If I'm not "demanding enough" from models, it's precisely because they are imperfect. Having them do long, complex tasks autonomously usually comes with more cleanup work afterwards IME.

My usual workflow (not counting throwaway code) is to use agents as virtual pair programmers, which gives me a good balance of quality and velocity.

Each interaction with the agent remains individually comprehensible and makes progress towards one of the following goals:

- Understanding the problem/solution space.

- Implementing a solution.

- Understanding the solution, especially any agent-written parts and interaction with existing code.

- Evaluating the solution (tests, code analysis, what-if scenarios, consider edge cases).

This does not just involve the model generating code, but also explaining or analyzing it as needed.

This process allows me to retain a mental model in lockstep with developing the written code and have some assurance both of its functionality and non-functional requirements. It also fits in nicely with most existing team-level workflows.

But it's also something that I can do with pretty much any of the modern models.