Four mistakes to avoid with your first AI agent

What shipped

A useful first AI agent is usually smaller than people expect. It does not need to run a department, replace a system, or make decisions on its own. It needs to help with one repeatable task, using known sources, with a named person reviewing the output before anything operational happens.

This matters because many SME teams now have access to tools such as Microsoft Copilot Studio, Google AI Studio and other agent builders. The tools are becoming easier to use, but the first mistakes are still mostly workflow mistakes.

The four common ones are simple:

1. The agent is too broad.
2. The source material is messy or untrusted.
3. No one owns the workflow.
4. There are no test questions before launch.

None of these are technical problems at heart. They are design and training problems. That is good news, because a non-technical team can learn to avoid them.

Richard recently completed a cross-departmental Copilot Studio workshop for a large organisation. The lesson that applies just as much to an Irish SME is this: the best first agents start with a narrow job and a clear review point.

That review point should be named. I usually call it Human Review and Approval. The agent can support a decision, draft a reply, sort a request, or summarise a document, but a person reviews, tests and approves the output before it is used operationally.

Who should care

This is for owners, operations managers, customer service leads, finance managers and team leads in Irish SMEs who are being asked, or are asking themselves, whether the team can build useful AI agents without hiring developers.

If your team already knows the work but does not know how to turn it into an agent, this is the right starting point. For example, the people handling enquiries, supplier emails, sales support, tenders, HR requests or internal knowledge questions often know the exceptions better than anyone. They can spot where an agent will help and where it should stop.

It is also for managers who have seen a quick demo and now worry that everyone will build their own small bot with no controls. That concern is fair. Agent-building should not mean a free-for-all. It should mean a shared way to define the job, choose sources, test outputs and keep a person in charge.

This is not about building a custom model from scratch. It is not a data science programme. It is training a working team to build safer, smaller agents that support existing work.

The clearest sign you are ready is not technical skill. It is having one repeated task that causes delay, duplication or confusion, and a manager willing to own the first version.

A worked example

Take an Irish distribution business where customer service receives product, pricing and delivery questions by email.

A poor first agent brief would be: “Build an agent that answers customer emails.”

That is too broad. It includes pricing, account terms, stock, delivery, complaints, credit notes and probably exceptions that live in people’s heads. It also risks sending answers before anyone has checked them.

A better first brief would be: “Build an agent that drafts a suggested reply for standard delivery-status questions, using the delivery FAQ, internal escalation notes and recent approved reply examples. The customer service lead reviews the draft before sending.”

Now the scope is tighter. The sources are named. The owner is clear. The review step is built in.

The team can then create test questions before the agent is used. For example:

• “Where is my order?”
• “Can you deliver before Friday?”
• “Why has part of my order arrived?”
• “Can you change the delivery address?”
• “My delivery is late, what happens now?”

The point of these questions is not to prove the agent is perfect. It is to find where it is useful, where it is vague and where it must hand over to a person.

The output might be a draft reply with a confidence note and a suggested next action. For example, “Draft only, check order status before sending” or “Escalate to logistics, address change requested.”

That is decision support. It reduces the blank-page work and helps the team be more consistent, but it does not remove judgement. A person still checks the facts, approves the message and decides what happens next.

This is the kind of workflow that can be taught in a practical session. The team learns how to narrow the job, prepare sources, write test questions and design the Human Review and Approval step before anyone relies on the agent.

Where I'd start

Start with one agent candidate and write it down in plain English.

Use this format:

• The agent helps with: one repeated task.
• It uses: three to five trusted sources.
• It must not: list the decisions it cannot make.
• It hands over when: list the exceptions.
• A person reviews: name the role, not just “the team”.
• We will test it with: ten real questions or cases.

If you cannot fill this in, do not build yet. Clean the workflow first.

For many SMEs, the right next step is a short enablement session where managers and staff learn the pattern, then apply it to their own work. That is what our agent-building training is for. It gives non-technical teams a safe way to move from AI curiosity to a small, reviewed workflow they can actually test.

If you are still choosing the first use case, it may help to read more of our other SME AI notes and look for a task that is frequent, text-heavy and currently passed around by email.

One clear takeaway: do not begin your first agent by asking what the tool can do. Begin by asking what decision support your team needs, what material it can trust, and who will review the output before it is used.

If you want to explore this, book a 20-minute call about agent-building training. Bring one messy workflow. That is enough to start.