AI and Machine Learning Advances

Enterprise use of large language models creates architectural challenges involving control, traceability, state management, output conformance, and operational governance. This design-science paper introduces the Deterministic– Probabilistic Responsibility Allocation Framework, which distinguishes a model-first architecture from a hybrid code-first architecture by specifying where validation, routing, state management, contextual reasoning, output enforcement, recovery, and telemetry should reside. The framework contributes an explicit responsibility-assignment method, a boundary contract for each model invocation, and lifecycle control points that connect probabilistic generation to deterministic validation and escalation. It is examined through an application programming interface sandbox-generation illustration and a modelagnostic reference procedure covering prompt structure, OpenAPI parsing, schema validation, retry and fallback behavior, and output acceptance criteria. The analysis shows how the framework can structure fault isolation, observable execution paths, and enforceable output contracts without claiming measured improvements in reliability, scalability, latency, or cost. Controlled multi-model benchmarking remains necessary to quantify these properties.