12 篇文档带有标签「Advanced」

Techniques and tools for tracing, logging, and diagnosing failures in AI agent systems.

How to measure, benchmark, and systematically test AI agent performance in production and development.

Threats, attack vectors, and defensive techniques for securing AI agent systems in production.

DAG-based workflow orchestration for ML and data pipelines — operators, sensors, hooks, XComs, and scheduler architecture.

Distributed event streaming with Apache Kafka — topics, partitions, producers, consumers, and real-time ML feature pipelines.

Distributed data processing with Apache Spark — RDDs, DataFrames, Spark SQL, MLlib, and driver/executor architecture.

Directed acyclic graph workflows for agents — parallel execution, task dependencies, and dynamic graph construction.

Claude Code 中的模型上下文协议（MCP）——MCP 服务器是什么、它们如何扩展 Claude 的能力、如何安装和配置它们，以及如何构建自定义 MCP 服务器。

Agents that evaluate their own output and iteratively improve through reflection, critic agents, and the Reflexion framework.

Claude Code 如何使用提示缓存来降低延迟和 token 成本，通过在多次 API 调用中复用之前处理的系统提示、工具定义和对话前缀。

如何构建将 AI 应用程序连接到 MCP 服务器的 MCP 客户端——涵盖客户端初始化、能力发现、工具调用、资源读取和传输选择。

如何构建向任何 MCP 兼容的 AI 应用程序公开工具、资源和提示的 MCP 服务器——涵盖服务器设置、能力注册、传输配置和完整的服务器生命周期。