Thank you! That's actually a big part of why I'm writing this series.

My goal isn't to teach people how to build agents using a particular framework or SDK. I want readers to understand the architecture behind agentic AI: why each component exists, how the components interact, what can go wrong, and what trade-offs we make when designing these systems.

I believe that when you understand the underlying concepts, learning a framework becomes much easier because you're no longer just learning APIs; you understand what those APIs are helping you build.

And your point about silent failures, partial results, and stale reads is exactly the kind of thing I want readers to think about. Execution is only one part of the picture. Verifying results, handling failures, and deciding whether the agent has enough evidence to proceed are equally important design considerations.

The goal is to build that mental model first, so readers can reason about these systems regardless of which framework or SDK they eventually use. Thanks for raising this!