AI Systems Need Method, Execution, and Evaluation

Most AI systems are still stuck in a single-inference pattern: they re-plan every time, discard execution results immediately, and accumulate no experience.

A more sustainable architecture separates concerns into three layers:

• a method layer defining goals and strategy
• an execution layer housing validated, reusable scripts
• an evaluation layer detecting degradation and triggering repair

Reasoning should intervene only when necessary.

The deeper shift is this: in the AI era, the real code is prompts, skills, and method descriptions. Traditional code degrades into a replaceable execution artifact.

The maintenance focus moves from crafting code to clearly expressing objectives, constraints, and success criteria.