For the past several years, the AI conversation has largely centered on models: which are the most capable, which will dominate and how quickly they will improve.

Instead of throwing agents into an SDLC that wasn’t built with them in mind, we’re trying to do something different: design the development lifecycle around them from the ground up. Below is an example of how we shrank a block of work we take on when we connect to a new exchange. Over the last few months, we've been running an experiment. If you take agentic coding seriously, what does it look like?

We sat down with Pat Cooney, Head of Platform Engineering, to talk about what Platform Engineering means here and where agentic AI fits into the picture. Pat has spent over a decade at Optiver across markets, regions, and roles.

When people talk about developer productivity, they often jump straight to tools: powerful coding agents, faster compilers, smarter automation. These things matter, but they are not the whole story.

In most systems, the database acts as a boundary. You write data into it, and other systems read from it. If you need something more dynamic, like reacting to changes as they happen, you usually introduce something alongside it, whether that is a service layer, a queue, or a stream.

Large language models (LLMs) are getting surprisingly good at learning the basics of trading. Consider that the latest models are able to perform tasks like pricing simple scenarios, reasoning through rules and even outlining basic strategies.