AI Agent

Short answer: An AI agent does more than answer a single question. It receives a goal, breaks it into steps and takes actions such as searching documents, reading a system, filling in a form or sending a message. It then checks the result and decides what to do next. A scripted chatbot follows fixed conversation paths. An agent chooses its own steps and can act for the user, within the permissions it has.

Agents suit tasks that follow a familiar pattern but vary in detail. Examples include researching a supplier, preparing a case file from several systems, and sorting and answering routine requests. Because an agent chooses its own steps, it behaves less predictably than a fixed automated workflow. A fixed workflow with one AI step inside it is easier to test, check and support. So when the steps can be set in advance, a fixed workflow is the better choice. The more freedom an agent has, the more its permissions, logs and human approval points matter.

An agent pairs a language model with a set of tools it is allowed to use. These can include a search function, a database, an email account or a direct connection into a business system. Given a goal, the model picks a tool, reads the result and plans the next step. It repeats this until the task is done or a stopping rule applies. A well-designed agent has limited permissions and a maximum number of steps. It also has clear points where a person must approve an action, and a full log of what it did and why, so the team can trace any error.

A procurement team receives 150 new supplier requests a month. For each one, an agent reads the request, checks the company registry, searches the supplier's website for certifications and compares the details with the internal checklist. It then prepares a summary that flags any gaps, and a buyer reviews and approves it. If preparation and review together fall from 45 minutes to 15 per supplier, the team saves 75 hours a month. The team also tracks how many errors the buyer catches, to judge whether the agent can take on more of the work.

The key question for leaders is which actions an agent may take on its own and which need approval. Agents can remove repetitive coordination work. But they act inside company systems, so a mistake costs more than a draft that a person reads first. Start with agents that prepare work for approval, and give them more freedom only after they show reliable, measured results.

Agents can take the wrong action or misread instructions, and their errors are harder to predict than those of a fixed workflow. Broad access to company systems creates security and data protection risks. Costs can rise quickly when an agent calls the model many times for each task. Some actions, such as sending an email or placing an order, are hard to undo. Those actions need approval controls, and the company stays accountable for them.