DecisionBrain: Agentic Solver Orchestration for End-to-End Operations Research

Solving an operations research problem end to end from its natural-language statement is challenging: the requirements are implicit in the statement, solver performance varies across problem classes, and an optimal solution to an incorrect model may violate the requirements. Although language models and agent frameworks have facilitated such workflows, most existing systems lack an explicit representation for a solution strategy designed by the agent, restrict execution to specific solvers, and require the solving stage to verify its own output. We present DecisionBrain, an end-to-end agentic workflow that orchestrates heterogeneous solvers through strategy-level design over a validated method library. It returns a decision only after an independent feasibility review, which routes repairable failures back to the responsible stage. DecisionBrain and every open-source baseline run on the same DeepSeek-V4-Flash backbone. On the stress test suite Hard32-Fea, DecisionBrain returns a feasible decision on 81.3% of instances against 59.4% for the strongest baseline. On FrontierOR65-Fea, where a solver based on one monolithic mathematical optimization model already suffices, DecisionBrain reaches 86.2% against 84.6% for the strongest baseline. Ablations confirm that each mechanism contributes, and removing the algorithm library has the largest effect, 46.9 points on Hard32-Fea.