What is an AI agent?

Imagine a customer enquiry comes in, but the answer is spread across previous tickets, contract agreements and internal documents. An AI agent can bring that preparation together: retrieve information, create a summary, flag missing data and present a proposal for review.

That is the practical difference from a simple chatbot. An AI agent is software that is given a goal, determines the steps needed and uses tools or integrations to carry out those steps.

The most important word is boundaries. A good agent has a clear scope of work. Which information may it read? Which systems may it change? When does an employee need to review it? Without those agreements, you mainly create risk nobody can see clearly.

How an AI agent works

Under the hood, three things matter: the model, the task logic and the integrations with your systems. That layer around the model is called the harness, and in practice it makes the difference between an agent that works and one that gets stuck. The language model understands the task and reasons about the next step. The task logic controls the goal, the intermediate steps and the point at which the work is complete. The agent also uses connections to your work, such as a search function, a database, a browser, a document system or existing business software.

A traditional chatbot explains how you write a quote. An AI agent can gather customer information, identify missing data, create a first draft of a quote and present the result for approval. The employee remains the owner of the outcome and has less manual work to do.

Where AI agents deliver value

AI agents are particularly useful for work that consists of recurring steps but is still too variable for traditional automation. For example: preparing customer enquiries, investigating invoice discrepancies, summarising documents, checking case files or retrieving internal knowledge from scattered sources.

Autonomy only matters when the work demonstrably becomes simpler. The value lies in fewer handovers, less searching and fewer manual intermediate steps. An agent therefore needs to be designed around a real process.

Take a service process. An employee receives a customer enquiry, searches previous tickets, checks the contract agreements, summarises the situation and then prepares a reply. An agent can bring those preparatory steps together: retrieve relevant sources, create a summary, flag discrepancies and prepare a proposal. The employee still decides what goes to the customer, but no longer starts from scratch each time.

What you need to watch out for

Start small. Choose one process where a lot of information is gathered, checked or retyped. Then decide which steps an agent may perform independently and where human approval remains necessary.

Pay particular attention to permissions and logging. An agent that only creates drafts carries different risks from an agent that changes customer data or performs actions in systems. For serious use, you want to be able to see which source was used, which step was taken and who approved the outcome.

You should also distinguish between preparation and execution. It is often wiser to first look for work an agent can prepare: drafts, checks, summaries, exceptions and follow-up actions. Only then does it make sense to let it carry out certain steps independently. That sequence keeps adoption clear for employees and manageable for IT.

From concept to application

The better question is which process in your organisation would benefit from agent support. That is where the real design starts: with work that recurs often, has enough value and can be safely scoped.

Start with one suitable process. The AI Readiness Scan helps you map out such processes, so you can see more clearly where agent support might make sense and which questions you should ask an implementation partner. For example, see how an agent turns meeting notes into a checked CRM update. For the broader context, you can also read the guide to AI automation for SMEs.