构建AI代理系统：从核心部分到实践的思考

Overview - The article discusses the concept of "AI agents" and their evolving autonomy, highlighting the distinction between workflow-driven processes and AI agents that make decisions during execution [1] Workflow - Workflows can be visualized as a simple chart with predefined steps, where each node represents a task and arrows indicate the flow of operations [3] - Core workflow patterns include prompt chaining, routing, parallelization, coordinator-worker models, and evaluator-optimizer setups [4] AI Agents - AI agents are essentially large language models (LLMs) equipped with instructions and tools for external system interaction, operating through an execution loop [7] - Key components of AI agents include context management, session state, and long-term memory for retaining user preferences and ongoing tasks [7][8] Comparison of Workflow and AI Agents - A comparison table illustrates the differences between AI agents and workflows, emphasizing flexibility versus predictability, and the associated costs and error management [14] Advanced Considerations - The article outlines the need for safeguards, observability, evaluation, security, deployment, and identity persistence in AI agent systems [17][19][28][34][36][37] - Safeguards include technical and policy measures to ensure safe and legal operation, while observability allows for monitoring and troubleshooting of AI agent performance [17][19] Conclusion - The discussion emphasizes that building AI agent systems involves understanding various components and trade-offs, with no single "correct" approach, but rather a set of evolving best practices [38]