怎么做 Long-running Agents，Cursor、Anthropic 给了两种截然不同的思路

Core Viewpoint - The article discusses advancements in long-running AI agents, focusing on two approaches: Cursor's multi-agent parallel collaboration and Anthropic's memory continuity for single agents [4][27]. Group 1: Cursor's Approach - Cursor aims to execute complex, long-term tasks by running multiple agents in parallel, similar to human team collaboration [4][8]. - The initial attempts at coordination faced challenges, including inefficiencies due to locking mechanisms and a lack of accountability among agents [10][12]. - The introduction of role differentiation among agents—Planners, Workers, and Judges—improved project coordination and scalability [15][21]. - Successful experiments included building a web browser from scratch, generating over 1 million lines of code, and migrating a large codebase, demonstrating the effectiveness of the new structure [17][19]. Group 2: Anthropic's Approach - Anthropic focuses on maintaining memory continuity for agents across multiple work sessions, addressing the limitations of context windows [27][28]. - The dual-agent system consists of an Initializer Agent to set up the project environment and a Coding Agent to execute tasks incrementally [34][39]. - This method emphasizes structured task management and thorough testing, significantly improving the accuracy of functionality verification [42][46]. - Open questions remain regarding the potential for specialized agents in various domains beyond web development [53].