The agent that acts, not just answers
It takes a request written in plain language and carries it through in your systems, stopping to ask for confirmation wherever the stakes are high
What it solves
An email arrives, a ticket, a handwritten request. Someone reads it, works out what's needed, opens the CRM, copies the data into the management system, updates the ERP, replies. The same loop, dozens of times a day. Hours of skilled people's time spent moving information from one system to another, with a chance to get something wrong at every manual step.
Chatbots and AI assistants leave this part untouched. They live outside the systems: they answer, suggest, point to the right procedure. Then opening the applications and actually changing the data lands back on someone's desk. For teams handling plenty of repetitive requests across CRM, ERP and management software, working out what to do was never the bottleneck. Doing it was.
An operational AI agent reads the request in plain language and carries out the actions inside the systems you already use. Faced with a sensitive operation, it stops and asks for confirmation; on everything else, it goes ahead on its own; every operation leaves a trace. The hard part of our job is giving it tight boundaries and making it observable, because you trust an agent once you can keep it in check.
How it works
It reads the request as it arrives
An email or a ticket written the way a person actually talks. The agent works out what's really needed: who the customer is, what action is being asked for, which system it belongs in. No form to fill in, no keywords to learn.
It knows where to stop
We write the boundaries of what it can do on its own together with you before we build it; it doesn't improvise them at runtime. A contract change, data going out to a customer, an irreversible write to the ERP: here it shows the operation it's about to carry out and waits for the go-ahead from the person accountable for that step.
It acts and logs every operation
It opens the record, changes the status, fills in the fields, sends the reply, with the same permissions a person would have. The log keeps what it did, when, on which request, and the reverse operation to undo it.
Why it stays under control
- It lives inside the systems you already have and follows the same permissions and rules as people do, without opening a parallel channel that bypasses them to move faster
- What it handles on its own is decided up front: irreversible operations only go ahead with the go-ahead from the person responsible for that step
- That same log is also your control lever: who asked for what, what the agent did, and the operation that reverses a result that went wrong
What changes
- People stop copying data from one system to another and go back to the parts of the job where their judgement is actually needed
- Requests move through on more consistent timelines and come with fewer transcription errors, because the manual step that caused them is gone
- At any point you know what the agent handled on its own and what went through a person, without having to reconstruct it after the fact
What we don't promise
- Sensitive operations stay under human approval by design: it isn't a temporary limitation we'll remove in a future version
- We won't hand you a demo that looks good on screen and then falls apart in production; when a use case can't support the controls it needs, we find that out during the Inception and tell you before writing any code
- The agent doesn't understand just any request: what it can handle is written down clearly, and beyond those boundaries the request goes back to a person instead of being forced through
Frequently asked questions
What happens if the agent gets an action wrong
Operations that could cause harm go through a person before they go ahead, and the agent never decides irreversible ones on its own. If something does go wrong, the log shows what happened, on which request, and where to pick up from.
How is this different from a chatbot or an AI assistant
A chatbot stops at the conversation: it answers and points to the procedure. An operational agent goes into your systems and carries it out for you, under the rules and permissions that already apply. The value isn't in how it talks, but in the operations it actually completes.
Do we need to change our systems to use it
No. The agent works inside the CRM, ERP and management software you already have, through their APIs or integrations. It doesn't ask you to rebuild your stack: it builds on what's there and respects its permissions and rules.
How do we find out if this makes sense for us before committing
With the Inception package. In two or three weeks we look at your actual flow and work out whether it holds up, where the guardrails are needed, and what's genuinely worth handing to the agent. At that point you decide with real evidence, not on a promise.
At a glance
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We start with an Inception package to see, on your actual case, whether and where it makes sense.
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