AI agents aren't digital employees. Here's what they actually are.

AI agents are suddenly everywhere. They're being described as digital employees, virtual colleagues and entire teams of AI workers. That's probably not the most useful way for a business to think about them.

A few years ago, businesses were being told they needed a chatbot. Then they needed a copilot. Now apparently we all need an army of AI agents.

The terminology moves incredibly quickly. The underlying change is real. AI systems are becoming capable of doing more than answering a question or generating a piece of content. They can increasingly use tools, access information, make decisions between steps and take actions towards a goal.

That's important. But I think describing them as digital employees can make the whole thing harder to understand. An AI agent doesn't need a job title.

It needs a job to do.

So what actually is an AI agent?

At its simplest, an AI agent is an AI system that can do more than produce an answer. It can take a goal and perform actions towards achieving it. Think about the difference between these two requests.

Chatbot: Write a follow-up email to this customer. The AI writes the email.

You copy it. You send it. Done.

Now imagine: Agent: Follow up with customers who received a proposal last week and haven't replied.

That requires much more. The AI may need to: find the relevant customers,

check when proposals were sent, see whether anyone has replied, understand what the proposal was about,

prepare an appropriate follow-up, possibly ask for approval, send it,

record what happened, and schedule whatever should happen next. The difference isn't simply that the second AI is more intelligent.

It can act.

An agent normally needs four things

Strip away all the terminology and most useful AI agents need some combination of four things. 1. A goal What are we trying to achieve?

Not necessarily a perfectly scripted sequence of instructions. A goal. Find invoices that are overdue and prepare the appropriate follow-up.

Research these companies and update our prospect list. Check incoming support requests and route them to the right place. The clearer the goal, the easier it is to decide whether the agent succeeded.

2. Information The agent needs the information required to do the work. That might come from:

your CRM, documents, email,

a database, your website, a spreadsheet,

an ecommerce platform, or the internet. This is one of the reasons business AI gets much more interesting once it can connect to existing systems.

Without context, AI can give you a generic answer. With the right context, it can potentially do useful work. 3. Tools

Knowing what should happen isn't the same as being able to make it happen. An agent may need tools that allow it to: search,

browse, create a document, update a record,

send a message, run code, query a database,

book something, or interact with another piece of software. This is the point where AI moves beyond simply generating information.

It starts interacting with the systems around it. 4. Some ability to decide what happens next This is the bit that makes agents different from a traditional automation.

A normal automation might say: IF this happens, THEN do that. An agent can potentially deal with more variation.

It can look at what happened and decide which action makes sense next. That's incredibly useful. It's also the bit that means you need to think carefully about what you're allowing it to do.

AI agent or automation?

This distinction is useful because businesses have been automating things for decades. You don't need AI for everything. Suppose every time somebody fills in a form on your website you want to add them to your CRM and send exactly the same confirmation email.

That's ordinary automation. There isn't much to interpret. The rules are clear.

But suppose you want the system to read the enquiry, understand what the person is asking for, identify which service is relevant, check their existing customer history and draft an appropriate response. Now there is interpretation involved. That's where AI becomes useful.

The most effective systems will often combine the two. Automation for predictable rules. AI for the bits that require interpretation.

You don't need an intelligent agent deciding something that a simple rule could handle perfectly well.

Why I don't love "digital employee"

Because an employee is much more than a collection of tasks. A good employee understands context that may never have been written down. They know which customer needs a phone call rather than an email.

They notice when something feels unusual. They understand office politics. They know that although the process says one thing, this particular situation needs judgement.

They learn from what happened last month. They take responsibility. And if something goes badly wrong, we know who is accountable.

Current AI agents don't suddenly acquire all of that because we give them a name and a profile picture. Calling an agent Sarah from Accounts doesn't make it an accountant. It may be very good at particular pieces of accounting-related work.

That's different.

Think role within a process, not job title

This is the distinction I find much more useful. Don't start with: Can we replace this person's job with an AI agent?

Start with: What actually happens during this process? Take a sales administrator.

Their job might include: reading new enquiries, checking whether the company is already in the CRM,

researching the prospect, deciding whether the enquiry looks relevant, assigning it to somebody,

preparing information, sending follow-ups, updating records,

chasing responses, and dealing with unusual situations. An AI agent may be excellent at several of those steps.

That doesn't mean you've created an AI sales administrator. It means you've identified work inside the role that can potentially be handed over. That's a much more practical way to implement AI.

Start with the boring bits

The best first agent in a business probably isn't the one making the biggest decisions. It's more likely to be the one doing something everybody is tired of doing. Checking.

Copying. Categorising. Researching.

Chasing. Comparing. Preparing.

Updating. Moving information from one place to another. Those jobs aren't exciting in an AI demo.

But they're often where the hours disappear. If an employee spends ten minutes doing something 30 times a week, that's five hours. You don't necessarily need to replace the employee.

Giving them those five hours back may be considerably more valuable.

Agents become more useful when they can see your systems

This is where things start moving beyond the chatbot. Imagine an AI that can only see the question you type into it. It knows nothing about your business unless you explain it.

Now give an agent controlled access to: your CRM, your product information,

your internal documents, your calendar, your inbox,

your reporting, and the software your business actually uses. Its potential usefulness changes enormously.

But so does the risk. Because now the question isn't simply: What does the AI know?

It becomes: What can the AI access and what can it change?

Read access and action access are very different

I think this distinction is going to become increasingly important for businesses. There is relatively little risk in allowing an AI to read a document it is authorised to see and summarise it. There is more risk in allowing it to change that document.

More again in allowing it to send something externally. More again if it can delete information. Spend money.

Publish content. Change customer records. Or access sensitive systems.

So when we build agentic workflows, permissions need to be part of the design. Not an afterthought.

You don't have to give the agent everything

There is a strange assumption in some AI demos that autonomy is the goal. Give the agent a task. Walk away.

Come back when it's finished. Sometimes that will make sense. Often it won't.

You can put approval points into a process. An AI could: research the customer,

prepare the proposal, create the CRM record, and then stop.

A human checks the proposal. Clicks approve. The agent sends it and schedules the follow-up.

That's still a highly automated workflow. The human is simply kept at the point where human judgement has value.

Human in the loop isn't old-fashioned

I actually think this phrase can make businesses feel as though they're doing AI badly. As though the ultimate objective should be to get the human out of the loop. Why?

If a person can spend 30 seconds checking something that would previously have taken them 30 minutes to produce, that's a very successful system. You don't win extra points because nobody looked at it. The right amount of autonomy depends on the consequences of getting something wrong.

The risk should determine the autonomy

Imagine three agents. One checks your website every morning for broken links and creates a report. One prepares customer-support replies.

One can issue £10,000 refunds. Should they have the same level of autonomy? Obviously not.

The first might be able to run completely independently. The second might send routine responses automatically but escalate unusual cases. The third probably needs very clear rules and human approval.

So instead of asking: How autonomous can we make this? I'd ask:

How much autonomy does this process actually need? That's a much better design question.

Agents still make mistakes

This matters because the demos are seductive. An agent opens the browser. Clicks around.

Finds something. Updates a spreadsheet. Writes an email.

Job done. It looks like a person using a computer. That visual similarity can make us assume it has the same understanding as a person.

It doesn't. Agents can misunderstand instructions. Choose the wrong information.

Click the wrong thing. Get stuck. Misinterpret a website.

Make an incorrect assumption and then continue working from it. Or complete the measurable task while missing what the person actually intended. As agents become more capable, these problems should improve.

But for now, reliability needs to be designed into the process rather than assumed.

The best agent may be quite boring

I think businesses are going to waste a lot of time trying to build impressive AI agents. An AI chief executive. An AI marketing department.

An autonomous sales team. I'd start much smaller. Find one irritating process.

Give the agent the information it needs. Give it the minimum tools required. Define what success looks like.

Decide where a human should check the work. Log what it does. Then see whether it actually saves anybody time.

If it works, expand it. That's less exciting than announcing your new AI workforce. It's also much more likely to produce something useful.

The really interesting part comes next

Once an agent can reliably handle one task, you can connect tasks together. The research agent passes information to the CRM. The CRM triggers another process.

The AI prepares the response. A human approves it. The system sends it.

The follow-up is scheduled. The result is recorded. Now you're no longer really talking about one AI task.

You're talking about a workflow. And that is where I think the next major shift in business AI is happening. The important question is becoming less:

What can I ask AI to do? And more: Which parts of the work can I hand over?

That doesn't make AI an employee. It makes it part of the operating system of the business. And for most companies, I think that's considerably more interesting.

Where to go next

• The next shift in AI isn't better prompting. It's handing over the workflow. What changes when AI moves from completing individual tasks to carrying work through multiple systems and steps?
• AI vs automation: what's the difference? Where traditional automation works better, where AI adds something useful, and why most good systems will use both.
• Where could AI fit into your business? A practical way to identify processes worth improving before you buy another AI tool.

Related: The next shift in AI isn't better prompting. It's handing over the workflow..