where to start
Pick the task you do every day, that follows roughly the same pattern, and that you would happily hand to a new employee with a one-page guide. That’s your first agent.
For most small businesses in Crete that I talk to, it is the same answer: answering messages. And that is exactly what I built first — the unified inbox you can see on my home page.