What is an AI agent in simple terms?

A program that receives a task, decides which steps to take and carries it through. It has access to company systems, so it reads data and performs actions rather than only answering in text.

How does an AI agent differ from a chatbot?

A chatbot follows a tree of prepared answers and breaks one step outside the script. An agent works towards a goal and picks the order of steps itself, reaching into the CRM, spreadsheets and databases. Asked about availability, a chatbot recites opening hours while an agent checks the booking system.

Which business tasks can an AI agent take over?

The first reply to an inquiry, qualification of leads and resumes, reading documents and correspondence, monitoring prices and reviews, drafting emails and meeting summaries. The general rule: the task repeats often and the decision follows clear signals.

Will an AI agent replace an employee?

It removes the repetitive part of the work. Negotiations, conflicts and decisions with personal accountability stay with people. Klarna showed that measuring the effect in replaced headcount leads to falling quality and bringing people back.

Where is the data the agent works with stored?

Inside your own infrastructure. The engine is deployed on your servers or on a server you control, so the client base, conversations and documents never leave your systems. When the project ends the system stays with you.

AI AGENTS

AI agents for business

An AI agent is a program that receives a task, decides for itself which steps to take, and carries it through. Unlike a chatbot it does more than answer in text: it reads data, reaches into company systems and performs actions.

In practice an agent looks like an employee with one area of responsibility. One handles incoming leads, another screens resumes, a third watches reviews and competitor prices.

We are Pragma, an automation studio in Almaty. We build these agents and deploy them on the client's own infrastructure.

How an agent differs from a chatbot

A chatbot runs on a decision tree. It knows a list of questions and replies with prepared answers. One step outside the script and the conversation breaks.

An agent runs on a goal. You tell it what result you need and give it access to tools: the CRM, a spreadsheet, a database, the mailbox. It works out the order of steps itself.

The difference shows on a simple case. Asked whether Friday is available, a chatbot replies with a canned line about opening hours. An agent checks the booking system and answers from the actual state of it.

Which tasks an agent can take

The rule is simple: hand over work where the decision follows clear signals and repeats many times.

First reply to an inquiry: The agent answers in the messenger, clarifies details, judges urgency and passes the sales rep a lead with the context already gathered.
Qualification and scoring: Scoring a lead or a resume against set criteria, sorting by priority, filtering out the clearly irrelevant.
Reading documents and correspondence: Extracting fields from invoices, contracts and emails, checking them against the database, preparing a summary.
Collecting and interpreting data: Monitoring prices, reviews and mentions, with a weekly read-out that draws conclusions instead of dumping a table.
Drafting replies and documents: A draft email, proposal or meeting summary that a person checks and sends.

Agent, chatbot and rule-based automation side by side

Three things people call by one word, even though they behave differently.

Comparison of a chatbot, rule-based automation and an AI agent by logic, system access and behaviour outside the script
Criterion	Chatbot	Rule-based automation	AI agent
Logic	Tree of prepared answers	Fixed sequence of steps	A goal, with freedom over the steps
Access to company systems	Usually none	Yes, along fixed rules	Yes, reaches in as needed
One step outside the script	Breaks	Stops with an error	Tries another route or hands over to a person
Text and documents	Matches keywords	Template only	Reads meaning, extracts fields, summarises
Predictability	High	Highest	Lower, needs testing on real data
Where it fits	Standard questions and opening hours	Moving data between systems	Qualification, analysis, preparing decisions

What to keep with people

Negotiations where a mistake is expensive. Conflicts. Decisions someone answers for personally.

A public example cuts both ways. In February 2024 Klarna reported that its AI assistant had handled 2.3 million conversations in its first month and was doing the equivalent work of 700 agents. In May 2025 its CEO acknowledged that cost had become too dominant a factor, quality suffered, and the company started bringing people back.

The lesson is about the measure rather than the technology. Counting the effect in replaced headcount is the mistake. Count it in freed-up time and in quality that did not drop.

Why it often fails to pay off

In a McKinsey survey of 1,719 companies nearly nine in ten regularly use AI in at least one function. Only 37% attribute any EBIT impact to it, and about 6% report significant value.

The difference is almost always the same one: a specific process was automated, or a general idea was bought. An agent without a described process is a showcase that demos well and moves no number in the report.

That is why we start with the process rather than with the choice of model.

How implementation runs

Day one: We map the process: where data comes from, who handles it, where time is lost, which decisions follow rules.
Days two to nine: We build the agent, connect systems through official APIs and test on real data from a past period.
Day ten: We run it alongside a person, compare results and adjust on real cases.
First month: Adjustments are part of the project. After that we stay available for support.

Cost and where the data lives

The price depends on the number of channels and integrations, the complexity of the scenario and the volume of data. Ready-made scenarios go live in 5 to 14 days, custom ones in about 10 days with iterations.

Data stays inside your infrastructure. The automation engine is deployed on your servers or on a server you control, so the client base and conversations never leave your systems.

When the project ends, the system stays with you and runs without us.

Ready-made agents

Six agents we have built many times over. Each owns one area of responsibility and has a known deploy time.

Lead Machine: Leads from every channel land in your CRM with AI scoring. Hot ones reach the sales rep in Telegram instantly.
Hire Flow: Job posts go live on hh.kz and LinkedIn, resumes parse in, AI scores them, candidates take a test, and approved ones book interviews on the calendar themselves.
Signal Agent: AI analyst tracks competitors, prices, and mentions across 2GIS, Kaspi, Telegram, and Instagram. A weekly report with insights and recommendations lands in the CEO's Telegram.
Client Flow: Deal closed → client gets onboarded → documents are generated → tasks fly to the team → weekly reports go out → NPS survey on day 30/60/90.
Meeting Flow: Zoom/Meet records → Whisper transcribes → AI extracts tasks → tasks go to your CRM, summary lands in Telegram.
CEO Dashboard: Every Monday at 9 AM, a single Telegram message: leads, revenue, conversion, ad spend — plus an AI take on what matters today.

Frequently asked questions about AI agents

What is an AI agent in simple terms?: A program that receives a task, decides which steps to take and carries it through. It has access to company systems, so it reads data and performs actions rather than only answering in text.
How does an AI agent differ from a chatbot?: A chatbot follows a tree of prepared answers and breaks one step outside the script. An agent works towards a goal and picks the order of steps itself, reaching into the CRM, spreadsheets and databases. Asked about availability, a chatbot recites opening hours while an agent checks the booking system.
Which business tasks can an AI agent take over?: The first reply to an inquiry, qualification of leads and resumes, reading documents and correspondence, monitoring prices and reviews, drafting emails and meeting summaries. The general rule: the task repeats often and the decision follows clear signals.
Will an AI agent replace an employee?: It removes the repetitive part of the work. Negotiations, conflicts and decisions with personal accountability stay with people. Klarna showed that measuring the effect in replaced headcount leads to falling quality and bringing people back.
What does implementing an AI agent cost in Kazakhstan?: It depends on the number of channels and integrations, the complexity of the scenario and the volume of data. Ready-made scenarios go live in 5 to 14 days, custom ones in about 10 days. Scoping and the estimate are free on a 30-minute call.
Where is the data the agent works with stored?: Inside your own infrastructure. The engine is deployed on your servers or on a server you control, so the client base, conversations and documents never leave your systems. When the project ends the system stays with you.

Sources

1.The state of AI in 2026: On the road to ROI · McKinsey, 2026
2.Klarna AI assistant handles two-thirds of customer service chats in its first month · Klarna, пресс-релиз, 2024
3.Klarna reinvests in human talent for customer service · CX Dive, 2025
4.Will AI Fix Work? Work Trend Index · Microsoft, 2023

Let us scope your case

Tell us which process eats the most time. We will say whether an agent fits it, and name the timeline and the price. Scoping is free.

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