In plain English
Most people’s first experience of AI is a chat box: you ask, it answers, and you do the rest. An AI agent goes a step further. You give it a job, such as “sort this morning’s enquiries and draft replies to the ones asking for a quote”, and it works out the steps, carries them out with the tools it has been given access to, and reports back.
The word gets used loosely. Some products called agents are little more than a chatbot with a few extra buttons. Others can read email, look things up in your systems, fill in forms and update records. Microsoft now offers agents inside Copilot, and plenty of other business software is heading the same way.
What makes something an agent is that it chooses its own next step. That’s what makes agents useful, and it’s also why they need clear limits on what they’re allowed to touch.
An example from a small business
Take a hypothetical 15-person building services firm that gets a steady flow of job requests through its website and its info@ mailbox. An agent could read each request, check whether the customer is already in the job system, create a draft job with the address and fault description, suggest a slot from the engineers’ calendars and draft a reply. The office manager reviews each one and presses send. They still make the call on anything unusual, but the typing and looking-up is done before they open the email.
What to watch out for
- Give it the least access it needs. An agent that can send email or change records can also do those things wrongly. Start with read-only access and drafts, and widen it once the results have earned your trust.
- Agents can be confidently wrong. They’re built on large language models, which can hallucinate. Anything that matters should be checked by a person, at least until the agent has a track record.
- Check how it’s charged. Some agents, including some of Microsoft’s, are billed by usage rather than a flat fee, so a busy agent can cost more than you expect. Ask before switching one on.
- Not every job needs an agent. If the steps are always the same, plain workflow automation is cheaper, faster and more predictable.
Related terms
- Workflow automation: the fixed-steps cousin of an agent.
- Human in the loop: how a person stays in charge of what an agent does.
- Large language model (LLM): the engine most agents run on.
- AI hallucination: why agents’ work needs checking.
Where AlphaOps can help
We build agents and automations that take repetitive work off your team, with sensible limits and a person checking what matters. See our workflow automation service, or start with a free automation audit to find out where an agent would genuinely earn its keep.