Modern agentic workflows decompose user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window. The prevailing route to improving such reasoning is test-time scaling, which trains models to search over long chains of thought; but the resulting capability is entangled in model weights, cannot be checked step by step, and is costly at inference. We present Forethought, a neurosymbolic reasoning system built from a library of symbolic and neural primitives composed through a typed domain-specific language. The results are reasoning programs: concrete representations of the model's work, in which every step carries an output contract that is checked at design time and enforced at execution time. Symbolic steps hold by construction, while learned steps are contract-checked and empirically validated, giving an explicit boundary between what is guaranteed and what is measured. Instantiated as a tool-calling execution kernel and evaluated on four standard tool-calling benchmark suites, Forethought improves end-to-end accuracy by a median of 32% relative to prompting the same base model directly, and reaches the highest aggregate accuracy among the evaluated configurations, though only by a margin within single-run noise over a frontier model. Systems pairing small open models with Forethought's fixed small-model layer match that frontier model, and a pipeline with a non-reasoning responder outperforms its reasoning sibling while requiring only a single fine-tuning stage. Removing the contract-and-control layer erases most of these gains, showing that checked structure, not decomposition alone, drives the improvement. Our results show the potential of rich symbolic orchestrators for building language-model agents that are more accurate, reliable, and auditable.

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