I’ve been reflecting on a recent experiment building a multi-agent system that generates software from a product specification. What stood out was not whether it could produce code, it obviously can. What stood out was how quickly weak product definition showed up in the output. In the first version, the system produced something functional, but it was clearly built on incomplete assumptions. It moved quickly from partial product definition to confident execution, filling in gaps along the way. For example, it treated actionable, outcome-driving elements as secondary, when they should have been the core of the product experience. Even though this was in the original product definition, without reinforcement the system did not design around it.
The issue was not the code generation, it was upstream product understanding. The system did exactly what we often see teams do, it took unclear inputs and turned them into concrete decisions. That pattern is not new. In engineering organizations, if product definition is not tight, teams still move forward. Execution compounds ambiguity, and the result is not a few defects, it is rework, churn, and ultimately something that misses the customer need.
In the second version, I focused less on improving the agents and more on the initial product interaction. More clarification, clearer assumptions, stronger framing of the customer problem, and sharper boundaries between what was known and what was inferred. The result was markedly improved. Instead of something that required major correction, it produced something an engineering team could take and refine. The shift was from rebuilding to iterating.
What this reinforced for me is that as AI accelerates execution, the bottleneck shifts further toward product understanding and judgment. The system will build what you describe, or what you fail to describe. That is where technical leadership matters, not just in architecture or delivery, but in being deeply involved in product. Understanding the customer, pushing on unclear requirements, and shaping the problem before teams or systems start building.
AI does not remove that responsibility, it makes the cost of vague thinking show up faster. The better the AI gets, the less room there is for weak product definition, and the more important it is to have technical leaders who can operate at the boundary between product, engineering, and customer value.