[jbloggs777]

It would be exhausting, if it were always necessary. If you spend the time up-front to properly define goals and requirements in a machine+LLM verifiable way, then the agents can manage it in isolation or with minimal oversight, and present a working result that meets those criteria.

BUT: How many people do you know who can achieve sufficient clarity up front? It is a skill (or set of skills) that needs developing. It can also mean the difference between spending $20 in tokens versus $2000, and/or throwing away the result and starting from scratch again (you don't really want to touch an AI generated codebase with fundamental design flaws if you value your time and sanity)

In the meantime, deliberate checkpoints for human review are still a good idea.

My theory: behind every "10x AI coder" is a long trail of expensive failures that never made the light of day, but which they are learning from. The early adopters will therefore have a competitive advantage.