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大模型应用发展与Agent前沿技术趋势(中)-CSDN博客
大模型与Agent结合的深度技术架构 分层式Agent架构设计 随着大模型规模的不断增长,传统的Agent实现方式已难以满足高性能、高可扩展性的需求。现代大模型Agent系统通常采用分层式架构设计,将复杂的决策过程分解为多个功能模块,每个模块负责特定的子任务。这种架构不仅提高了系统的可维护性,还为性能优化提供了更多可能性。 分层式Agent架构通常包含以下核心组件: 感知层(Perception Layer):负责接收和处理原始输入数据,包括文本、图像、语音等多模态信息 理解层(Understanding Layer):基于大模型进行语义理解、意图识别和上下文建模 规划层(Planning Layer):制定长期目标和短期任务计划,进行路径规划和决策优化 执行层(Execution Layer):将规划转化为具体行动,与环境进行交互 记忆层(Memory Layer):存储和管理历史交互数据,支持长期记忆和上下文连贯性 学习层(Learning Layer):通过强化学习、监督学习等方式持续优化Agent策略...
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