Treat Domain-Specific Languages as Design Variables in LLM Agents

Large Language Model (LLM) agents increasingly excel at complex tasks, yet a persistent bottleneck emerges at the boundary of rule-based systems. Here, the core challenge is often not comprehending intent, but routing user intent through a prescribed, executable interface. This paper posits that this routing problem is best addressed not by scaling model reasoning alone, but by treating the interface itself—specifically, Domain-Specific Languages (DSLs) and their attached verifiers—as first-class boundary infrastructure. We argue that an effective DSL functions as a cognitive shortcut: by providing a high-abstraction, strongly constrained representation, it can compress the intent-to-action path, prune invalid actions before they are attempted, and mechanistically verify correctness via its harness. Our central contribution is to frame the deliberate selection, design, and evaluation of such language--harness pairings as an under-studied research variable that directly shapes an agent's effective action space, failure modes, and learning signals. We develop this position through a dual lens of lifecycle and domain analyses, and ground it with targeted case studies.