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CLI-Driven Development: Building AI-Friendly iOS and Mac Apps

I’ve been using Claude Code for several months now on many personal projects, and I’ve worked out some practices that work really well for me as an iOS and Mac developer. This CLI-driven development approach has fundamentally changed how I build applications with AI assistance.

The Problem with GUI-Based Development

One of the most powerful aspects of AI coding assistants is their ability to receive feedback and iterate on solutions. Give an AI a goal, and it can refine and improve until it gets there. However, if you’re an iOS or Mac developer, you’ve likely hit a wall: GUI interfaces are opaque to AI systems.

You could set up tooling to capture simulator screenshots, but this approach is slow and error-prone. By the time the AI gets a screenshot, analyzes it, and suggests changes, you’ve lost the rapid iteration cycle that makes AI assistants so valuable in the first place.

The other option is writing comprehensive unit tests. If you’re not doing test-driven development with AI yet, CLI-driven development is a nice stepping stone toward that goal. It has the added flexibility of interacting with real data—somewhat like an end-to-end test. Tests are still important, but this is another tool in your toolbelt for those not ready to go full TDD.

The goal is to give the AI the full context of your running application so it can fully interact with it.

Note: See the caveats section below regarding respecting user privacy and security when giving AI access to application data.


The Solution: CLI-Driven Development

CLI-driven development means architecting your application so that every use case accessible via the UI is also accessible via a command-line interface. The UI and CLI becomes access points to these use cases, rather than containing the business logic itself.

This isn’t a new idea. We’ve been told for years not to put business logic in view controllers or SwiftUI views. When working with AI, this separation becomes critical.

Benefits

  1. Better Architecture: Enforces separation between UI and business logic
  2. Faster Debugging: AI can identify and fix issues more quickly
  3. Faster Feature Development: AI has a way to give itself feedback
  4. Improved Testability: These principles make your app more unit-testable too
  5. Easier Data Migrations: AI can access and transform your real data

Architecture: Three Targets

I’m vastly oversimplifying the architectural requirements for a real application here. Folks will have their choice of patterns, frameworks, and approaches. I’m focusing on the minimal Swift package setup that will allow for this flow to work.

Think of your application as having three distinct targets within a Swift package:

  1. UI Target: Contains your SwiftUI views, view controllers, and UI interactions
  2. CLI Target: Handles command-line input/output
  3. Core Target: Contains all business logic, services, data interactions, and workflows

Both the UI and CLI targets are thin layers that simply pass data to the Core. When a user taps a button, the UI sends data to a service. When you run a CLI command, it does the same thing.

Shared Services in Core

This approach requires discipline you probably want anyway: every new feature or interaction needs a single method that can be called with all necessary parameters, with all the important logic happening outside your views. These shared services live in your Core target.

Here’s a brief example of a service to add a TODO:

Core Target – TodoService.swift:

public class TodoService {
    public func addTodo(listName: String, item: String) throws {
        // All business logic lives here
        let list = try getOrCreateList(name: listName)
        let todo = Todo(title: item, completed: false)
        list.todos.append(todo)
        try persistenceManager.save(list)
    }
}

CLI Target – main.swift:

// Parse command: ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"
let service = TodoService()
try service.addTodo(listName: listName, item: item)
print("Added: \(item)")

Mac App Target – AddTodoView.swift:

struct AddTodoView: View {
    @State private var itemName = ""
    let listName: String
    let service = TodoService()

    var body: some View {
        TextField("Item", text: $itemName)
        Button("Add") {
            try? service.addTodo(listName: listName, item: itemName)
        }
    }
}

Both the CLI and Mac app call the exact same TodoService.addTodo() method. The business logic is shared, and both interfaces benefit from any improvements or fixes.


What Can You Do With This?

Debugging with AI

Let’s say you see an issue when adding a TODO to your Grocery list in the Mac app. Here’s how the conversation might go:

> I see a bug when adding a TODO to my Grocery list. Check it out.

Let me test this using the CLI:

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - Buy milk
  - Buy milk

I see the issue - the TODO is being added twice. Let me check the add-todo implementation...

[AI fixes the duplicate insertion bug]

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - Buy milk

Fixed! The issue was in the data persistence layer where we were saving twice

The AI can use the CLI to reproduce the issue, see what’s happening, and iterate on fixes until it’s working correctly.

Building Features Faster

When building a feature, you can give the AI requirements and have it verify its work. Here’s an example:

> Add a feature to mark TODOs as complete. When I mark a TODO as complete,
> it should show with a checkmark when I list them. Verify it with the CLI
> and make sure it works before you're done.

I'll implement the complete-todo feature. Let me build it and test it:

[AI implements the feature in Core target]
[AI adds CLI command for marking complete]
[AI updates list display to show completion status]

Now let me test it:

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy milk"
Added: Buy milk

$ ./MyAppCLI add-todo --list "Grocery" --item "Buy eggs"
Added: Buy eggs

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - [ ] Buy milk
  - [ ] Buy eggs

$ ./MyAppCLI complete-todo --list "Grocery" --item "Buy milk"
Marked complete: Buy milk

$ ./MyAppCLI list-todos --list "Grocery"
Grocery List:
  - [✓] Buy milk
  - [ ] Buy eggs

Perfect! The feature is working. TODOs now track completion status and
display with checkmarks when completed.

The AI builds the feature and verifies it works before reporting back. In theory, you’ll see the same results in the UI application.

Data Migration and Transformation

During early iterations, you frequently need to migrate data when properties change. With a CLI, you can ask the AI to transform all your proof-of-concept data because it has access to all the APIs necessary to read and write data.

> Migrate my TODO data from the old JSON format to the new format.
> Make a backup first, then verify the results match exactly.

$ ./MyAppCLI export-data --output todos_backup.json
Exported 127 TODOs to todos_backup.json

[AI implements migration from old format to new nested format with IDs]

$ ./MyAppCLI migrate-data --from todos_backup.json --to-new-format
Migrating 127 TODOs...
Migration complete.

$ ./MyAppCLI verify-migration --old todos_backup.json --new current
✓ All 127 TODOs migrated successfully
✓ Data integrity verified

Migration complete. Backup saved at todos_backup.json.

Important Caveats

This is Not a Replacement for Unit Tests

Unit testing leads you toward similar practices of keeping services separate from UI. Following CLI-driven development actually makes your app more testable. I suggest using this approach for both unit testing and CLI-driven development. Don’t focus on CLI-driven development alone.

Security and Privacy Considerations

You must respect your customers’ privacy and security. Be mindful when giving AI access to data and understand your privacy requirements. The CLI should be treated as a development tool with the same security considerations as direct database access.