In short
An AI agent chains several steps without intervention: read a document, extract the fields, check them against a rule, write the result into your system, and flag what needs a human decision. It is more capable than a chatbot and it fails differently, so it gets guardrails and an audit trail from the first day.
What an agent does well
- Processing documents in volume. Supplier invoices, delivery notes, statements, client files.
- Copying between two systems that do not talk to each other, without typing errors.
- Checking before writing. Does the total match the sum of the lines, does the reference exist, is the date coherent.
- Sorting and routing. Classifying an incoming request and sending it to the right person.
- Flagging the anomaly rather than passing it through silently.
The guardrails, which are not optional
- A trace of every action. Who did what, when, on which record. Without it you can neither audit nor correct.
- Explicit limits. The agent does only what is permitted and nothing beyond, even if its reasoning points elsewhere.
- A confidence threshold. Below it, the agent stops and asks.
- A human stop before anything irreversible: payment, message to a customer, deletion.
- An observation mode for the first weeks: the agent proposes, a person confirms, and you measure before handing it control.
The question to ask about an agent is not what it can do, but what it does when it is wrong. If it cannot stop, it is not an agent, it is an exposure.
Describe the task someone in your business redoes every day. We will tell you whether an agent does it better, or whether a form would do.
Ask for a quoteWhen an agent is not the answer
- Low volume. Five documents a week does not justify an agent.
- Rare, expensive decisions. Choosing a supplier, negotiating a price: low volume and costly to get wrong, exactly the wrong shape.
- Unstable rules. If the rule changes weekly, the agent will live in maintenance.
- Missing data. An agent cannot check against a reference that exists nowhere.
How we go about it
We always start with a single measurable task, never a full chain. A narrow task that works reliably beats an ambitious agent nobody can tell is right. The sequence is the same as any project, described in what a first AI project actually looks like.
Frequently asked questions
What is the difference between a chatbot and an AI agent?
A chatbot answers a question. An agent carries out a chain of actions: read, extract, check against a rule, write into a system, and flag what needs a human decision. An agent acts on your data, so it needs guardrails a chatbot does not.
Can an agent act without human approval?
For reversible, low-stakes actions, yes, once measured over several weeks. For anything irreversible, payment, customer message or deletion, we put a human stop in by design.
How do I know when the agent is wrong?
Through the execution trace and a test set. Every action is recorded with its source record, and performance is measured on cases the system has not seen. Without both, you are trusting rather than knowing.
What volume makes it worthwhile?
There is no universal threshold, but if the task comes up only a few times a week the arithmetic rarely works. Count the time actually spent before committing to anything.
What if our rules change?
Rules live in a separate layer you can change without rebuilding the agent. If they change weekly, we will say so during scoping, because in that case automation costs more than it returns.