通过 LLM 批处理生成 100 个以上代理设置的方法
📄 中文摘要
在社交媒体模拟中，自动化数百个 AI 代理的配置需要系统地定义时间、事件、活动模式和响应延迟等多种属性。手动处理会显著增加时间和错误。MiroFish 通过基于 LLM 的配置自动生成来加快这一过程，系统接收模拟需求、文档和知识图谱作为输入，从而生成每个代理的详细属性。然而，由于 LLM 的局限性（如输出截断、JSON 格式错误和令牌限制），需要采取一些实施策略，包括逐步生成配置、批处理以避免上下文超限、JSON 恢复和错误处理、在 LLM 失败时应用基于规则的默认值、按类型应用代理活动模式以及验证生成值的有效性。
📄 English Summary
LLM 배치 처리로 100개 이상 에이전트 설정 생성 방법
Automating the configuration of hundreds of AI agents in social media simulations requires systematic definitions of various attributes such as time, events, activity patterns, and response delays. Manual processing significantly increases time and errors. MiroFish accelerates this process through LLM-based automatic configuration generation, where the system takes simulation requirements, documents, and knowledge graphs as input to generate detailed attributes for each agent. However, due to the limitations of LLMs (such as truncated outputs, JSON format errors, and token limits), several implementation strategies are necessary, including stepwise configuration generation, batch processing to prevent context overflow, JSON recovery and error handling, applying rule-based defaults in case of LLM failures, applying activity patterns by agent type, and validating the generated values.