Purpose IR: Compiling User Purpose for Privacy-Preserving Agent Workflow State

Abstract

LLM-agent workflows routinely transform rich observations into plans, summaries, memories, handoffs, and other reusable state. This creates a privacy boundary that task relevance alone does not capture: an observation may be needed as evidence for a task without its full semantics being authorized to persist. We call this mismatch the purpose-to-state semantic gap. The key distinction is that evidence authority is not release authority. We introduce Purpose IR, a bounded typed intermediate representation that compiles natural-language user purpose into an executable semantic boundary for reusable agent state. A Purpose program is an acyclic semantic dataflow graph in which evidence leaves specify task-required semantics, typed dependencies constrain authorized derivations, and declared sinks determine which values or actions may cross the reusable-state boundary. A deterministic compiler constructs and validates the authoritative program, while a typed runtime reconstructs reusable state only from validated sink outputs. Under explicit execution contracts, this design yields release confinement and purpose noninterference, characterizing what factual semantics may be released and which observation differences must become irrelevant to reusable state. Across 1,200 cases spanning LangGraph, CrewAI, and MobileRun, Purpose IR achieves 99.6% Privacy, 83.3% Task, and 83.3% Privacy-Compliant Success (PCS), outperforming the strongest high-privacy baseline by 27.5 PCS points. On 493 PrivacyLens trajectories, it achieves the highest Safe Action Rate at 77.3%, 10.4 points above the next-best method, extending the same boundary from reusable state to downstream actions. These results show that privacy-preserving agent state requires more than filtering sensitive or task-irrelevant content: it requires an explicit, executable representation of what the user's purpose authorizes to survive.