An AI-native company: our agents know what our team knows

TL;DR: At VivaDrive, people and AI agents work from one shared, approved source of company knowledge. Agents do not only read it - they propose what they learn. Every area has an owner who reviews each change, every change is traceable and can be rolled back. AI speeds things up, organises and remembers. A person decides.

Not the number of tools

It is easy to call a company AI-native because it uses a dozen AI tools. We think that misses the point. Tools without shared context are just faster ways of guessing - from disconnected files, old e-mails and whatever happens to sit in someone's head.

For us, an AI-native company is defined by two things: agents know what the team knows, and someone is accountable for what the agents know.

One source of truth for people and agents

Every product and every system at VivaDrive has its own documented source of truth: how the infrastructure works, how we deploy, how we run operations for clients, how we sell and how we communicate. Before an agent acts, it reads the relevant part. A new person on the team reads exactly the same thing.

Live data stays where it belongs. Sales data lives in our CRM, which agents read directly. Morning reviews of priorities, tenders and funding opportunities are prepared by AI before the working day starts.

Agents write back what they learn

This is the part that changes the most. When an agent runs into a pitfall during a deployment - a missing path, an unexpected dependency, a step that only works in a certain order - it does not just work around it. It proposes an update to the knowledge base. The same problem does not surprise us a second time. Neither a person, nor a machine.

Code works the same way. Our bug-fixing agent prepares a fix as a proposed change, which a person reviews and approves.

We treat knowledge like code

Company knowledge is part of the production system - so it gets the same discipline as production code:

• Area owners. Infrastructure, deployments, AI governance, fleet operations, sales and communication - each area has one owner responsible for keeping it accurate.
• Proposed changes. Nothing lands in the approved version directly. Every change - from a person or an agent - is a proposal the owner accepts, edits or rejects.
• Changelog. Every change records what changed, why, and where the information came from.
• Rollback. If something goes wrong, we can trace it and undo it.

The approved version is distributed to everyone as one package. People and agents always work from the same, current knowledge - not from local copies.

There is always a person on the other side

This is the principle we care about most. AI speeds up, organises and remembers. Decisions - commercial, technical, about clients - are made and signed by a specific person. That is not a limitation of the model. It is how we want to run a company.

From our company to our products

We apply the same principles in what we build for clients: shared, approved context in fleet management, and evidence instead of promises in AI governance. We built this way of working for ourselves first. Now we bring it to our clients.

Who in your company can change the "official" knowledge - and how do you know it is current? If you are asking yourselves the same question, let's talk.