AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project assignment, and budgeting—as set covering, assignment, and knapsack optimization models in Python using Gurobi.

The post Optimizing AI Agent Planning with Operations Research and Data Science appeared first on Towards Data Science.

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