Scaling Intelligence

At CBK.AI we can now deploy AI agents the way we deploy infrastructure, i.e declaratively. It started as a practical solution first. We know infrastructure as code, it works, and almost nobody else is applying it to agents. Useful, and a bit of an edge.

The bigger realization for us came later. To write an agent as code, you have to know its shape. And it turns out we do now, in a way we simply did not a year ago. An agent needs instructions. It needs skills and a set of tools. It needs a workspace to operate in and a mission to pursue. It needs metrics on its own performance and a feedback loop so it can correct course. That list used to be fuzzy. Today it is crystal clear.

Once the shape is settled, deployment at scale stops being the interesting question. Serving an agent to users is solved. The real question is whether the intelligence does anything useful. That is the scale problem. Is this agent worth keeping?

You no longer hand-wire the systems behind an agent, so you can run survival of the fittest on the agents themselves. An agent begins as an idea, possibly a bad one. You prescribe and deploy it. Then you watch what it does, record, and calculate its ROI. After a week it goes through a performance review. Did it do anything useful? If not, deprecate it, replace it, or change it. If it did, let it run longer and check again.

Now do that for hundreds of agents at once. That part is easy today. You arrive quickly at a clear picture of which agents earn their keep and which do not. That is what scale looks like to me. Computational throughput is a separate problem. Ordinary software hits scale when more people start using it. Intelligence scales along a different axis.

Scaling intelligence is a search problem if you think about it. We are looking for the ways AI can do something genuinely useful, and the honest starting point is that we do not know yet until we try. The faster we iterate, the faster we find out, and the faster we start reaping the benefits.

This is what we do at CBK now, though we began slowly, building these systems by hand. That still has merit. With the latest Terraform provider we can push it hard. We can now spin up many systems at once, deploy hundreds of agents across hundreds of settings with all kinds of goals, all generated automatically by coding agents whose job is to design AI agents. Then we assess, and decide what to try next.

It was never about just chat.