When All You Have Are Decoders, Every Decision Looks Like Generation

Agentic AI systems often misuse generative decoders for simple decision-making tasks like routing, treating them as text generation problems. This approach can be inefficient, requiring sequential token generation, parsing, and validation for what is essentially a classification choice. A more optimal design involves a dedicated "decision layer" that evaluates a finite set of explicit candidates and returns scores, similar to a traditional classifier. This structured decision interface offers a faster, cheaper, and more measurable alternative, allowing expensive generative capacity to be reserved for tasks that genuinely require open-ended generation.