Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling

Cloud-native container orchestration requires resource schedulers capable of balancing infrastructure expenditure, fault resilience, and node utilisation. Conventional reinforcement learning approaches typically rely on monolithic single-agent models that suffer from gradient interference and reward dilution when mapping conflicting operational goals into a single scalar reward. We present Agentic-Kube, a cooperative multi-agent reinforcement learning framework designed for real-time Kubernetes pod placement. The architecture decomposes multi-objective scheduling into a tripartite optimisation space managed by dedicated sub-agents for cost minimisation, anti-affinity fault tolerance, and ...

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