AI agents in business: 5 tasks they can handle today

The short answer

An AI agent can already reliably handle five types of tasks in a company: sorting e-mails and drafting replies, extracting data from invoices and documents, first-line customer support, preparing reports from company data, and writing and maintaining product copy. The same rule applies everywhere: the agent prepares and proposes, and a human approves any step with financial or legal impact.

How an agent differs from a chatbot

A chatbot answers questions. An AI agent also acts: it reads an e-mail, looks up the order in your system, drafts a reply, creates a task or writes data into accounting. It has tools, meaning access to selected systems via API, and decides on its own which one to use. That is why you need to define precisely what it may do by itself and what it may only propose.

5 tasks an agent can handle today

1. Sorting e-mails and drafting replies

The agent reads incoming mail, tells an enquiry from a complaint, an invoice or spam, routes it to the right person and drafts replies to routine questions with data from your system (order status, delivery date). A human only checks and sends. With dozens of e-mails a day this is the fastest visible saving.

2. Processing invoices and documents

From a PDF or a photo the agent extracts the supplier, company ID, amounts, VAT and due date and prepares a record for invoicing or accounting. It checks whether the supplier exists and whether the amount duplicates another invoice. Booking and payment are approved by a human.

3. First-line customer support

Working from your terms and conditions, price list and order status, the agent answers questions like "where is my parcel" or "how do I return an item". When it is unsure or the customer wants something outside the rules, it hands the conversation to a human together with a summary.

4. Reports from company data

Instead of building spreadsheets by hand, you ask: "Which products sold worse last month than a year ago?" The agent builds a query over the database or an export and returns an overview. It can prepare regular reports every morning on its own.

5. Product copy and catalogue

Product descriptions, translations, filling in missing attributes from data sheets, checking for duplicates. The agent handles hundreds of items; a human reviews a sample and the rules, for instance that it must not invent features the product does not have.

What the agent does alone and what a human approves

Task	Agent alone	Human approves
E-mails	sorting, draft reply	sending outside templates
Invoices	data extraction, duplicate check	booking, payment
Support	routine questions by the rules	exceptions, discounts, complaints
Reports	query, table, summary	decisions based on them
Product copy	draft, translation, attributes	publishing, sample check

What an agent looks like technically

There is nothing mysterious behind the word agent. It has four parts:

• A language model that understands text and decides on the next step.
• Tools: precisely defined actions such as "find an order by number" or "create a draft invoice". The agent has no free access to the database, only to these actions.
• Rules: what it may do, what it may not, and when to hand work to a human. They are written like instructions for a new colleague.
• A record: a log of every step, so you can check afterwards why the agent did what it did.

Most of the work is in the tools and rules. The model is ready-made; the connection to your systems and processes has to be built.

How to measure whether an agent pays off

Before deployment, write down how much time the task takes today, for example two hours a day sorting mail. After a month, compare: how many cases the agent prepared, how many a human approved without changes and how much time was left for review. If a human has to rewrite most proposals, the problem is usually in the rules or source data, not in the AI. Adjust them and measure again.

The most common mistakes when introducing an agent

• Too big a first step. "An agent that handles all of support" cannot be tested well. An agent for one type of question can.
• Poor source material. If your terms, price list and actual practice all say something different, the agent will answer just as inconsistently.
• No owner. Someone in the company must monitor the agent, adjust the rules and decide on exceptions. Without that, quality slowly drops.
• Blind trust after the first week. Keep sample checks running even after everything works.

Where an agent does not belong yet

• Irreversible steps without review: payments, deleting data, contracts, sending a mass mailing.
• Tasks without clear rules: if you cannot explain it to a new colleague, the agent will not do it better.
• Processes that are not in a system: when data lives in people's heads and on paper, you first need to digitise, and only then automate.

How to deploy your first agent

• Pick one repetitive task that eats time and can be measured, such as the number of e-mails or invoices per week.
• Give the agent the minimum permissions it needs. Reading orders does not require the right to change them.
• Start in proposal mode. For the first weeks the agent only prepares and a human approves. Once quality is stable, you let it handle routine cases.
• Log everything. Every agent step should be traceable: what it saw, what it did, who approved it.
• Think about GDPR. Decide which data may be sent to the AI model provider and which may not.

More on choosing your first task in the article AI in business – where to start.

Cost and timeline

Most of the work is not the AI itself but connecting it to your systems: e-mail, e-shop, accounting, database. A simple agent on top of one system is a matter of days, not months. We charge €38 per hour excl. VAT and you get an estimate of hours before we start. More about deployments on the AI for business page.

Which task would you hand to an agent?

Tell us what your company does over and over again. A senior will assess whether an agent fits and propose a first step. Get in touch.