AI Agents for Business: What are they and what can they do?

What is an AI agent?

An AI agent is a software system that uses an artificial intelligence model and can choose actions and tools to perform a specific task.

A standard chat assistant often answers a question based on provided text and existing knowledge. An AI agent, if it has corresponding capabilities, can perform more steps. For example:

1. Understand the user's task.
2. Decide what information is needed.
3. Utilize an available tool.
4. Evaluate the obtained result.
5. Provide an answer or prepare the next action.

This doesn't mean every AI agent can independently perform any job. Its capabilities depend on the provided tools, instructions, permissions, and control mechanisms.

How does an AI agent differ from standard ChatGPT?

ChatGPT is an AI program where various functions can be used, and in some environments – tools and agentic capabilities as well. Therefore, the difference isn't just the product name. It's more about what a specific system can perform.

Simple chat: 'Write an email to a customer regarding a late payment.' AI prepares text based on information provided by the user.

Chat with business integration: 'Find customers whose invoices are overdue by more than 14 days and prepare reminder drafts.' An AI agent can use an accounting data tool, select records, and prepare appropriate texts.

If the integration supports actions, sending reminders after employee confirmation can also be provided.

How does an AI agent get information from business systems?

An AI agent doesn't inherently know company orders, customer debts, or warehouse stock. This requires a connection to corresponding data sources. Such a connection can be created via:

• Existing connectors.
• Custom API integrations.
• MCP servers.
• Other supported tool interfaces.

MCP (Model Context Protocol) is an open standard allowing AI programs to use external tools. For example, a custom MCP server can provide a tool designed to check order status in an ERP system.

More on the technology: What is an MCP server and how does it work?

What tasks can AI agents perform in business?

AI agent capabilities depend on the specific integration. Common practical scenarios:

Sales analysis. Selecting customers based on purchase changes, preparing summaries, helping set priorities.

Customer service. Finding order information and preparing an answer draft.

Financial information. Checking invoice statuses and providing payment summaries.

Internal documents. Finding the necessary procedure, contract, or instruction.

Task management. Preparing or creating tasks in CRM or a project management system.

Manager summaries. Getting the most important data from several systems with one question.

Such functions can reduce time spent on repetitive information searches.

Practical Example: AI Agent Before a Customer Meeting

Imagine a sales manager is going to a meeting. On the way, he asks by phone: 'Prepare a short summary of Baltic Trade customer's situation.' The AI agent can perform several steps:

1. Find customer information in the CRM system.
2. Check the last registered contacts.
3. Get active order statuses in the ERP system.
4. Check allowed payment information in the accounting system.
5. Provide a concise summary.

The manager doesn't need to open three systems separately. For real operation, integrations, proper permissions, and high-quality data are necessary.

Can an AI agent work independently?

Some AI agents can perform multi-step tasks without constant human intervention. However, the level of independence must depend on the task's risk.

For example, preparing an internal report usually carries less risk than transferring money or deleting customer data. Therefore, it's worth distinguishing three levels of operation:

Informational. The AI agent only reads data and provides answers.

With confirmation. AI prepares an action, but an employee confirms it.

Automatic. Clearly defined, low-risk actions can be performed according to set rules.

Not every task is worth automating completely.

Can an AI agent make mistakes?

Yes. AI models can misinterpret a query, choose the wrong tool, or inaccurately summarize obtained data. Errors can also arise from messy data in business systems. Therefore, it's important to:

• Clearly define the purpose of tools.
• Check access rights.
• Test real questions.
• Show data sources when possible.
• Require confirmation for sensitive actions.
• Log important actions.

An AI agent should help an employee make a decision, not unnoticeably take over all responsibility.

How to start implementing an AI agent?

Start with one clear business process. For example: 'Sales managers check order statuses in several systems daily.' Then evaluate:

1. What data is needed.
2. Where it is stored.
3. Which questions recur most often.
4. What access rights are essential.
5. How you will measure benefit.

The first goal should not be the maximum number of functions, but reliable performance of a specific task.

Summary

AI agents can become an additional work tool, allowing employees to get information faster and perform certain tasks.

The greatest practical value arises when the agent has secure access to relevant business systems and clearly defined capabilities.

More on creating such solutions: AI Integrations for Business.