
Everyone is talking about the best new colleague
What happens when the AI agent becomes the most convenient colleague? On the quiet crowding-out of human collaboration, and how we deliberately push back.
Knowledge beats wording. We started out believing that an agent mainly needs good instructions. In fact, almost everything depends on what it knows. When a result misses the mark, the cause is nearly always a gap in the knowledge base, not clumsy wording. That turned our way of working on its head: today we write down knowledge where we used to fine-tune instructions.
An agent is not a tool but an asset. It does not get better because you use it more often. It gets better because someone looks after it. That is an unspectacular insight with considerable consequences for planning: the effort does not stop once the agent is built, it just shifts.
Approval stays with people. We have never questioned this and would not today. It looks like a brake and is the opposite: if you know someone will check at the end, you can use the agent earlier and more boldly.
The speed of the team. We expected the technology to become the bottleneck. It was our capacity to take things in. An agent can produce more in an hour than a person can assess in a day. If you don’t plan for that, you build up pressure nobody asked for.
The quiet crowding-out. Not everyone on a team gets on board at the same pace, and those who hesitate rarely say so out loud. It took us a while to see that holding back is not a lack of interest but, more often than not, an unanswered question.
The baseline. We did not measure how long things took before. That means we still lack a clean comparison for the first phase. It is the one measurement you cannot make up for later. And it is the mistake we pass on most often, so that nobody repeats it.
The next step is not a better version of the same thing. In AIC Group’s software development, we are currently trialling how several agents can work together. Not three agents tossing tasks to each other, but a system with several layers: agents that translate business requirements into stories, implement them and test them. Above them, an orchestration layer that distributes the work. Alongside them, instances that check independently whether our standards are being met. When several tasks are worked on in parallel, individual roles run more than once, each based on the same role description.
People remain a fixed part of the process at two points: defining the business requirements, and checking before anything moves on.
We deliberately say “we are trialling”. This runs internally; it is not a service we offer and not established practice. What is clear after the first few weeks, though: bug fixes and new features are coming out at a pace we have not reached before. And contrary to what we expected, this is not at the expense of quality. The code has become more consistent, not more rushed.
We don’t have reliable figures on this yet. Which brings us back to the last article.
More articles…

What happens when the AI agent becomes the most convenient colleague? On the quiet crowding-out of human collaboration, and how we deliberately push back.

An AI agent is only as fast as the team that can carry it. Why good AI does not solve the speed problem but shifts it, and what that means for working together.

Customers’ decisions are more individual than ever: when, what, through which channel. Why the new MAS is our answer to this, and why everything starts with a central data base.
*Some articles are created using AI.