6 May 2026

The interesting question in agentic coding right now is not whether AI can write code. It can, and increasingly it does. The question is what that means for how engineering teams in financial services should be organised and what they should be valued for.

The article frames it as: code is cheap, so human effort should concentrate on testing, documentation, and architecture. That sounds reasonable. In practice, for teams building loan origination platforms or credit decisioning systems, it has sharper implications than it first appears.

Regulatory explainability requirements do not disappear because the implementation was fast. If anything, the accountability burden increases when AI generated the code. Someone still needs to own the design decision that sits behind the feature. Someone still needs to produce documentation that would satisfy an FCA review or survive a Subject Access Request. That work cannot be delegated back to the model.

The architectural judgement piece matters even more in consumer credit than in general software. The choices about where to draw system boundaries, how to handle consent flows, which decisioning logic gets hardcoded versus configured, these are not neutral technical decisions. They carry conduct risk. A model will implement whatever it is asked to implement with very little friction, which is precisely the problem.

So the practical shift for technology leaders is this: your senior engineers need to move upstream. Less time in implementation, more time writing the specifications and constraints that the agent works within. The skill being tested is whether you can define the problem well enough that cheap code generation does not create expensive compliance problems later.

The teams that will struggle are the ones treating agentic coding as a way to reduce headcount at the senior end. That is the wrong direction entirely.