# How Agent Enhanced Development Work Flows - Local Models

Typical development processes are that you focus on small steps at a time to scaffold out an idea and then focus on the logic flow. This usually means a lot of saving and rapid iteration. This is actually the least productive way to work with an agent enhanced development process.

When you are first starting out using an AI agent to assist with developing your app, the temptation is to continue using it for the quick iterative feedback like we currently get from a “write, save, retry” development process. However, with an AI agent enhancing our development process, we are rewarded for planning in detail, and using our natural language. By that I mean, writing and building feature requests in large batches and letting the AI run over many features.  

The agent should be driving the majority of the development, and you should be able to walk away and do something else. It’s a great way to end a day of planning, and then let your laptop sit for a time while the agent runs. It’ll spend the time it needs until it succeeds at building a thorough feature, including your tests.

This means you can plan your time so that your agent is building your app while you’re typically away from your computer, this way you’re maximizing your local hardware availability while getting the benefits.

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*Chapter 07 of the AED canon · published **verbatim** from the author's original · back to the [reading order](README.md#reading-order)*