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.

Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth. However, developing and evaluating such systems is challenging due to the inherent complexity of enterprise environments, where data is fragmented across multiple sources and governed by sophisticated access controls. We present EnterpriseBench, a comprehensive benchmark that simulates enterprise settings, featuring 500 diverse tasks across software engineering, HR, finance, and administrative domains. Our benchmark uniquely captures key enterprise characteristics including data source fragmentation, access control hierarchies, and cross-functional workflows. Additionally, we provide a novel data generation pipeline that creates internally consistent enterprise tasks from organizational metadata. Experiments with state-of-the-art LLM agents demonstrate that even the most capable models achieve only 41.8% task completion, highlighting significant opportunities for improvement in enterprise-focused AI systems.