AI agents for business
An agent differs from a chatbot in that it does not only answer but acts: it opens a deal, prepares a draft document, updates the data, starts the next step of a process. And that is exactly why its boundaries matter more than its capabilities.
The word “agent” is used for everything these days, so let us agree on what it means. We call an agent a system that receives a task, decides for itself what actions it needs and carries them out in your systems. That is noticeably more useful than an ordinary bot and noticeably riskier: an agent’s mistake lands straight in the CRM, in a document or in a letter to a customer. That is why half of the work in such projects is limits, checks and a clear way to roll back.
What an agent does in practice
- Sorts an incoming request and records it in the CRM with a category and an owner
- Collects data from several systems to prepare a reply or a calculation
- Prepares drafts: a letter, a commercial proposal, a task description — for a person to check
- Updates statuses and fields in your systems based on the outcome of a conversation
- Keeps track of deadlines and sends a reminder when something has stalled
- Stops and calls a person when it is not confident enough
How the agent works
Goal, plan, tools and boundaries — in that order.
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01
It receives a task
A customer request, a new application, an event in a system — the agent starts from what happened, not from someone pressing a button.
started by an event -
02
It gathers the context
It reads the customer’s history, the deal record, stock levels, terms and policies — everything it needs for the decision to be well-founded.
data from your systems -
03
It plans the steps
It breaks the task into actions and chooses the tools: create a deal, send a letter, update a field, call a person.
a plan, not a single answer -
04
It acts in your systems
It carries out the steps through your systems’ APIs and checks the result of each one. What did not work is not hushed up but escalated.
real actions -
05
It reports back
At the end there is a trail: what was done, on what basis and where a person stepped in. Without it an agent can be neither checked nor improved.
a transparent trail
How an agent differs from a targeted neural network
If the task is solved by one answer, an agent is an excessive and more expensive construction.
Targeted neural network
Input → processing → result.
- One function and one result
- A person applies the decision
- Changes nothing in your systems
- Cheaper and faster to roll out
- Good where the flow is uniform
AI agent
Goal → plan → actions up to the result.
- Takes the process to the end on its own
- Uses the CRM, email, databases and external APIs
- Makes a decision at every step and knows when to stop
- Needs permissions, boundaries and an action log
- Good where there are many connected steps
What it looks like in real projects
Our products in which the model does not just answer but works.
What the business gets
Time saved. Not one step leaves the process but the whole chain of transfers between systems — together with the waiting between the steps
Lower costs. The routine part of the work gets done without people, and people stay where a decision is needed rather than execution
Processing speed. A request is handled the moment it arrives, not when someone gets round to it: at night, at the weekend, at the peak
Nothing gets lost. Every step has a trail and an owner — you can see where the process stopped and why
How we limit the agent
An agent without boundaries becomes a source of unpleasant surprises.
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01
A list of permitted actions. The agent does only what it is explicitly allowed to do, and does not work out new capabilities for itself
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02
Confirmation for anything irreversible. Sending something to a customer, changing an amount, deleting data — only through a person
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03
A full log. Every action is recorded: you can see what it did, on what basis and when
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04
A clear rollback. Any automatic change can be undone, and this is checked before launch
What it runs on
We are not tied to one vendor — and that is part of the savings.
We choose the specific model for the task and the budget: not everything needs the most expensive one. If the data must not leave your premises, we deploy an open model on your own infrastructure or in a cloud region you choose.
Integrations
Not a closed list: if your system has an API, we can most likely connect to it too.
Tell us what needs to be done
We reply within one working day. Scoping and the estimate are free.