AI Agent for Business: How It Differs from a Chatbot

A chatbot answers. An agent does. Give it a task — read today's invoices, check them against the orders, flag the discrepancies — and it works through the steps itself and reports back with a result you can check.

What is an AI agent, in plain terms?

A programme that receives a goal rather than a command, and works out the sequence of steps needed to reach it. It can read a document, look something up in your system, compare two figures, write a reply and record the outcome — in whatever order the task requires.

The important part is that nobody scripts every branch in advance. You describe the goal, the rules and the limits. The agent decides how to get there and stops for a human where you told it to stop.

How does an AI agent differ from a chatbot?

A chatbot is a conversation. It waits for a message, produces a reply, and the exchange ends. Its output is text.

An agent is a job of work. It starts on a schedule or an event, touches several systems, and its output is a change in the real world: a record created, a document filled in, an email sent, a discrepancy escalated.

Put simply: a chatbot tells your client what the delivery date is. An agent reads the order, checks stock, books the courier and writes the date into the system — then tells the client.

Which jobs does an AI agent take over from a small business today?

• Handling the inbox. Sorting incoming mail, pulling out the substance, creating records, drafting replies for a person to approve.
• Checking documents. Reconciling invoices against orders, contracts against the agreed terms, statements against expectations — and reporting only the mismatches.
• Keeping data in order. Filling gaps in records, removing duplicates, chasing missing details.
• Monitoring. Watching prices, stock levels, competitors or mentions, and raising an alert only when something crosses a threshold you set.
• Routine written work. Standard offers, specifications, summaries of long correspondence.

What these share: the rules can be written down, the work repeats, and the output can be checked against how a person did it.

How much does an AI agent cost, and what sets the price?

Guide figures for the Moscow market, in roubles:

• A single narrow agent — ₽120,000 to ₽300,000. One task, one or two data sources, thirty days.
• An agent across several systems — from ₽350,000. Reads from one place, writes to another, with reconciliation in between.
• A chain of agents — from ₽600,000, where one hands work to the next.
• Running costs — from ₽3,000 to ₽15,000 a month for the language model, depending on volume, plus support from ₽20,000.

Three things move the price: how many systems it must connect to, how precise the output has to be, and how much of the decision stays with the agent rather than a person. Demanding near-perfect accuracy on an unusual task is what makes a project expensive — not the technology itself.

How do you tell a working agent from one that only looks busy?

Ask for numbers, not a demonstration. A working agent comes with a test report: how many real cases it was run on, how many results matched what a person produced, and what the disagreements were about.

Three questions that settle it quickly. What was it tested on — your data or the supplier's example? What happens when it is unsure — does it guess or escalate? And what does the log show — can you open any single case and see what it did and why?

If the answer to all three is a slide deck, there is nothing there yet.

What do you need before starting?

Less than people expect. The rules of the process written down in ordinary language, historical data for the last few months to test against, and access to the systems involved. No new software, no change of CRM, no data science team.

What genuinely blocks a start: rules that only exist in one person's head, and no history to check the result against. Both are worth fixing regardless of whether you automate anything.

Where should you start?

With one task that repeats daily and costs a person real hours. Measure how long it takes now, state what the result should look like, and test on data from a period that has already been and gone. That is a short piece of work, and it tells you whether an agent is worth building before any significant money is spent.