Optimizing AI Agent Planning with Operations Research and Data Science

As organizations increasingly adopt multi-agent AI systems, managing their escalating costs can be addressed using optimization models from operations research. This approach frames agent planning as a mathematical problem with decision variables, real-world constraints like budgets, and a clear objective such as minimizing cost. The article details how to apply four standard optimization patterns: the set-covering, assignment, knapsack, and network problems. These models offer practical solutions for common business challenges, like selecting the minimum number of agents to cover all required skills or assigning agents to projects to maximize value.