When building architecture for an AI agent, as with the game of Chess, the real skill isn't just reacting to the board state right in front of you, it's anticipating how the downstream system will react two or three moves later. Consider the following:

1. Anticipating how the LLM will interpret a prompt before sending it.
2. Anticipating how a human will interact with a LLM on the application surface.
3. Anticipating how an agent governance contract will hold up when LLM assistants rotate.

Seeing those downstream moves ahead of time cuts through unnecessary clutter and lands on solutions that are simple, elegant, and actually work.