Evidence-Guided Updates for Reusable Agent Workflows

Language-model agents reuse executable workflows to make recurring work efficient, but changing user requirements demand updates that preserve existing responsibilities. Correct examples can identify a new decision without specifying when it should apply, allowing an otherwise successful change to disrupt other uses of the workflow. Starting from an existing program and a small set of correct public execution examples, we propose evidence-guided local updates that combine execution constraints with inherited applicability conditions. The program's conditions are bound across function calls before input adaptation, preserving the behavior of uses unaffected by the new requirement. To reduce the cost of selecting reliable changes, we replay captured decisions to screen bounded candidates and use native execution to accept the selected program. On authored reconciliation workflows, inherited conditions improve evidence-registration success from 60% to 100%. For identical final programs, replay reduces incremental acquisition time by 61.7% relative to native interpreter reuse with early stopping.