The algorithmic study of the principal-agent framework is an emerging frontier for algorithmic game theory. We extend this model by incorporating knapsack constraints to capture real-world resource limitations. To address the computational challenges arising from these constraints, we develop approximation algorithms that guarantee near-optimal outcomes for both the principal and agents. Our research contributes to the understanding of contract design in complex environments.
