Governed decision infrastructure for complex optimization
umamimind separates proposal generation, decision ranking, and governance into explicit layers—so autonomy is explainable, reviewable, and auditable.
Specialized agents generate candidate actions under explicit constraints. They do not execute decisions. This enables autonomy without losing control.
Classical optimization ranks feasible plans with inspectable objectives and constraints. Trade-offs are explicit, results are replayable, and outcomes can be reviewed before execution.
Policy gates enforce what is allowed, block non-compliant actions, and produce evidence trails—constraints applied, scores, agent contributions, and approvals.
Quantum methods can accelerate exploration for large combinatorial search spaces. They are never required for correctness, and the system degrades gracefully to classical-only optimization.