SPINE: Structured Policy Refinement with Intent-to-Execution Lineage for LLM Software Agents

As Large Language Models (LLMs) expand agentic workflows across complex systems, execution logs capture runtime actions but fail to answer key operational questions: which stakeholder intent prompted an action, which intermediate decision derived it, and who was responsible. Consequently, operators must reconstruct attribution post hoc rather than retrieve it directly from the execution record. We present SPINE, a policy refinement framework that represents stakeholder intents as declarative policies and progressively refines them into definitive and imperative policies with explicit refinement relationships. The resulting policy hierarchy establishes an end-to-end intent-to-execution lineage: every policy node records its parent, definer, and enforcer, while an orchestrator stamps the active policy identifier onto execution records at write time. Traceability is thus reduced to a parent-pointer traversal bounded by policy tree depth. Evaluated on SWE-bench Lite instances using two foundation models across four intent representations (68 evaluated runs, 62 resolved), SPINE preserves task resolution performance while achieving 100% (344/344) write-time provenance stamping compared to 0% for other representations. SPINE fundamentally replaces implicit trace reasoning with explicit, queryable semantic lineage by construction.