Locating the Model-Harness Boundary in Interactive Diagnosis

Interactive diagnosis requires an agent to recognize diseases, maintain evidence state, decide next steps, and determine when evidence is sufficient for a diagnosis. Existing LLM-based medical agents typically place this entire sequential control loop inside the model. We study a fundamental architectural question: which responsibilities should remain inside the LLM, and which should be executed by the surrounding harness? We decompose interactive diagnosis into evidence-state maintenance, search strategy, and terminal control, progressively externalizing them to a rule-bound runtime that executes machine-readable diagnostic programs compiled from clinical knowledge. At the fully externalized endpoint, the full harness directly takes over these responsibilities, restricting the LLM primarily to language–symbol grounding and residual reasoning for cases not resolved by the programs. Across three interactive medical benchmarks, the full harness improves diagnostic correctness by 24.7–49.7 percentage points over prompting with the same programs, exposes explicit execution provenance, and narrows performance variation across model scales. These results highlight a critical distinction between merely accessing procedural knowledge and reliably executing it. The effectiveness of an interactive agent depends not just on the underlying model's capability, but on how sequential control is strategically divided between the model and its execution harness during diagnosis.