A new benchmark, WorkArena++, has been introduced to evaluate the performance of WebAgents. The best-performing agent on this benchmark achieved 0% accuracy, while human evaluators obtained 94%. In related developments, a new technique called Agent Workflow Memory (AWM) has been introduced to improve the performance of LLM-based models on long-horizon tasks with complex action trajectories. AWM allows agents to store and reuse workflows from past experiences, leading to significant improvements. Specifically, AWM improves the relative success rate by 24.6% and 51.1% on Mind2Web and WebArena tasks, respectively, while reducing the number of steps required to complete these tasks. For more details, refer to the repo and abs.