The need for "complex tasks" should be exceptional enough that you're not building your workflow around them. A good example of such an exception would be kickstarting a port of a project for which you have a great test suite from one language to another. This is rare in most professional settings.

I wholeheartedly disagree with this. For any iteration, Claude should be reading your codebase, reading hundreds of thousands of tokens of (anonymized) production data, asking itself questions about backwards compatibility that goes beyond existing test suites, running scripts and CI to test that backwards compatibility, running a full-stack dev server and Chrome instance to QA that change, across multiple real-world examples.

And if you're building a feature that will call AI at runtime, you'll be iterating on multiple versions of a prompt that will be used at runtime, each of which adds token generation to each round of this.

In practice on anything other than a greenfield project, if you're asking for meaningful features in complex systems, you'll be at that 10 minute mark or more. But you've also meaningfully reduced time-to-review, because it's doing all that QA, and can provide executive summaries of what it finds. So multitasking actually works.