From assistant to agent
A chat assistant waits for a question and gives an answer; you decide what to do with it. An agent is given a goal and access to tools, then works out the steps and takes them. Asked to arrange a meeting with three suppliers next week, an assistant drafts an email. An agent checks calendars, sends the invitations, handles the replies and books the room.
Major vendors including Microsoft, Google, OpenAI and many business software companies are building agent features into their products. Some are general, others focused on a task like customer service, bookkeeping or IT support. The technology is developing quickly, and capabilities vary widely between products, so judge each on what it actually does today.
Where agents might help
Good candidates are routine, multi-step processes with clear rules and low cost if something goes slightly wrong. Examples include sorting and routing incoming email in a shared inbox, gathering information from several systems into a weekly report, chasing overdue timesheets, preparing draft purchase orders from a stock list, or answering common internal questions from your own policies and procedures. These are tasks where a person currently copies information between screens or follows a checklist. An agent can do the legwork and present the result for a quick human review, which is often where the real time saving lies.
Why agents need more care
When an assistant makes a mistake, a person usually catches it before anything happens. When an agent makes a mistake, it may already have sent the email, changed the record or placed the order. Errors can also repeat at speed: an agent misreading a rule might apply the same wrong action to dozens of items.
Agents also widen the security picture. They need access to systems, often with broad permissions, and they read content such as emails and documents. Attackers have shown that instructions hidden inside an email or webpage can sometimes trick AI tools into doing things the user did not intend, a problem known as prompt injection. An agent with access to your finance system and inbox is a valuable target.
Controls to put in place
Give each agent the narrowest access it needs, ideally through its own account rather than a person's, so its actions are clearly identified and can be switched off. Require human approval for anything that sends money, contacts customers, deletes data or changes important records. Keep logs of what the agent did and review them, especially in the first weeks.
Set limits on volume and scope, such as the number of emails it may send per day or the systems it may touch. Decide who owns each agent, who monitors it and how it is turned off quickly if it misbehaves. Treat an agent like a new junior staff member with access to company systems: supervised, limited and reviewed.
Questions to ask before deploying one
What exactly will it do, step by step, and which systems will it access? What happens when it is unsure? Can it be restricted to suggesting actions before it is allowed to take them? Where is data processed and does the vendor use it for training? How are actions logged, and can you see why it did something? Also ask what it costs to run. Some agents are priced per action or by usage, which can add up quickly if a process runs often. Run a limited trial with full logging before letting an agent loose on live processes.
A measured approach
For most small businesses the sensible path is to start with agents that prepare work for people to approve, in a process where mistakes are easy to spot and undo. Expand their autonomy only when the logs show they are consistently right. Keep your AI use policy updated to cover agents, and make sure staff know which processes involve one. Agents are likely to become a normal part of business software over the next few years. Getting the controls right on a small, low-risk process now builds the experience to use them safely on more important work later, without betting the business on a technology that is still maturing.
Keep an eye on the agent features that arrive quietly in software you already use. Vendors increasingly switch them on by default or offer them in a settings menu staff can reach. Ask whoever manages your systems to review new AI options when updates are announced, so the decision to let software act on the business's behalf is always made deliberately.
In short
- AI agents take actions across systems rather than just giving answers.
- Start with routine, low-risk, multi-step tasks and human approval.
- Give agents their own narrowly scoped accounts, logs and volume limits.
- Hidden instructions in emails or webpages can mislead agents, so limit their access.