Poradnik
An AI agent for monitoring websites: how to receive only the changes that matter
An AI agent for monitoring websites can collect information from chosen sources, recognise changes and pass them to the person responsible for acting on them. What decides whether it is useful is whether the recipient gets current, complete information they can act on. The number of pages visited says little about the value to the company.
This matters for teams tracking notices, calls for applications, terms of cooperation, supplier catalogues or market information. With more sources, checking by hand becomes a job of its own. Comparing versions and working out whether a change even affects the company also eats time.
How to decide what the agent should monitor
Start from the decision the information supports. Someone handling training may need to know a deadline has moved. A purchasing team may track delivery terms. In both cases what is needed is a specific update, not a summary of everything that appeared online.
A good task description contains the list of sources, the information sought, how often to check and who receives the result. From the outset, explain what counts as a meaningful change. A new deadline, a different price and a corrected typo may need completely different treatment.
Also set out how gaps are signalled. An unavailable page should carry the status “not checked”, so the recipient does not confuse it with a confirmed absence of changes, especially where monitoring replaces a regular check done until now by a person.
An unavailable source ≠ no changes
Record the gap, retry the check, show it to the recipient.
What a useful notification should contain
A notification should let the reader see quickly what happened. A useful record covers the source name, the link, the date checked, the previous and current value and a short description of the change. If a document has its own publication or effective date, keep that separate from the retrieval date.
Colour can speed up reading, but a text description is needed too, for example “deadline moved from 12 to 19 October”. That keeps the meaning clear once the message is printed or forwarded. These dates are only an example of the message format.
Not every update has to trigger an immediate alert. You can separate changes that need a fast reaction from information gathered into a weekly summary. The rules should follow the recipient’s needs and the consequences of reacting late.
How to check whether monitoring saves time
Compare the whole task. Before the change, count searching, comparing and recording information. After the change, include reviewing notifications, clarifying doubts and making corrections. Too many irrelevant alerts can absorb a large part of the saving.
Worked example, fictional figures: assume eight hours of manual work a week and one hour of review and corrections after monitoring goes live. The difference is seven hours, that is 87.5% of the previous time. This shows how to do the arithmetic, not the outcome of a particular deployment.
Check completeness separately. Take a sample of the information that should have been detected and compare it with what the tool produced. An absence of error reports does not show how many changes were missed. To judge quality you need to know what you were looking for and how many cases were checked.
Does every monitoring job need an AI agent?
If the sources offer structured data and the comparison rule is fixed, check a simpler retrieve-and-compare mechanism first. AI can help where document content varies. More autonomy for an agent needs a separate assessment: which further actions it may choose, and when it should ask a person to decide.
We cover choosing a solution in more depth in AI agent or automation?. In practice one process can combine fixed rules, text analysis by AI and human review.
Where to start the first test
Choose a subset of sources covering both easy and hard cases. Agree the expected result with the person who will receive it, and keep the manual baseline for comparison. Only then judge whether it is worth widening the scope.
At dyrektor.ai we help establish what information the company needs, prepare a test and choose the next step based on the result. The starting point can be one monitoring job that regularly takes someone’s time.