What is an AI agent, and when does it make sense for a business?

What does the name “AI agent” actually mean?

An AI agent is a program in which a language model can select subsequent actions and use provided tools. The tool can search documents, read information from the system, or prepare a file. The program receives a goal, performs an action, analyses the result, and can decide on the next step.

The line between an agent, an assistant, and a chatbot isn't uniformly defined across the industry. Anthropic proposes a useful distinction: in a fixed process, the order of steps is dictated by the code, while in an agent, the model determines the workflow and tool usage. Therefore, the product name itself doesn't determine how it works.

When choosing a solution, ask for a description of the specific task. More important than the label is what the program will read, what output it will produce, and whether it will be able to modify data.

Chatbot, AI process or simple script?

“Chatbot” mainly describes an interface: the user types a message and the system replies. Behind the chat window there may be a simple model or a program with access to tools. You cannot tell from the interface alone.

Let's assume that each query is to be assigned a category and forwarded to the appropriate folder. If the steps are always the same, a fixed process can be designed. When the task requires the selection of various sources and subsequent actions based on the information received, it's worth considering a more flexible model.

Copying files by name or summing fixed columns doesn't have to involve AI. Automation scripts can handle those tasks. Clearer rules make it easier to define the expected result and check that the task was completed correctly.

Example: Preparing a response to a client

Imagine a company receiving inquiries about services. The scope is included in the offer, the terms of service in a separate document, and deadlines are listed in the calendar. The employee consults several places before writing a response. This is a sample scenario for analysis, not a description of a completed implementation or measured savings.

The tool could identify the need, find a service description, and prepare a draft with links to the materials used. If it doesn't have access to a calendar, it should indicate the lack of confirmation of the appointment. It shouldn't replace it with a likely-sounding date.

Initially, the employee can approve each response. This allows them to verify that preparing and verifying the draft takes less time than previous work. Only the test results provide a basis for discussing expanding the program's autonomy.

How do you use an agent day to day?

You don't have to type a long command every time. With a custom solution, you select the settings needed for a specific task, run the job, and return for the results. Available options depend on the scope of the implementation.

In the example of competitive research, the client selects companies from a prepared list and categories: advertising, new products, or both. The agent checks the agreed-upon sources and records the findings with a date and reference. Unavailable sources should be marked so that the lack of results is not confused with a lack of new offers.

A web dashboard or Discord bot can provide the interface. A button starts the task, status indicators show progress, and a history lets users revisit reports. Schedules, notifications and approval points depend on the workflow. The interface alone does not define the agent’s capabilities.

Why does an agent need an API, and where does its information come from?

In the implementations I offer, the program connects to an external AI model via an API. This is a means of communication between programs. Connecting to the model allows for processing the transferred context; access to company data requires separately prepared sources and tools.

For example, a model helps interpret a query, and a connection to a product catalogue allows for the reading of specifications. A model key does not automatically grant access to email, warehouse, or CRM. Each such connection has its own permissions and data scope.

During implementation, we agree who owns the accounts, how to add and rotate keys securely, and the service costs. Do not enter keys in a contact form or Discord channel. We also define which information may be sent to the model provider.

The agent can also use a model running locally. The external API is a requirement of the described offer, not a requirement for the definition of each AI agent. The differences are described in the guide on local AI models.

How do you establish access and responsibility?

Separately define what the program can read, what it can prepare, and what it can change. Access to the product description does not necessarily imply the right to change the price. Preparing a message does not necessarily imply the right to send it. This separation should be part of the design.

A decision also needs to be made about what happens in the event of missing data, failures, or conflicting information. Does the task return to the worker? Can the program try again? Who will see the error? Without these arrangements, correct answers in the demonstration say little about everyday usability.

Include test documents containing embedded instructions, such as requests to disclose data. The program should treat these as material to analyse, not as permission to act. A prompt instruction alone does not replace restrictions on the functions the program can use.

Do you need multiple agents?

The number of agents isn't a measure of a solution's value. A single program can use multiple tools and generate an entire report. Sometimes it's useful to separate tasks, such as data collection and comparison analysis, but such a division should address a specific problem.

More agents also mean passing results between stages, additional model calls, and more places to test. A second agent evaluating the response can also make mistakes. Therefore, in your design, ask about the result, sources, and control rules, not the mere number of agents.

How do you evaluate a pilot?

Prepare a list of issues that arise in your work: easy questions, unusual wording, missing data, and situations requiring human assistance. For each case, write down the acceptance criteria before viewing the program's response.

Assess more than just the flow of the text. Do the names and terms match? Does the referenced document actually justify the answer? How many corrections does the employee make? Is the error visible or hidden within a convincing sentence? A single successful conversation is too small a sample.

Measure the whole task, including review. If an employee must check everything from scratch, a fluent draft may not deliver the expected benefit. A pilot should also help you decide whether to simplify or postpone the project.

What does the implementation cost consist of?

Implementation involves more than model prompts. It may require preparing materials, connecting systems, setting permissions, testing and building an interface. Ongoing costs include service usage, performance checks and adapting to changes in your services or data.

Ask for development and running costs to be separated. Agree who updates the knowledge sources and handles recurring errors. A locally hosted model also needs resources and maintenance, as explained in my guide to local AI models for business.

If you'd like to evaluate an idea, please provide a sample task, sources of information, and the desired outcome. You can find a project discussion on the AI agent implementation page. I first identify the task the tool should help with, then choose how to implement it.