The AI Act: What SMEs Must Document Before Deploying an AI Agent

AI agents are leaving the experiment stage and moving into daily business work: answering calls, reading documents, classifying candidates, preparing actions. For an SME that is a real opportunity — but the moment an agent touches a live process, the company takes on responsibilities. The EU AI Act is a risk-based framework, and it places obligations on deployers, not only on providers. The good news: the essentials can be documented up front, before launch, not after the first problem. Here is what an SME should write down before an AI agent touches real data, users or decisions.

The AI Act is risk-based

The same technology does not carry the same obligations across use cases. A tool that summarises meeting notes is nothing like a system used in recruitment, HR, a medical setting, or personal safety. That is the core logic of the regulation: the higher a system's impact on a person, the stronger the obligations for transparency, documentation and oversight. So the first step is not buying a tool. It is documenting the use case — because the use case, not the technology, is what decides which obligations apply.

Document the use case

A vague use case ("we want an AI assistant") becomes a risky one. It needs to be written down clearly: what problem the agent solves, which workflow it fits into, who uses it, who is affected by its outputs, what data it sees, what output it produces, what stays human, and what it must never do. It should also define how success is measured. This document fits on a single page, but it is what turns a general idea into a scope you can assess, control and contain.

Classify the risk, even by default

The next question is direct: could this use case be high-risk? Does it affect people, employment, health, safety, finance, or access to services? High-impact uses call for stronger controls — and that is expected. But even when you conclude that a use is not high-risk, that reasoning should be written down. A stated, dated and justified classification is worth far more than silence: it shows the question was asked, and it becomes a reference point if the use later shifts onto more sensitive ground.

Inventory the real data

"Internal documents" is not an inventory. List the real sources: which data categories, who owns them, what permissions apply, what retention, and above all whether personal or sensitive data is involved. You also need to know whether that data leaves the company, passes through third-party servers, or is used to train models. GDPR obligations do not disappear because AI is involved: purpose, legal basis, minimisation and retention still apply. A precise inventory is what lets you say, later, exactly what the agent could have seen.

Vendor commitments in writing

Marketing claims are not enough — what counts is the contract. Where is the data hosted? Who are the subprocessors? Is the data used to train models? How long is it kept, how is it deleted, and how can it be exported? Is there a data-processing agreement (DPA) and security guarantees? And what happens to data access after termination? An SME deploying an agent on real data needs written, verifiable answers. If a vendor cannot commit to it in the contract, that is already information.

Oversight, access control and transparency

Three safeguards are decided before launch. Human oversight first: who reviews the agent's outputs, and which of them need approval before they are used? Access control next: the agent should only answer from data the user is allowed to see, so an employee cannot indirectly obtain what they could not open themselves. Transparency last: people who interact with an AI system should be told they are doing so. These rules do not slow the agent down — they make it usable within a real framework.

Logging, monitoring and incidents

An agent that enters daily work must leave a trace. Keep appropriate logs, define who reviews the quality of responses, how errors are handled, how a serious incident is escalated, and when the system is paused. Without that monitoring, an SME only discovers problems once they have become visible from the outside. With it, the company can explain what the agent did, fix what needs fixing, and show that it stays in control.

Where BeLogic fits

At BeLogic, we help SMEs deploy AI agents with practical governance from the start: a clear use case, approved data sources, a defined human review, access control, visibility over sources, plus logging and monitoring. We also provide a simple documentation pack that positions the project against the GDPR and the AI Act, and we help clients access regional AI subsidies. The aim is straightforward: an AI agent should enter daily work as a controlled process, not as an experiment with no record.