Generality on Demand: Progressive Behavioral Consolidation for Web Agents

Web agents often invoke a model again for routines they have already solved, paying for fresh action selection on every repetition. We study whether verified experience can instead become executable behavior that still recovers from deployment drift. Progressive Behavioral Consolidation (PBC) compiles repeated successes into parameterized workflows. At a live failure it returns control to an agent, then persists a recovery only if the resulting patch passes replay, regression, and held-out validation. On Gmail, PBC achieves 0.998 success, comparable to the strongest baselines (0.986–0.992), while reducing agent dependence from 1.000 to 0.306. Its final artifact executes 0.80/0.80 of isolated and cumulative drift states without a model. On GitLab, dependence falls to 0.111 and tokens to 0.34M per stream, with 0.948 success versus 1.000 for the full-solver baselines. Main-table results average three independent draws. Among the gate’s rejections, what blocks a recovery is preserving behavior the system has already verified, not the novelty of the fix. Verified recovery thus changes how later tasks execute: the agent handles unresolved cases, while the workflow serves behavior the system has learned to verify.