AI orchestration still needs an architect.
As code generation accelerates, framing the problem and judging the result become more valuable.
Using AI in development is more than writing a few functions faster. The challenge is turning model capability into a product that works, remains maintainable and solves a business problem.
Frame the problem before dividing the work
Before assigning an agent, define the desired behaviour, system constraints and evaluation criteria. When these are vague, more output simply creates more ambiguity.
Specialisation, review and integration
Specialised agents can work concurrently, but someone still needs to define contracts, detect inconsistencies and make architectural decisions. Building, reviewing and testing serve different purposes; each should challenge the others.
Faster production does not remove architectural responsibility. It makes that responsibility more important.
A product is not the end of a conversation
The work reaches its objective when the deployed version is examined, real user behaviour is understood and operational feedback returns to development. An operator’s mindset connects AI execution to product delivery.