Insights
What is an AI agent, and what should we automate first?
An AI agent is the difference between a tool that answers and a tool that acts. A chatbot responds to a prompt and stops. An agent is given a goal, breaks it into steps, uses tools and systems to carry them out, checks its own progress, and keeps going until the job is done or it hits a limit you set. That shift — from answering to doing — is what has moved agents to the top of the AI agenda, and it is also what makes choosing the first one carefully worth the effort.
This is a plain-language guide to what an agent is, where it earns its place, and how to pick the first process to hand it.
What an agent actually is
Picture the difference through a task. Ask a chatbot to "chase our overdue invoices" and it writes you a tidy reminder email to send yourself. Give the same goal to an agent and it pulls the overdue list, drafts a reminder tuned to each account's history, sends the routine ones, flags the awkward ones for a person, and logs what it did.
The agent has three things the chatbot lacks: it can take actions in your systems, not just produce text; it works across multiple steps towards a goal rather than one turn at a time; and it operates with some autonomy, deciding what to do next within limits you define. None of that removes the need for oversight — it raises it — but it is where the leverage comes from.
Where agents help, and where they don't yet
Agents earn their place on work that is repetitive, rule-shaped, and spread across systems — the tasks where the cost is coordination rather than judgement. Chasing invoices, triaging inbound requests, reconciling records between two systems, first-pass drafting, monitoring for changes and routing them: these play to the strengths.
They struggle where the work turns on scarce context, high-stakes judgement, or steps a mistake makes expensive to undo. An agent that files the wrong thing is a nuisance; an agent that pays the wrong supplier is a problem. The rule of thumb: the more consequential and irreversible the action, the more the human belongs in the loop — and the earlier you should ask whether an agent is the right tool for that step at all.
How to choose the first process
Resist the instinct to automate the most painful process first. The opening agent should be chosen to succeed and to teach, not to conquer your hardest problem. Score candidate processes against five questions.
| Question | You want | Why it matters |
|---|---|---|
| Volume | High and repetitive | Enough repetition for the effort to pay back |
| Rules | Clear and stable | Agents follow patterns; fuzzy, shifting rules break them |
| Reversibility | Mistakes are cheap to undo | Keeps the first project low-risk while you learn |
| Data access | The systems it needs are reachable | An agent that cannot reach its data cannot act |
| Measurable outcome | A number that moves | Proof it worked, and the case for the next one |
A process that scores well on all five — high-volume, rule-based, low-stakes, connected, and measurable — is your first agent. Inbound-request triage or invoice chasing usually fits; a nuanced pricing negotiation does not.
Keep a human in the loop
The point of an agent is not to remove people; it is to strip the coordinating drudgery from around a decision so people can spend their judgement where it counts. Set the boundaries deliberately: what the agent may do on its own, what it must hand to a person, and what it must log. Where agents deal with customers or shape decisions about people, disclosure and oversight are not only good practice — under the EU AI Act's transparency rules they can be an obligation.
Start with one
The mistake is to plan an agent programme before you have run a single agent. Take one process that scores well on the five questions, give it clear limits and a human backstop, measure it against the number it was meant to move, and learn from what it gets wrong. One working agent, judged honestly, teaches you more about where they fit in your business than any strategy deck — and it earns the right to the second.
Picking the first process for value and safety, wiring the agent to the systems it needs, and setting the oversight around it is the process-and-agent readiness Firestarter runs in its six-week accelerator — the easy wins first, with a human in the loop by design.