An AI agent is a program that uses a large language model to reach a goal in several steps: it understands a request, decides what to do, uses tools (your software, your files, your email), checks the result and reports back. Where a conversational assistant simply answers, an agent acts. That is what makes agents the most promising business use of artificial intelligence today — and also the one that demands the most discipline.
AI agent, chatbot, automation: what is the difference?
The three terms are often used interchangeably, yet they describe very different levels of autonomy.
| Chatbot or assistant | Classic automation | AI agent | |
|---|---|---|---|
| What it does | Answers a question | Runs a fixed script | Plans and carries out actions |
| Handling the unexpected | Limited | None: the script breaks | Good, within a defined scope |
| Access to your tools | Read-only, sometimes | Yes, hard-coded | Yes, chosen case by case |
| Example | “What are your opening hours?” | “Every Monday, export this table” | “Prepare reminders for overdue invoices” |
A chatbot holds a conversation. Classic automation — a script, or an “if this, then that” tool — runs steps defined in advance and stops at the first case that does not fit. An agent combines both: it understands an instruction written in plain language, then decides for itself which sequence of actions will satisfy it, adapting to the data it meets along the way.
How does an AI agent work?
Under the bonnet, an agent rests on four building blocks.
1. A language model that reasons
At the heart of the agent sits a large language model (LLM). It reads the instruction, breaks it into sub-tasks and decides, step by step, what to do next. Recent models can draw up a plan, adjust it when a result is unexpected and explain what they have done.
2. Tools
A model on its own can neither read your accounts nor send an email. So it is given tools: precisely described functions (“search for an invoice”, “create a draft”, “read a file”) that it can call. These tools go through your software’s interfaces (APIs), through standard connectors such as the Model Context Protocol, or even through browser automation when a service offers no interface at all.
3. Memory and context
The agent needs to know where it stands: the instruction, the steps already taken, the results so far. It can also have a longer-lasting memory — internal rules, a customer’s particularities, house procedures — so nothing has to be re-explained for every task.
4. Guardrails
This is the most important block in a business setting: a clearly limited scope (which folders, which applications, which actions), permissions modelled on those of an employee, human approval before any binding action (sending, paying, deleting) and a log of everything that was done.
Which tasks should you give an AI agent?
The best candidates share three traits: they are repetitive, they revolve around text or documents, and they call for a little judgement (otherwise classic automation will do).
- Admin and accounting: reconciling bank statements with invoices, checking entries, preparing reminders for overdue invoices, extracting data from scanned documents.
- Customer service: sorting incoming requests, pulling up a customer’s history, drafting a personalised reply for approval.
- Sales: preparing a quote from a request, updating the CRM after a meeting, summarising exchanges with a prospect.
- HR: preparing files, checking payroll variables, answering routine staff questions from internal documents.
- IT: inventorying equipment, diagnosing a workstation, applying updates with each command approved.
- Document production: compiling reports, spreadsheets or presentations from several sources.
In each case, the agent does the gathering, checking and drafting; the person keeps the decision.
The limits you need to know
An AI agent is neither infallible nor magic. Four limits must be designed for from day one.
Model errors. A language model can be wrong with total confidence. The remedy: have exhaustive processing (going through 500 lines, adding up amounts) done by code rather than by the model, and have sensitive results checked by a person.
Prompt injection. A malicious document or email can contain hidden instructions designed to hijack the agent. A strict scope, minimal permissions and human approval of binding actions sharply limit the damage.
Confidentiality. The agent sends the model the extracts it needs to do its job. So choose a provider whose terms rule out training on your data and, for sensitive information, anonymise it before it leaves.
Cost and latency. Every step consumes model calls. A well-designed agent avoids needless round trips and keeps the model for the decisions that genuinely need it.
Rolling out an AI agent: the method
- Pick a precise, measurable task. “Prepare reminders”, not “run the accounts”. Measure how long it takes today.
- Map the data and the tools. Where does the information live? Does the software have an API? Is an MCP connector or browser automation needed?
- Define the scope and the approvals. What the agent may read, what it may change, and what a person must always approve.
- Build a pilot. A few weeks on real cases, with a detailed log, to measure time saved and quality.
- Extend step by step. Once the first task is reliable, add another, reusing the connectors already in place.
This step-by-step approach is how we design custom AI agents. It is also the philosophy behind our software AIKeep, which runs this kind of processing directly on a company’s workstations, within a scope declared in advance and with every action logged.
Bespoke agent or off-the-shelf software?
Two approaches coexist. Ready-made agent software suits needs built around common tools (email, office documents, accounting, banking): it is quick to get going and the connectors already exist. A bespoke agent makes sense when the process is specific to your trade, must fit inside an existing application, or when security requirements call for a particular architecture. Either way, connecting AI to your software is the part that deserves the most care.
Frequently asked questions
Can an AI agent replace an employee?
No, and that is not the point. It takes on repetitive tasks — often the ones nobody has time to do properly — and leaves decisions to people. The gain is measured in hours freed up and errors avoided.
Do you need technical skills to use an agent?
Not to use it: instructions are given in plain language. Setting it up — connecting software, permissions, guardrails — is integration work.
How is an AI agent different from RPA?
RPA (robotic process automation) replays clicks according to a fixed script. An AI agent understands the instruction and adapts to the content: it can read a freely written email, which an RPA robot cannot.
Is an AI agent GDPR-compliant?
It can be, if it is designed that way: a defined purpose, minimal data, a provider bound by contract, logging, and information for the people concerned. Our article on AI, the GDPR and the AI Act covers the obligations in detail.
Step By Step, a software and AI studio based in Ajaccio, Corsica, builds AI agents that are controlled and measurable. If a task is eating up too much time, tell us about it: it is often the best place to start.