What is an AI agent?

A chatbot answers questions. A workflow follows the same fixed steps every time. An agent sits between the two: you give it a goal, and it works out the steps, looking things up, using your systems and checking its own progress until the job is done or it needs a person.

An example. A trade customer emails: "Same as last month, but swap the discontinued fitting for whatever replaces it." An agent can read the email, find last month's order, check which product replaces the old one and whether it's in stock, build the order at the customer's own prices, and put it in front of the customer or your team to approve. Nobody had to draw that path in advance, because every email is a little different.

Where agents earn their place

Agents are at their best on work that's too varied for a fixed workflow but repetitive enough to be worth automating: order exceptions, quotes from free-text requests, supplier queries, returns that need a judgement. Where the steps are always the same, a well-designed workflow is simpler and does the job. We choose the right level for each job, and many systems combine both.

When several agents share the work, each with its own job, it's called an agentic system, and the whole field is known as agentic AI. It suits complex operations that run across teams and systems.

What keeps an agent safe

An agent can act, so its limits matter more than its cleverness. The ones we build have their own identity in each system with only the access the job needs, ask a person before anything that commits money, stock or a promise, work within a budget of steps, time and cost, keep a full record of what they did and why, and have an off switch. Those limits are enforced in code, not just described in the prompt.

How to test an agent properly before you let it act on its own: Evaluating an AI agent before you trust it, from Enceladus AI, the PureNet Group's AI strategy company.