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

arXiv: https://arxiv.org/abs/2603.12031 · HTML: https://mlsanity.com/paper/2603.12031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Reinforcement Learning with Non-Markovian Rewards](https://mlsanity.com/paper/1912.02552/) (2019)
- [ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning](https://mlsanity.com/paper/2106.03051/) (2021)
- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing](https://mlsanity.com/paper/2001.03929/) (2020)
- [Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization](https://mlsanity.com/paper/2403.07905/) (2024)
- [A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks](https://mlsanity.com/paper/2002.02806/) (2020)
- [Crisis-Resilient Portfolio Management via Graph-based Spatio-Temporal Learning](https://mlsanity.com/paper/2510.20868/) (2025)
- [Containerisation for High Performance Computing Systems: Survey and Prospects](https://mlsanity.com/paper/2212.08717/) (2022)
- [A Decentralized Multiagent-Based Task Scheduling Framework for Handling Uncertain Events in Fog Computing](https://mlsanity.com/paper/2401.02219/) (2024)

## Abstract (arXiv)

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 vector resource balancing. Agentic-Kube integrates a bipartite Graph Convolutional Network to capture dynamic host-pod dependencies, a two-stage monotonic QMIX value factorisation network to maintain joint action value coherence, and a plurality voting consensus mechanism with action feasibility masking against allocatable node predicates. We evaluate the framework across live heterogeneous Google Kubernetes Engine deployments and macro-scale cluster environments spanning 50 to 1,000 nodes under empirical Alibaba trace data, diurnal microservice variations, and flash-crowd bursts. Across physical and simulated evaluations, Agentic-Kube consistently achieves Pareto-efficient placements. In diurnal microservice workloads, it reduces anti-affinity service collisions to 7.11%, representing a 53.0% relative reduction compared to the default Kubernetes scheduler. Under Alibaba traces, the policy achieves a 65.15% spot instance allocation ratio, while macro-scale benchmarks demonstrate scaling up to 1,000 nodes with mean decision latencies under 17ms and 99th-percentile latencies under 31ms, executing without container restart failures and operating well within standard scheduling admission timeouts.