AI agents: what they change for your work

For a year now, one word has kept coming up in every announcement: “agent.” Every provider promises theirs. But behind this buzzword — one more in the vocabulary of generative AI — what really changes for someone who works? A breakdown without the hype.

An assistant that acts, not only answers

A classic chatbot answers a question, then stops. An agent goes further: you give it a goal, and it chains several steps to reach it, using tools.

Take “prepare a comparison of the three suppliers in my sector.” An agent can search for the information, organise it into a table, then write a synthesis. Where you would have asked ten successive questions, you ask one, and it handles the chain.

The difference comes down to one word: autonomy. The agent decides the intermediate steps instead of waiting for you to dictate them.

	Classic chatbot	Agent
Behaviour	Answers, then stops	Pursues a goal over several steps
Initiative	None: it waits for the next question	Decides the intermediate steps itself
Tools	Text alone	Search, files, other software
Example	“What is a comparison?”	“Prepare the comparison and write the synthesis”
When to use it	A one-off answer	A multi-step task with a verifiable output

What it already makes possible

The most solid uses today are rarely spectacular, but concrete:

• Sort and route. Read incoming requests, classify them, prepare a template reply to validate.
• Gather information. Cross several sources and draw a structured synthesis from them.
• Assist over the course of a task. A developer who delegates a fix, an analyst who automates a recurring report.

The common thread: a repetitive task, with a clear goal and a verifiable output.

A concrete example: the weekly report

Take a task many people redo every week: compiling an activity report. Here is how an agent runs through it, where you would have chained the manipulations yourself:

1. Goal received: “prepare this week’s report from these three sources.”
2. Collection: it fetches the figures from each source (spreadsheet, tracking tool, inbox).
3. Formatting: it arranges the data into the report’s usual format.
4. Synthesis: it writes the three highlights of the week.
5. Handover for validation: it presents the whole thing — you correct, you validate, you send.

The gain is not magic: it is the sum of five micro-tasks you no longer do by hand. And step 5 stays yours — the agent prepares, you decide.

Where you must stay cautious

Autonomy does not mean total reliability. The more steps an agent chains, the more a small error early in the chain can propagate. On a sensitive action — sending a message, committing a spend, changing data — the right reflex remains human validation before execution.

In other words, an agent is an excellent doer and a poor final decision-maker. It saves time on the “how,” not on the “should we.”

Three questions before handing over a task

Before delegating a task to an agent, these three questions avoid nasty surprises:

• Is the output verifiable? If you cannot judge the result at a glance, you will not know when the agent gets it wrong.
• Can a mistake along the way be recovered? Prefer tasks where a misstep costs a few seconds, not an invoice.
• Where does human validation go? Identify the consequential step (sending, spending, changing) and keep control there before execution.

If all three answers are clear, the task is a good candidate. Otherwise, start smaller.

Should you get into it now?

There is no need to automate everything at once. The approach that works: spot a low-risk task you redo every week, and test whether an agent can take part of it. To frame it properly, our 5-step automation method gives a thread to follow. You will keep control of the validation, and you will measure the real gain rather than the promised one.

Agents do not replace your judgement. They shift your work: less execution, more supervision. That is already a change worth paying attention to.