While artificial intelligence can assist with implementation, analysis, and testing, human engineers remain essential for establishing what to build and defining acceptance evidence. Chat interfaces provide a fragile record of architectural choices because model context limits and compaction frequently strip away vital justification details. To resolve this, developers can store durable decisions in reviewable documents like project guides to verify an agent's interpretation prior to implementation. Teams must also balance the operational costs of providing comprehensive documentation against the ongoing resource drain of correcting agent mistakes.
Key points
Human engineers remain strictly responsible for deciding what to build and determining th…: Human engineers remain strictly responsible for deciding what to build and determining the evidence needed to accept it.
Storing durable technical decisions in small project guides helps verify an agent's inter…: Storing durable technical decisions in small project guides helps verify an agent's interpretation before code generation starts.
Verifying code increments against acceptance cases prevents default artificial intelligen…: Verifying code increments against acceptance cases prevents default artificial intelligence assumptions from overriding requirements.
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Software engineering · AI-assisted development
AI Can Write More of the Code. We Still Have to Engineer the System.
Durable decisions, relevant context and verifiable increments: a pragmatic perspective on keeping engineering judgment in AI-assisted development.
When people say AI has transformed software development, I want to know which part they mean. Producing an implementation? Exploring a design? Deciding whether a system solves the right problem?
Those activities are connected, but automating one does not settle the others. My argument is that agents can take on more implementation work, and assist with analysis, design, and testing, while engineers remain responsible for deciding what to build and what evidence is sufficient to accept it.
That changes the work. It does not make the responsibilities disappear.
I am wary of describing this as replacing the person who churns out code. It reduces developers to typing, understates what agents can assist with, and turns task automation into a claim about jobs. Claims about workforce replacement require evidence beyond an impressive demonstration.
The practical question interests me more: how do we use these tools…
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