Answer2Path: Outcome-Guided Acquisition of Reusable Web-Agent Experience

Successful web-agent trajectories are costly to acquire, and terminal success alone does not show whether an agent gathered evidence or learned a reusable procedure. Yet many tasks already have a known answer or desired final state, typically used only for evaluation. Can a small pool of such outcomes guide trajectory acquisition and seed further supervision? We propose Answer2Path (A2P), a closed-loop framework that uses outcomes during exploration and expands supervision from verified webpage evidence. For queries, the agent seeks a path supporting the known answer; for modifications, the desired state guides action and post-state verification. We compare when the same outcome is revealed and whether it explicitly guides evidence seeking. A condition-blind companion records evidence, and independently validated page facts become provenance-preserving task–outcome pairs. Fixed workflow induction then tests whether acquired trajectories help on held-out tasks without outcome access. On 61 WebArena Shopping queries over three seeds, A2P raises pooled official success from 40.4% to 84.7%; at fixed K, induced workflows raise held-out success from 88.3% to 97.2%. Across six modification families, eligible source success rises from 22.9% to 54.2%, while source tokens per success fall by 75.0%. Late guidance has the highest aggregate yield in a two-family placement study, and a two-round single-family pilot validates backend-free, evidence-grounded task expansion. Together, these results show how limited outcome supervision can improve procedural experience acquisition and seed further verified tasks.