HarnessIR: An Ontology-Guided Intermediate Representation for Cross-Runtime Transfer of Agent Harnesses

Agent Harnesses encode reusable operational knowledge that determines how an agent organizes and executes under different runtime conditions. However, this knowledge is often tightly coupled with framework-specific control structures and runtime-specific execution designs, making it difficult to reuse and verify a tuned agent system once it is separated from its original implementation. We study how to represent harness-level behavioral knowledge independently of a specific runtime and preserve its key behavioral constraints when the implementation environment changes. To address this problem, we introduce HarnessIR, an ontology-based, runtime-independent intermediate representation (IR) for representing and reusing behavioral designs embedded in Agent Harnesses. A Harness ontology provides a common conceptual foundation for Harness execution, allowing framework-specific constructs from different runtimes to be related within a shared semantic space. Based on this foundation, HarnessIR separates a Harness into semantic mechanisms and execution policies: the former capture reusable semantic objects and their stable dependencies, while the latter specify how these mechanisms are selected, composed, and constrained under different execution conditions. Through this intermediate representation, behavioral designs accumulated in a Harness can be separated from a specific framework implementation and realized again in heterogeneous runtimes. We evaluate HarnessIR on HarnessIR-Bench, a benchmark we construct for systematic evaluation. Across 10 source Harnesses, HarnessIR achieves 45.0% Behavior Success, compared with 34.0% for a static ontology-based baseline, indicating improved reuse and preservation of Harness behavior across heterogeneous implementations.