Guide · Last updated July 31, 2026
AI Agents for Business: What They Actually Do
An AI agent for business is software that runs a real workflow end to end: it reads the input, decides what to do, calls the tools and systems involved, and produces the result, with humans only handling the exceptions. The workflows worth automating first are the repetitive, rules-heavy, high-volume ones that eat your team's day. The catch is reliability: a built agent keeps running where a chatbot demo breaks.
What an AI agent does that a chatbot does not
A chatbot answers questions. An agent takes action: it connects to your tools (email, CRM, Jira, internal APIs, spreadsheets), follows multi-step logic, and completes the task, not just talks about it. That is the difference between a helpdesk widget and something that actually clears work off your team's plate.
What it actually does, in plain terms
Forget the category names. Here are the jobs an agent or an automation does inside a company, in the words you would use yourself. If you recognise one of these as something a person in your company does by hand every week, that is the one to start with.
Sales and offers
- Writes the offer from the client's request, in your format and your prices
- Builds the effort estimate for a project, task by task, and turns it into a price
- Answers a tender or RFP: takes each requirement and writes the matching answer
- Finds companies that fit your customer profile and ranks them by how likely they are to buy
- Follows up on the quotes nobody answered, on schedule, without anyone remembering to
Website and catalogue
- Puts the products on your site from the supplier file, with pictures, descriptions and prices
- Updates the prices across the whole catalogue when the supplier list changes
- Writes the product descriptions and the structured data, so the products can be found in search and by AI assistants
- Publishes the posts and the newsletter on a schedule, and checks afterwards that they actually went out
Operations and tickets
- Turns incoming email into tickets, assigns them and chases the ones that went quiet
- Keeps the tracker honest: what is really done, what is stuck, who is waiting on whom
- Builds the delivery plan with dates, and updates it when something slips
- Fills the timesheet from what actually happened, instead of from memory at month end
Money and paperwork
- Issues the invoice and files it with the tax authority, and retries on its own when the state system is down
- Reads incoming invoices and PDFs and puts the data where it belongs, without anyone typing it
- Checks the bank statement line by line against the invoices and shows you what does not match
- Produces contracts, annexes and certificates from your templates, with the right data filled in
- Prepares the monthly pack for the accountant, complete, on the same day every month
Inbox and customers
- Sorts the inbox by what actually needs you today, and drafts the repetitive replies
- Answers the questions that repeat, and hands the rest to a person with the context attached
- Watches for a customer reply and tells you the moment it lands
Watching over things
- Notices that a good customer has not ordered in sixty days, and says so
- Watches a system that is supposed to keep running and repairs it, or wakes someone up
- On Monday morning, gives you the conclusion, not the raw numbers
We are not describing this from a brochure. We run our own company on these: our invoices are filed with the Romanian tax authority by an agent, our bank statement is reconciled against them automatically, our contracts and certificates are generated from templates, our inbox is triaged three times a day, and more than twenty automations run on a schedule without anyone starting them.
One example, all the way through: triaging incoming tickets
Ticket triage is the job most companies recognise instantly, so here it is end to end: what goes in, what the agent decides on its own, what still reaches a person, how we prove it works before it touches anything real, how long it takes and what it costs. Same example explained twice, once in plain terms and once for your engineers.
In plain terms
- What goes in: everything that lands in the shared mailbox or the ticket system. Customers, suppliers, the contact form, all of it.
- What the agent does: reads each one, works out what it is about and how urgent it is, pulls out the details that matter (order number, client, invoice), sends it to the right person or queue, and writes a draft reply for the ones that repeat.
- What still reaches a person: complaints, anything involving money, and anything the agent is not confident about. It hands those over with the context already attached, so the person starts from a summary instead of from scratch.
- How we prove it before you trust it: we run it in parallel on your real tickets for a week without letting it act, then compare its decisions against what your team actually did. You see the disagreement rate before anything goes live.
- What you need to give us: access to the mailbox or ticket system, and roughly a hundred past tickets so we can measure it on your reality rather than on ours.
- How long and how much: one to two weeks at 700 to 1,500 EUR if it stays inside one system. From 3,500 EUR and three weeks if it has to reach into your CRM or ERP as well.
For your engineers
- Classification runs against a labelled evaluation set built from your own historical tickets, so accuracy is a number we report, not an adjective.
- A confidence threshold splits auto-routed from human-reviewed. You choose where the line sits, and it is a config value, not a rewrite.
- Deduplication on message id, so a resent email or a webhook retry does not open a second ticket.
- Retries with backoff on the ticket-system API, and a circuit breaker so a provider outage queues work instead of losing it.
- Every decision written to an audit log: what came in, what it classified, what confidence, what it did. That log is what makes a wrong call debuggable instead of mysterious.
- Webhook in, API out. No scraping of a web interface that breaks on the next redesign.
What it actually costs to keep running
The build is a one-off. After that, running an agent has two lines: the platform and model usage, which you pay directly to the provider with no markup from us, and optional support from us at 290 EUR a month. For a single workflow the usage line is usually the smallest number in the whole picture, and we calculate it on your real volume during the diagnostic rather than quoting you a guess.
This is the part most people get wrong in both directions. Some assume an AI agent carries an enormous monthly bill, which for one well-scoped workflow it usually does not. Others assume it is free forever, which ignores that models change, third-party APIs change, and something has to notice when a process quietly stops working. We publish the usage costs separately precisely so you can see which is which, instead of finding a padded number buried in a monthly invoice.
Agent, automation, or chatbot: which one do you need?
An automation does the steps you wrote down, exactly the same way every time. An agent is told the result you want and picks its own steps, including when something unexpected shows up. A chatbot only answers questions. Cheapest first: if the steps never change, you want an automation, not an agent.
- Can you write down every step and it never changes? Automation. Cheaper and safer. Take that one.
- Is every case different, and there is always an "it depends"? Agent.
- Do you only want questions answered, not work done? Chatbot. Cheaper still, and we will say so.
We would rather sell you the cheaper thing that fits than the expensive thing that does not. If your job is a spreadsheet formula and a scheduled email, we will tell you that instead of quoting an agent.
Which workflows to automate first
Start with workflows that are repetitive, rules-based, high-volume, and currently done by hand: processing incoming documents or invoices, triaging and routing email or tickets, extracting and entering data between systems, drafting routine responses, and monitoring something that needs a human only when it goes wrong.
- Document and invoice processing
- Email and ticket triage and routing
- Data extraction and entry between systems
- Drafting routine, structured responses
- Monitoring and alerting with human-in-the-loop on exceptions
Why most AI automations fail, and what to demand
Most AI automations work in the demo and break the week after, because the demo never covers the API timeout, the bad model output, or the unexpected input. A production agent has error handling, retries, evaluations that measure correctness, audit logs, and tests. Demand those, not a slick recording.
This reliability layer is the entire reason an automation survives real use. We build it in by default and run our own company on a 200-plus component agent stack, so what we ship for you is what we already trust.
How a build works
We start with a short paid discovery to scope the workflow and what done looks like, then build it for a fixed price with milestones, usually over a few weeks, and hand over code you own with documentation. No open-ended hours.
On price: there is no fixed menu. Small scoped automations can start from a few hundred euro; a full production agent build typically starts around 10,000 EUR. These are examples that depend on scope, fixed in your quote after discovery.
Tell us the task that eats your team's day
We will tell you honestly whether it is worth automating, and scope the build if it is.