What Is an AI Agent?
Author: Domeny.ForSale editorial team Published
In brief: An AI agent is a system that uses artificial intelligence to achieve a goal: it chooses successive steps, uses available tools and responds to the results of its actions. Unlike a simple chatbot, it does not have to stop at providing an answer — it can, for example, check an order and prepare a complaint ticket. Its degree of autonomy depends on the permissions and rules assigned to it, not on the name “agent” alone.
What makes a system an AI agent
There is no single, universally accepted boundary between an agent and other AI applications. In practice, the term most often refers to a system that decides what to do next based on a goal and current information, rather than simply executing a predetermined sequence of commands.
Many modern agents use a large language model: a model capable of processing and generating text and interpreting instructions. However, the model itself is not the whole agent. It needs a software environment that provides tools, passes back the results of actions and enforces restrictions.
A typical agent has several components:
- Goal and instructions: define the task and the conditions for completing it.
- Context: includes the information needed for the work, such as the contents of a support ticket and what has been agreed so far.
- Tools: allow it to retrieve data or perform operations, such as searching documents or creating entries in a customer support system.
- Control rules: specify which actions are permitted, when human approval is needed and when work must stop.
An agent does not need persistent memory or internet access. It can work solely with the documents provided and within a single session.
How an agent carries out a task step by step
An agent often operates in a loop: it assesses the situation, chooses a step, runs a tool, analyses the result and makes the next decision. This loop continues until it produces a result, encounters an obstacle or reaches a limit on time, cost or the number of operations.
Suppose a shop customer asks about a delayed shipment. The agent handling the ticket might:
- Read the order number and ask for any missing information.
- Retrieve the status from the order system and obtain the carrier’s data.
- Compare the information with the policies for handling delays.
- Prepare a reply or create a ticket for a staff member.
- Stop before issuing a refund if that operation requires approval.
This is a possible implementation scenario, not a set of features every agent has. Without the appropriate integration, the system will not check the parcel’s actual status. The text “I checked the order” is not proof that an operation was performed — confirmation should come from a tool result recorded in the action history.
Agent, chatbot and automation — how they differ
A chatbot is an interface for conversing with a user. It may only answer questions, but it can also serve as a way to interact with an agent. So chat and agent are not opposites: the first term describes the form of interaction, while the second describes how a task is carried out.
Traditional automation follows predefined rules. For example: when a form is submitted, add the contact to the database and send a confirmation. If the process is predictable, this kind of solution is usually easier to test and control.
An agent is useful when the path to a result depends on the content and context. It might recognise that a customer’s message concerns both product damage and an incorrect address, then choose the appropriate course of action. However, this flexibility comes with less predictable decisions.
Both approaches can be combined in one system: AI interprets the ticket, while ordinary code checks permissions and performs the permitted operation. The model should not decide for itself whether it is allowed to bypass safeguards.
Which tasks are worth using an agent for
A good candidate is a process that requires analysing varied information, taking several steps and choosing tools. Examples include organising support tickets, gathering information from company documents, preparing reports and proposing code changes. To be useful, the result must be verifiable.
Before choosing a solution, answer three questions: will ordinary rules suffice, does the agent have access to reliable data, and can an incorrect action be easily undone? If the task involves moving data between fixed fields, simpler automation may be a better choice.
In marketing work, a sensible starting point might be an agent that drafts product descriptions based on an approved catalogue. Publishing those descriptions automatically is a separate decision: it requires additional checks because the system may add a feature the product does not have.
How to reduce errors and risk
An agent may misinterpret the goal, choose the wrong tool or generate false information. It may also treat the contents of a document as a command. This type of attack, known as prompt injection, involves placing instructions in the materials being processed in order to change the system’s behaviour.
A safer starting point is read-only access limited to the data necessary for the task. Sending messages, deleting files, publishing content and performing financial operations should have separate permissions and appropriately chosen approval requirements. Restrictions must be enforced in software, not merely written into the model’s instructions.
Limits on operations, a history of tool use and the ability to stop the process are also needed. When choosing a service, check the provider’s current documentation for its data retention policies, available integrations and billing method.
Start with one reversible task and test it on both typical and problematic cases. Increase autonomy only when you can identify what the agent did, on what basis, and who approves actions with more significant consequences.
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