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 agentsGenerative AI answers questions. An AI agent does the work.
Almost every company works with ChatGPT or Claude today: ask a question, draft a text, summarize a document. That is useful – but it is no longer an advantage, and the work itself still waits.
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 run reliably in everyday use, an agent needs more than a good model: access to the right data, connections to your existing programs and a point where a person checks the result. That is exactly the software we build around it.
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.
Support everyday operation
After the first release we keep agent and software reliable: changes are tested, delivered with a clear record, and handed over in an understandable form. New tasks are added step by step once the first one holds up 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 – and 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
What is the difference between ChatGPT and an AI agent?
ChatGPT answers questions; an AI agent completes a task. The agent reads your data, compares, drafts and hands over a result for approval – within fixed permissions and with a log. Generative AI is the engine; the agent is the configured tool that uses it to take over one step of your workflow.
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 starting with what slows your business down, whether that needs an app, an agent, or both, and where custom software would create real value.
Give us a call:
+49 441 3091 9602Or write to us:
hello@certaince.comChat with us on WhatsApp:
https://wa.me/493035056713