Suffice: Inferring Scopes and Interfaces for Reusable Agent Execution with Sufficient Information Flow

Long-horizon agents often repeat similar tool calls and recovery steps across tasks, motivating the abstraction of repeated agent execution into reusable callable units (e.g., skills and task workflows) to reduce context and decision costs. Such abstraction therefore appears to require a tradeoff between omitting intermediate information that may alter subsequent decisions and retaining excessive history that erodes its benefits. We present Suffice, which solves this problem by deriving precise conditions on the information that must cross an abstraction boundary to preserve agent decisions. We further prove that passive trajectories generally cannot uniquely identify this information. Suffice uses controlled replay queries to hold candidate information fixed while varying other information and observing whether the agent's decision changes. Based on these queries, it identifies the minimal information required to preserve each decision and uses it to construct callable units. Across 200 synthetic programs, Suffice achieves an F1 score of 0.974 in recovering decision dependencies and exactly recovers 96.0% of abstraction boundaries. On AppWorld, Suffice improves Scenario Goal Completion (SGC) over the existing method ICAL by up to 23.5%, while achieving comparable performance to retaining all observed dataflow with 48.7% fewer interface tokens. Overall, agent execution abstraction appears to require a tradeoff between efficiency and task performance. Suffice shows that it need not.