The execution harness is a critical component of LLM agents, shaping how the agent interacts with its environment and organizes multi-step execution. Prior work has shown that improving tools, workflows, and other harness components can substantially improve agent performance. This suggests that the harness can be treated as a learnable control layer that adapts execution decisions from experience, rather than as a predetermined execution configuration. In this work, we introduce HarnessMDP, a learning-based framework that makes harness control itself learnable. HarnessMDP formulates agent execution as a finite-horizon Markov decision process, where the harness learns to select structural execution actions from the evolving agent state. The harness policy is learned offline from collected trajectories using task performance and deployed online to adaptively control new executions. Extensive experiments on coding tasks, $τ$-bench, AgentBench, and other agent evaluation settings show that HarnessMDP consistently improves execution behavior and final task performance over baseline harnesses. These results demonstrate the effectiveness of HarnessMDP and establish learnable harness control as a practical mechanism for improving LLM agents through better execution. Our code is available at https://github.com/Hik289/Agentic-RL-harness.git.

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