AI agent

What an AI agent is

An AI agent is a system that works through a multi step task on its own. The difference from a chatbot lies in the number of steps. A chatbot answers a question. An agent is given a goal, breaks it into steps, executes them, looks at the intermediate results and decides how to continue. To do that it may use tools, meaning functions that trigger something: query a database, produce a document, send an email.

Technically an agent is therefore not a new model but a loop around a model. The model decides the next step each time, a program executes it, and the result goes back into the model. If that loop runs without a break, an agent can carry out many actions in a short time.

What it can do in a shop

• Pre sort enquiries: read a customer email, look up the order and any return in the system, prepare a reply and present it to a person for approval.
• Check data: go through an assortment, find missing dimensions and duplicate item numbers and produce a list of open points.
• Watch competitors: capture prices at intervals, mark deviations and deliver a summary.
• Prepare research: collect sources, link statements to their origin and build a first outline.

Why control points stay indispensable

An agent that takes several steps on its own can repeat a mistake several times. A misunderstood instruction then does not lead to one wrong answer but to a hundred wrong changes. Rules for running it follow from that property.

The first principle is that writes do not happen without approval. An agent may read, summarise, suggest and prepare. It may not change prices, write to customers or delete data itself. The second principle is a limit on the number of steps and the runtime, so a loop does not continue without end. The third is logging: every tool call is recorded, with input, result and timestamp. Without that record a mistake cannot be explained later.

What decides the benefit

The benefit depends less on the model than on tool access. An agent without a connection to your own data can verify nothing and stays with general phrasing. Conversely, narrow tool access already achieves a lot: read permissions on orders and product data cover most relief in daily work. Anyone starting with such a case collects experience with mistakes, approvals and logs before attempting a second step.

Frequent questions

• Is an agent a product of its own? No, it is an arrangement of model, tools and control logic in between.
• Do I need developers? For standard cases there are building blocks. As soon as your own systems are connected, someone who can do that is needed.
• How do I recognise a good use case? By many repetitions, clear rules and a result that is easy to check.