Operational decisions are not only prediction problems.
Many businesses need to choose the best allocation, route, schedule, portfolio, or production plan under real constraints. Linear programming, mixed-integer programming, constraint programming, heuristics, and simulation make those trade-offs explicit.
Solvers as tools for agents.
An agent can translate a business request, gather the right inputs, and invoke an approved solver. The solver ensures that the recommendation satisfies capacity, timing, policy, and resource constraints before it reaches a user.
Combine learned signals with deterministic decisions.
Forecasts, probabilities, anomaly scores, and learned costs can feed the optimization model. This creates a system that predicts uncertainty and then chooses a feasible action.
Evaluate the decision, not only the model.
Quality includes objective value, constraint satisfaction, robustness, sensitivity, runtime, and the effect on the operating process—not only predictive accuracy.