A classic chat assistant answers a question and then waits for the next one. An AI agent goes further: it receives a task, breaks it down into steps, calls tools for them and decides based on the results how to proceed. This means AI does not just deliver text but can partly carry out workflows itself.
This makes agents interesting for automation, but it also brings new risks. A system that can act needs clearer boundaries than one that only answers.
How does an AI agent work?
The core of an agent is usually a large language model. It works in a loop:
- It analyzes the task and the current state.
- It decides which tool makes sense next, for example a search, a database query or creating an entry.
- The application executes this call and returns the result to the model.
- The model evaluates the result and plans the next step until the goal is reached or a defined limit takes effect.
Tools are connected via application programming interfaces. Standards such as the Model Context Protocol standardize how agents access data sources and applications.
Typical examples
- An incoming ticket is pre-qualified: the agent collects device data, searches for similar cases and proposes a solution.
- Incoming invoices are matched with orders, and discrepancies are flagged for review.
- For a request, information from several systems is gathered and bundled into a report.
What companies should keep in mind
- Minimal rights: An agent should only receive the permissions it needs for its task, according to the principle of least privilege.
- Approvals for consequential steps: Payments, deletions or messages to external parties should only take place after confirmation by a human, see human in the loop.
- Consider manipulation: If an agent reads emails, websites or documents, they may contain hidden instructions (prompt injection).
- Ensure traceability: Every step and every tool call should be logged.
- Set limits: Caps on steps, runtime and costs prevent endless loops and unexpected bills.
How it differs
Robotic process automation (RPA) follows fixed, programmed sequences and always delivers the same result for the same input. An AI agent decides flexibly, but is less predictable as a result. For stable, clearly regulated processes, fixed automation is therefore often the better choice. For tasks with changing, unstructured inputs, an agent can offer advantages.
View automation