Procedural memory for language agents typically accumulates as trajectories, scripts, or task-specific skills, so memory grows with the number of interactions rather than the number of distinct procedures learned. We propose a workflow-centric procedural memory framework that continually consolidates repeated experience into a bounded set of natural-language workflows. These workflows are merged online under outcome-conditioned update policies. On ALFWorld and ScienceWorld, our memory is competitive with trajectory-level procedural memory on task success while using 3.5× fewer memory tokens on average (1.1-9.1× across settings), showing that representation besides construction, retrieval and updating is also a key design axis for scalable procedural memory.