This research project aims to design a decentralized and autonomous resource management system for heterogeneous cloud networks. Building on the shift toward distributed architectures driven by concerns over data sovereignty and performance, the project seeks to move beyond the traditional centralized control plane model used in Kubernetes. Each organization involved in a federation of clusters would be represented by an intelligent agent, capable of negotiating resource placement according to its own objectives while preserving data confidentiality. The interaction among these agents is modeled as a multi-agent game, where incentive mechanisms are designed to reach a mutually beneficial equilibrium. The project plans to formalize the problem, adapt multi-agent reinforcement learning methods to the challenges of distributed settings (such as fault tolerance and asynchronous communication), and develop a functional implementation in Rust. In doing so, it lays the groundwork for a new paradigm of collaboration among cloud service providers.