AI coding agents have rapidly improved in their ability to write software, completing tasks that once took weeks in mere hours. These AI-native systems are built to learn rather than be explicitly designed. They function by establishing a feedback loop where code acts as policy, deployment represents the episode, and bug reports serve as rewards. This approach allows machines to execute the code while the AI handles judgment calls during the coding process. Historically, humans have managed both judgment and execution in coding. Judgment involves making decisions based on nuanced criteria, while execution follows clear, explicit rules. Traditional software excels at executing these rules but lacks the flexibility to handle ambiguous or fuzzy classifications. Current agent architectures often confuse these two roles, employing neural networks for both judgment and execution, which limits their effectiveness. For instance, tools like Stagehand and browser-use rely entirely on neural networks, which lack the determinism and precision necessary for complex business logic. The article highlights problems with relying solely on neural networks for execution. For example, in a medical equipment ordering system, the author illustrates how traditional code can handle rare edge cases with 100% precision, maintaining clarity and auditability. Neural networks, on the other hand, struggle with these tasks due to limited training data and opaque decision-making processes. The author proposes a better architecture where the LLM makes judgments during build time, allowing for the creation of executable code that can be version-controlled and audited, rather than relying on ephemeral runtime decisions. This shift could enhance both the reliability and transparency of software systems.