What an AI agent actually is

What shipped

An AI agent is not a robot employee, and it is not a magic version of ChatGPT. For a business team, a useful definition is simpler: an AI agent is an assistant that answers from approved knowledge and can take small, confirmed steps in a workflow.

That matters because many non-technical teams hear the word agent and assume it belongs to developers. In practice, a well-scoped agent can be built and maintained by trained business users, provided the work is bounded, reviewed and tested.

For example, an agent might answer staff questions from an internal policy pack, prepare a draft reply to a customer enquiry, create a task for a manager, or collect the details needed before a booking is confirmed. It should not be making final decisions on credit, employment, pricing, safety or compliance without a person in the loop.

The key design rule is this: the agent supports a decision, then a person reviews and approves before the action becomes operational. In Artellis projects, that human review step is named clearly. A common name is the Manager approval check. The point is to make the hand-off visible, not buried in the tool.

This is why agent-building training is useful. The technical parts matter, but the real skill is learning how to choose the right job for an agent, write clear instructions, connect approved source material, test edge cases, and decide where human approval sits. That is the focus of Artellis agent-building training.

Who should care

This is relevant for Irish SMEs where teams already spend time answering repeat questions, searching shared folders, copying details between systems, or drafting the same type of message again and again.

It is especially relevant to managers in operations, sales, service, finance, HR and administration. These are the people who know where the delays are. They also know which answers are safe, which need judgement, and which steps should never be automated.

The common blocker is confidence. Staff may use AI informally, but they are unsure how to turn that into something useful for the team. They may also worry that building agents requires coding, data science or a large platform project. For many SME use cases, it does not.

The better question is not “can we build an agent?” It is “what narrow job should this agent do, what knowledge should it use, and who signs off its output?”

Richard recently completed a cross-departmental Copilot Studio workshop for a large organisation. The useful lesson for SMEs is that mixed business teams can learn the basics quickly when the session is grounded in real workflows rather than abstract AI theory.

If your team can describe a process in plain English, identify the documents it relies on, and agree what must be checked by a person, you have the raw material for a first agent. The training turns that raw material into a safe, testable workflow.

For related practical thinking on where AI fits in day-to-day SME work, see the Artellis insights library.

A worked example

Take a small service business that receives regular customer enquiries by email. The questions vary, but many follow a pattern: opening hours, service areas, standard pricing bands, booking requirements, warranty terms, what to send before a site visit, and who handles urgent cases.

Today, a coordinator reads each message, searches for the right information, drafts a reply, asks a colleague to confirm anything uncertain, and sometimes creates a follow-up task. None of this is highly complex, but it interrupts the day and creates delays.

A simple agent for this workflow could be trained on approved material: the current service guide, pricing rules, booking checklist, warranty note, escalation policy and tone-of-voice guidance. The agent’s job would be limited to three things.

First, it classifies the enquiry, such as booking request, pricing question, warranty issue, urgent escalation or unclear. Second, it drafts a suggested reply using only the approved knowledge. Third, where appropriate, it prepares a task with the key details filled in.

The agent does not send the reply itself. The named human review step is the Manager approval check, or in a smaller team, the Coordinator approval check. The person checks the suggested answer, edits it if needed, and only then sends it or creates the task.

This is decision support. It reduces searching and drafting time, but it does not remove judgement. If the enquiry falls outside the approved material, the agent should say so and route it to a person. If the customer asks for an exception, the agent should not invent one. If the wording is unclear, it should ask for more information or flag the message for review.

In training, the team would build a first version, test it with real but safe examples, record where it failed, improve the instructions, and agree the approval point. That is much more useful than a demo that looks good once and then breaks when normal business messiness appears.

Where I'd start

Start with a workflow that is common, low risk and slightly annoying. Good first candidates include internal policy questions, customer enquiry triage, draft booking replies, supplier information requests, onboarding checklists, or finding the right document in a shared folder.

Avoid starting with anything that decides pay, hiring, lending, legal position, medical advice, safety actions or disciplinary outcomes. Those areas may still benefit from AI later, but they need stronger governance and review.

For a first training exercise, pick one team, one knowledge set and one clear task. Write down five things the agent is allowed to do and five things it must not do. Then define the human review step in plain words. For example: “The agent may draft a customer reply, but a coordinator must review and send it.”

Next, gather the approved source material. Do not start from every document in the business. Start from the documents a good staff member would trust today. If the source material is out of date or contradictory, fix that before asking the agent to rely on it.

Then test with realistic examples. Include normal cases, awkward cases, missing information and questions the agent should refuse or escalate. Keep a simple test log: what worked, what failed, what instruction changed, and who approved the result.

The clear takeaway is this: an AI agent is only useful when the workflow is clear. The tool can help, but the team still needs to decide the job, the knowledge, the limits and the review point.

If you want your non-technical team to learn this by building a real first agent, book a 20-minute call about agent-building training.