Agentic Interpretation: Harness-Governed Evidence Construction for Software Agents

Yifeng He, Yanjun Wang, Siwei Cui, Aritra Sengupta, Murali Krishna Ramanathan

Software agents answer behavioral questions about code, such as whether a patch passes tests or which lines caused a failure. These tasks require repository-wide evidence, yet most scaffolds leave intermediate findings in free-form trajectories, making them hard to reuse and easy to disconnect from final answers. We present Agentic Interpretation (AgInt), a framework for harness-governed evidence construction. The model proposes typed records, while a deterministic harness admits, grades, and persists them, reports unresolved requirements, and licenses answers under task-specific rules. The resulting certificate gives the model persistent external state and leaves an auditable decision record. Formally, append-only certificate states form a grounding lattice ordered by record inclusion, where each admitted record extends the state by union. We evaluate AgInt with four models on SWE-bench-Live and DeepSWE for patch acceptance and Defects4J and GitBug-Java for fault localization. AgInt outperforms the plain agent and Agentic Code Reasoning on all reported patch-acceptance metrics and ranks responsible lines higher while inspecting fewer irrelevant lines in fault localization. For…