Arca, an AI-native wealth management firm, uses agents to perform long-horizon financial work, such as analyzing client data and generating financial plans that can take up to ten days to complete. The firm has developed an internal memory system to track the progress of these tasks and ensure that human advisors are involved in the decision-making process. Arca's agents are designed to prompt humans for input, rather than the other way around, and the firm has implemented various safety measures to prevent agents from making decisions that could harm clients. AI summary
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This paper develops a system called RoboArchitect that automates the design of robotic agents by learning to optimize their harnesses, which determine when the agent interacts with the environment. A practitioner might care about this because it could significantly improve the performance of robotic agents without requiring significant updates to their underlying models.