What is an AI agent and what can it do for your business?

An AI agent is a program built on a large language model that completes a task in several steps on its own: it reads a request, works out what data it needs, finds that data in your systems and prepares the result. Unlike a chatbot, an agent does not just answer – it acts, for example by updating your CRM, drafting a quote or compiling a report. It carries out important actions only within the rules you set and hands risky ones to a person for approval.

How does an AI agent work?

An AI agent works in a loop: it receives a task, chooses an action, carries it out and checks the result. If the result is wrong or data is missing, it tries another step or asks a member of staff.

Say your shared inbox receives the email “We need another 40 of the same tiles as last time”. The agent:

1. Recognises that this is an order, not a complaint or an invoice.
2. Finds the customer and their previous order in the CRM.
3. Checks stock and the price for that customer’s group.
4. Drafts a quote and flags anything that needs clarifying.
5. Passes the draft to a sales manager for approval.

Each step uses a specific tool: a CRM search, a stock query, an email draft. The model decides which tool to use; figures and records come from your systems, not from the model.

How is an AI agent different from a chatbot or ordinary automation?

In short: a chatbot answers, ordinary automation follows a fixed script, and an agent understands free text and chooses its own actions from an approved list.

The lines are blurred: a chatbot that can check an order status already has agent features. And if data always arrives in the same format, a simple script without AI will be cheaper and more reliable.

What is an AI agent made of?

A working agent has five parts, and each one affects how useful and how safe it is.

• Language model. The “brain” that understands text and chooses actions. It can be a cloud model (for example from OpenAI, Anthropic or Google) or a model running on your own server when data must not leave your organisation.
• Tools and APIs. The specific actions the agent is allowed to take: find a customer, create a record, read a PDF, prepare an email. The agent cannot see your whole system – only the actions programmed for it.
• Memory and data. A knowledge base (price lists, policies, sample answers) and the context of the task, such as earlier correspondence with the customer. Knowledge bases often use vector search so the agent can find the right passage in a document.
• Rules. What the agent does, what it never does, its limits (amounts, customer groups, AI spending cap) and when it must hand over to a member of staff.
• Human in the loop. A person who approves important actions, plus an activity log showing what the agent did and what it based its actions on.

A common mistake is to start by choosing a model. Quality depends far more on clear rules, good tools and testing against your own examples.

Which tasks can an AI agent handle for a small business?

An agent helps most where someone reads free text and retypes it into systems. The Saitai.lt team has built solutions like these:

• Handling enquiries. The agent reads emails and form submissions, separates orders from questions and complaints, extracts contacts, products and addresses, and creates an enquiry in the CRM.
• Answers from a knowledge base. AI-powered search over a vector database returns not just links but a full answer drawn from your documents: policies, manuals, price lists.
• Filling in CRM records and spreadsheets. Data from emails, forms and documents goes into your CRM, Google Sheets or Excel without manual retyping.
• Invoice and document recognition. The agent reads invoices, picks out the supplier, amounts and dates, and prepares an entry for your accountant to check.
• Automatic reports. Every week or month, data is gathered from several systems and AI describes the changes in plain words. The figures are calculated by software, not by the model.
• Data collection and analysis. Scrapers gather information from websites or files; the agent summarises and compares it, then publishes or archives it.
• Phone calls. A voice agent recognises speech, talks to customers on the phone – taking an order or a booking, for instance – and saves a summary of the call in your system.

For each task, decide what runs automatically and what waits for approval: a CRM record, say, can be created automatically, but an email quoting a price goes out only after a manager’s review.

What are the risks and limitations of AI agents?

The main risks are errors, data protection and legal requirements – all manageable if assessed before the build.

Errors and “made-up” answers

A language model sometimes gives a convincing but wrong answer, so figures, prices and stock levels must come from your systems. The agent must be allowed to say “I don’t know” and pass the question on. It is tested before launch and after every change.

Personal data and the GDPR

If the agent processes customer or employee data, the EU General Data Protection Regulation (GDPR) applies. In practice this means:

• sending the model only the data the task requires;
• having a data processing agreement with the AI model provider and the system developer (Article 28 GDPR);
• not leaving the agent decisions that produce legal effects for a person or similarly significantly affect them – Article 22 GDPR restricts decisions based solely on automated processing.

We do not build AI that makes decisions about people on its own: the agent prepares a draft or sorts the items, and a person decides.

The EU Artificial Intelligence Act

The EU AI Act is Regulation (EU) 2024/1689. According to the European Commission’s timeline:

• the Act entered into force on 1 August 2024;
• prohibited AI practices have applied since 2 February 2025;
• most provisions, including the transparency rules, apply from 2 August 2026;
• following the 2026 amendments, the deadline for high-risk systems listed in Annex III (such as recruitment and education) has moved to 2 December 2027.

For small businesses, Article 50 is usually the most relevant: if an AI system interacts directly with people, they must be told they are dealing with AI unless this is obvious. An agent that only updates a CRM as part of an internal process is generally not high-risk, whereas one that makes decisions on job candidates or a customer’s creditworthiness may be.

Other limitations

An agent is not “set and forget”: price lists, policies and APIs change, so it needs maintenance. Each action also incurs a model usage fee, so a monthly cap is set.

How do you get started with an AI agent?

Start with one process and a pilot project, not “AI for the whole company”, so you quickly see whether the agent saves time.

1. Pick one process. A good candidate is something that happens every day, takes a lot of time and has a clear outcome, such as moving enquiries into the CRM.
2. Collect examples. 30–50 real emails, documents or enquiries, each with the correct outcome. This becomes your test set.
3. Write down the rules. What the agent does on its own, what it only suggests and what it never does.
4. Launch with approval. For the first few weeks a member of staff approves every action, and you can see how many are correct.
5. Expand. Once errors stop appearing, some actions can run without approval, or you can add the next process.

Sometimes the assessment shows you need no agent at all: simple business process automation or a website chatbot will do – which saves money too.

How much does an AI agent cost?

At Saitai.lt, building an AI agent costs from €1,490, with a typical timeline of 1–3 weeks. The final price depends on how many systems need connecting and how many actions the agent performs.

Model usage prices are published by the provider, for example OpenAI. Prices are final – no VAT is added, as we are not VAT-registered.

How can we help?

The Saitai.lt team will assess your process and tell you whether it needs an agent or something simpler. If an agent fits, we build it, connect it to your CRM, email or accounting software and launch it with staff approval.

• AI agent development for businesses – process, pricing and examples.
• AI solutions – all our AI services in one place.
• Business automation – when you don’t need AI and connecting your software is enough.
• Document recognition and report automation – when the agent needs to read invoices or prepare reports.
• AI solutions cost – starting prices for agents, chatbots, AI search and other solutions.