AI agents for business: what they are and what they can do right now

The word "AI" now appears in the description of every other product. This has blurred its meaning badly: it's hard to tell where the real capabilities end and the marketing begins. Let's get specific: what an AI agent is, how it differs from other tools, and what it can actually do for a business today.

No hype, no exaggeration.

Three levels of AI, and where the agent fits in

A neural network or language model

This is the base level. ChatGPT, Claude, Gemini, YandexGPT: language models that can generate text, answer questions, translate and summarize. They're great tools for specific tasks, but on their own they aren't built into your business processes. You ask a question, you get an answer. That's it.

A chatbot

The next level. A bot works from a pre-written script: if the customer types "I want to order," the bot moves to the order step. It handles standard scenarios well but breaks down on non-standard questions. It doesn't understand context and doesn't make decisions beyond the script. For simple, repetitive questions that is often enough; see chatbots for WhatsApp and Telegram.

An AI agent

One level higher. An agent is a system built on a language model that has a goal, tools to achieve it, and the ability to make intermediate decisions. An agent doesn't just answer a question, it completes a task: it gathers information, interacts with external systems, checks the result, and adapts its actions to the situation.

The difference is like the one between a reference book and an employee. A reference book answers questions. An employee gets tasks done.

What an AI agent is made of

Under the hood, every working agent has five parts, and the result depends on each of them:

1. A language model. It understands the message and phrases the answer. It can be swapped for another one if a better fit appears.
2. A knowledge base. Your prices, terms, answers to common questions, internal rules. The agent answers from it, not from general knowledge on the internet.
3. Tools. Connections to the CRM, 1C, the warehouse, the calendar, WhatsApp. Through them the agent doesn't just talk, it acts: creates a deal, checks stock, books a slot.
4. Rules. What the agent may and may not do: no promised discounts, no delivery dates that aren't in the base, call a manager on a complaint or a large order.
5. A log. Every conversation and every action is recorded. The log shows where the agent got it wrong, so it can be corrected.

Without one of these parts you don't get an agent, you get either a chatty chat window or a bot with a rigid script.

What an AI agent can do right now

Handle incoming requests without a human in the loop

A customer writes in WhatsApp, Telegram or by email. The agent reads the message, understands the gist, checks the data in the CRM (in Kazakhstan that is usually amoCRM or Bitrix24), drafts a reply that accounts for the customer's history, and, if needed, creates a task or a deal. If the request is non-standard, it hands it to a live employee with the full conversation. All of this happens in seconds, around the clock, in Russian and Kazakh. The same works on the phone: an AI voice agent answers calls, books the visit and logs the result.

Run the first conversation on templated deals

The agent can run the initial conversation with a prospective customer: clarifying the parameters of the task, answering common questions about the product, and agreeing on basic terms. When the conversation reaches a point that requires a human, it hands it off with full context. The manager steps in not at the "tell me about your product" stage, but at the "let's discuss the details" stage.

Monitor data and send alerts

The agent watches metrics in real time: sales figures, stock levels, orders coming in from Kaspi, user activity, system health. When something deviates from the norm, it immediately notifies the responsible person with a description of the problem and a first take on the causes. It doesn't wait for the morning report.

Work with documents and data

It extracts structured data from unstructured text: contracts, invoices, delivery notes, requests, emails. It checks documents against templates. It fills in fields in your systems from incoming data, for example by preparing a draft document in 1C. It compiles summaries and reports from different sources. Everything an analyst used to do by hand for hours.

Execute a chain of actions

The agent can work through several steps in a row: receive a request, check stock availability, calculate shipping, issue an invoice, send the customer a confirmation, and create a shipping task. Each step talks to a different system, and each result becomes the basis for the next step.

More on how we build these agents: AI agents and assistants for business.

An agent and a chatbot on the same example

Imagine a customer writes: "I want the same thing I got last time, but more of it, and shipped to a different address."

A chatbot can't handle this. It has no access to order history, it doesn't understand "the same thing" without clarification, "more" is an undefined quantity, and "a different address" requires a form.

An AI agent checks the customer's order history in the CRM, identifies the last order, clarifies the quantity ("Do you mean 10 units instead of 5?"), accepts the new address as free text, recalculates shipping, and confirms the order. If anything is unusual, it asks the customer for clarification or hands the conversation to a manager.

This isn't the future. Systems like this are being built and used right now.

What an AI agent can't do, and why it matters to be honest

An agent can't make strategic decisions. It works within the tasks and rules you set for it. Changing a sales strategy, handling complex negotiations with a key partner, resolving a conflict with a major client: that is still work for a human.

An agent also depends on data quality. If your CRM is a mess and your systems aren't connected, the agent will work with what it has, and its capabilities will be limited. Before deploying an AI agent, you often need to put your data and processes in order first.

And most importantly: an agent is a tool, not a magic button. It delivers results where there's a clear task, the data to solve it, and well-defined success criteria. We cover what this looks like in support in AI in customer service.

Data and the law: what to consider in Kazakhstan

An agent works with conversations, customer names and phone numbers, and that is personal data. Three things are worth deciding before launch, not after.

Storage in Kazakhstan. Under the Law of the Republic of Kazakhstan "On Personal Data and Their Protection", personal data is stored in a database located in Kazakhstan (Article 12, paragraph 2). So the request database and the agent's conversation log should live on a server in the country.

Sending data to a model abroad. Large language models run on servers outside Kazakhstan. If customer data goes into a request to the model, that is a cross-border transfer, and it has to be named in the consent to data processing. Good practice: send the model only what the task needs, without extra fields. More in our guide to personal data on a website.

Being upfront with customers. The Law "On Artificial Intelligence" No. 230-VIII of 17 November 2025 requires that users be informed when goods, works and services are produced or provided using AI systems (Article 21, paragraph 1). For an agent this is a simple rule: it introduces itself as an assistant and calls a person in when the customer asks.

Check the details of your case with a lawyer: we build these requirements into the architecture, but we don't replace a legal opinion.

How to get started

The first step isn't choosing a platform or studying the technology. The first step is finding a specific task where manual labor is expensive, the data exists, and the result is measurable.

Good candidates for a first AI project:

• incoming requests: high repeatability, clear outcome;
• work with documents: lots of manual entry, low added value;
• monitoring and alerts: a missed problem is expensive.

Poor candidates to start with: complex negotiations, decisions with many variables, tasks without data or quality criteria.

Start small, measure the result, scale what works. That's more reliable than building a large system on assumptions about what AI will do for your business. If you are not sure which task to pick, AI consulting helps find where an agent pays off and where simple rules or an existing tool will do.