The word “agent” is now used for almost everything: a chatbot, a form with one prompt, a three-button automation and a digital employee who, according to the sales deck, will run the department by Tuesday.
Let us leave the digital director alone. An AI agent is a system that receives a concrete job rather than just a question. It works out what needs to happen, chooses the next step, uses the available data and tools, checks the result and either continues or hands the task back to a person.
How does an AI agent differ from ordinary chat?
Imagine a chef. The language model is the chef: it can understand the request, read a recipe and decide what to do next. Ordinary chat is a chef standing at an empty table. Every time, you bring the ingredients, hand over the knife, explain the recipe and say what to do after each step.
An AI agent is the same chef in an equipped kitchen. There are ingredients and prepared components — your data and files; knives, ovens and fridges — the available programs; recipes — skills and instructions; hygiene rules — limits and checks. You name the dish, and the chef understands which tools to use and in what order.
The chef may stay the same while the kitchens change. In one, it prepares advertising reports; in another, presentations; in a third, it sorts email. Same model. Different task, data, tools and rules.
Answers.
Then waits.
You ask → receive text → decide and perform the next action yourself.
Acts.
Then checks.
Receives a job → chooses tools → completes several steps → checks the result.
Chat helps you think.An agent can take over part of a process. Preferably not the entire business at once 🙂
What can an AI agent actually do?
An agent does not possess secret company knowledge and cannot telepathically enter your CRM. Its capability comes from four things: the model, instructions, available data and tools.
- Collect information. Find what it needs in your work chats, such as Slack, as well as email, documents, tables, CRM, a local folder or cloud storage — only where you have granted access.
- Choose the next step. Decide what an email is about, which template fits, who should receive a lead or which information is missing.
- Create the result. Prepare a report, deck, client reply, brief, table, web page or task list.
- Change something, not merely answer. Write to a table, edit a file, save a document, create an email draft, assign a task or update a client status — if permission exists.
- Check its own work for nonsense. This may be the most important part. A skill or agent is not merely a very long recurring prompt. It needs tests: compare with an example, find missing pieces, step back, correct the mistake and try again. If that fails, it should call a human instead of confidently announcing success.
What does this look like in practice?
My morning starts with a traffic summary
I am Head of Growth and CRO for a gaming portal with more than one million monthly users. We buy traffic across many channels. In the morning I ask our corporate agent for yesterday’s summary. It brings spend, visits, registrations and other indicators into one place, compares them and points out where the numbers disagree. Then I ask the human questions: why did this become more expensive, where did the growth come from and which system is reporting something different?
Process incoming leads without losing them
Suppose an email says: “We need AI training for a team of twenty and want to start in October.” The agent extracts the task, team size and date, notices that budget and format are missing, and drafts a reply with two questions. It creates the CRM card and reminder. The email leaves only after a person approves it.
Build a deck you are not embarrassed to open
You give the agent a voice note, a spreadsheet, three old decks and the brand rules. It structures the story, lays it out in your colours and type, checks for overflow and returns an editable PowerPoint. If evidence is missing, it leaves a question instead of inventing a number. Read more in “How do you keep an AI-generated presentation on brand?”
Turn tickets and bookings into a finished budget
A company regularly receives tickets, hotel bookings, receipts and travel records. Instead of copying everything into Excel, the agent reads the approved mailbox or shared folder, finds dates, routes and amounts, enters them into the correct rows and recalculates the budget. Missing and duplicate documents are flagged separately.
Who actually needs an AI agent?
A good candidate is recurring work where the input changes slightly each time, several steps are required, two or three sources must be checked and clear rules can be applied.
Examples: prepare a weekly report; classify and route every incoming request; turn an interview into an article, cards and email; check documents against a checklist; draft a proposal from client materials.
If a task happens once, is solved by one question or follows completely rigid rules, an agent may be excessive. Sometimes you need a good prompt. Sometimes a normal automation. Not every spreadsheet needs to become an elaborate AI agent. Do not use a microscope to hammer nails.
QUICK TEST
Does it repeat, change and require judgement?
If a process has all three qualities, it is worth considering as an agent task.
How difficult is it to build one?
That depends less on the word “agent” than on what it is allowed to do.
- Simple. Instructions, your files and one clear output. The agent changes nothing outside its workspace. This can begin without development.
- Operational. Email, documents, tables, CRM or other tools are connected. Permissions, checks and approval points matter.
- Production. The agent works with an important process or company data. Now you need action logs, tests, cost control, error handling, security and a responsible owner.
The sensible path is one narrow process, minimum permissions, observation of real errors and only then expansion. A regular report or on-brand presentations is a much healthier start than a many-armed command centre.
What should you define before you build?
- Result. What completed work looks like.
- Input. What the agent receives and which sources are trusted.
- Tools. What it may read, where it may draft and where it may act.
- Limits. What is forbidden and how many attempts are allowed.
- Approval points. Where a human must take over.
- Quality check. Which examples and criteria define “good enough”.
Can I simply ask: “build me an agent”?
You can. The catch is that no matter how capable AI becomes, working with it still requires us to structure our own thinking and business processes. If the process is chaos, the first thing automation will scale is the chaos.
A polished demo on one perfect example is easy. Real work begins with less glamorous questions: where does the data come from, what counts as an error, who may make the decision and what happens when the required file is missing?
So I do not begin with the fashionable platform. We take the task, break it into steps, remove what is unnecessary and define the boundaries. Only then do we decide whether it needs an agent, a skill, an automation or simply a properly designed template.