What agentic development still requires from you

1) Vision/Goal. AI will never tell you your goal.  Without the goal, how do you evaluate it is a success?

2) The current step. This is what most people set in their prompts.  However, it is better to start with the vision and make the steps to reach it.

3) Clear requirements for each of the steps. If you cannot articulate what success looks like in a way that can be evaluated, no amount of autonomous execution will produce it. Agents cannot clarify requirements they are never given. They will fill the gaps with assumptions, and those assumptions compound.

4) Strong abstractions. An agent given a well-designed system with clear module boundaries, coherent interfaces, and good separation of concerns will produce better results than an agent given a tangled codebase where everything depends on everything else. Clean architecture is more important when agents are doing the implementation, because agents amplify the properties of the system they are working in.

5) Reliable tests. This deserves its own section.

6) Careful tradeoffs. Agents optimize for the stated objective. They do not naturally balance competing concerns, anticipate second-order effects, or flag when a technically correct solution is the wrong product decision. That judgment still lives with you.

7) Human Oversight. Agents do impressive work. They also make confident mistakes. The output quality is high enough to get past casual review, which means the bar for your review skills actually increases, not decreases.