Deploy AI agents that take over whole tasks
We set up AI agents for the tasks where every case looks different. The agent works within fixed boundaries: with the permissions, log, and review steps we define with you beforehand. Approval stays with your people. And we build the software an agent needs in everyday use.
Use AI agentsFrom an AI answer to a completed task
A single AI answer can help with a question or a draft. Completing a whole task means connecting the necessary steps: reading data, checking it against instructions and handing over a result for review. What matters is the workflow that has been set up, not the product name.
An AI agent takes over the task: it reads the request, checks it against your data, marks what is missing and hands over a finished result for approval. It works within fixed boundaries: with the permissions, logs and review steps we define with you beforehand. Its value shows where the rule cannot be written down in advance, because every case looks different.
For that to work reliably in everyday use, an agent needs appropriate data access, connections to your programs and a point where a person checks the result. We first check what your existing tools already provide. We build missing connections or custom software where the workflow needs them.
What an agent deployment covers
An agent is not built as a product. It is built around one task from your business, in four steps, in this order.
Find the first task
You show us real examples from your business. We assess which task an agent can take over and recommend a starting point, even when that means an existing tool is enough. Tasks that suit an agent take several steps and cost reading, comparing, and compiling today.
Set the agent up with boundaries
The agent receives fixed permissions: what it may access, what it may do on its own, and where it has to hand over a result. Every step is logged, so it stays traceable later where a figure came from. Approval is given by a named person.
Build the software around it
An agent needs connections to your programs, clean access to the right data, and an interface where someone checks the result. We build that environment: from single interfaces up to a business app of your own when your workflow has rules of its own. You own the code.
Agree ongoing support
Ongoing operation is agreed separately as monthly support; it is not included in implementation. Under that agreement we test changes, deliver them with a clear record and explain what has changed to your team. New tasks follow once the first works reliably in everyday use.
Not every task needs an agent
We choose the smallest dependable approach. Where an agent is the wrong one, we say so in the intro call and not after the invoice.
The rule fits in three sentences
If the rule for the normal case fits in three sentences, a fixed automation is cheaper and easier to verify. It returns the same result for the same input every time. An agent cannot promise that, and for such a task it is not needed.
An existing tool is enough
Claude, ChatGPT, or an application you already use solves the task as soon as it gets the right context. In that case we set up that working method instead of building something new.
The necessary information is not available yet
An agent works with what it can read. When the decisive details sit in people's heads, in email threads, or on paper, the first step is to make them available, not to put an agent on top of them.
How you can check our work
The best evidence for an agent is an agent that runs. At MAFU-SHERPA an AI sales assistant reads the websites of potential customers, scores the fit against the actual buying signals: machine park, lot sizes, processes. Then it drafts the outreach. The salesperson reviews and sends. The DATEV export at the same client shows the other side: there the rule is settled, so the task runs as a fixed automation and not as an agent.
You talk directly to the developer who is responsible for your solution. You do not need a requirements document for that. One real example from your business is enough to begin.
Book an intro callFAQ about AI agents
Is an existing AI tool enough, or do I need a custom agent?
An existing tool may be enough if it can complete the task with the right data and permissions. A custom agent makes sense when your shared workflow needs connections or rules that cannot be set up reliably in that tool. What matters is how the task is carried out and how the result is checked. The product name alone tells you little about that.
When does an AI agent pay off, and when not?
An agent pays off when the rule cannot be written down in advance, because every case looks different: reading requests, reconciling data, preparing drafts. If you can write the rule for the normal case in three sentences, a fixed automation is cheaper and easier to verify, and we will tell you so.
Does an AI agent need its own software?
Often yes, but never for its own sake. An agent needs access to the right data, connections to your programs and a point where a person checks the result. We build that environment alongside the agent: from single interfaces up to custom software when your workflow has rules of its own.
Talk to us about your workflow
We like to start with the back-office work that slows your business down, which of those tasks an AI agent can take over, and which software it needs for that.
Give us a call:
+49 30 3505 6713Or write to us:
hello@certaince.comChat with us on WhatsApp:
https://wa.me/493035056713