AI Summary (English)
Title: Building Effective Agents
Summary:
Anthropic's article details best practices for building effective large language model (LLM) agents. It emphasizes simplicity, starting with basic prompts and only increasing complexity (like using agentic systems) when necessary. The article outlines several workflows (prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer) and discusses the design and implementation of agents, highlighting the importance of well-defined toolsets and thorough testing. Successful agent implementation prioritizes simplicity, transparency, and a well-crafted agent-computer interface (ACI).
The most successful LLM agent implementations utilize simple, composable patterns rather than complex frameworks. Workflows, where LLMs and tools are orchestrated through predefined code paths, are suitable for well-defined tasks. Agents, where LLMs dynamically control their processes and tool usage, are better for flexible, model-driven decision-making at scale. The article provides examples of each workflow and emphasizes the importance of starting with direct LLM API calls before considering frameworks. Augmenting LLMs with retrieval, tools, and memory is crucial for building effective agentic systems. The article also stresses the importance of prompt engineering for tools, suggesting that as much effort should be invested in creating good agent-computer interfaces (ACI) as in human-computer interfaces (HCI). Two successful applications of agents are highlighted: customer support and coding agents. Both benefit from the ability to combine conversation, action, clear success criteria, feedback loops, and human oversight.
Key Points:
1) 💡 **Prioritize Simplicity:** Begin with basic prompts and only add complexity (agentic systems) when simpler solutions fail.
2) ⚙️ **Choose the Right Approach:** Use workflows for predictable, well-defined tasks; use agents for flexible, model-driven decision-making.
3) ⛓️ **Workflow Types:** The article details five key workflows: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer, each suited to different task types.
4) 🤖 **Agent Design:** Agents are LLMs using tools based on environmental feedback in a loop; prioritize simplicity, transparency (showing planning steps), and a well-crafted ACI.
5) 🧰 **Tool Engineering:** Invest significant effort in tool design and documentation; consider the model's perspective and create clear, easy-to-use interfaces.
6) 🧪 **Thorough Testing:** Extensive testing in sandboxed environments with appropriate guardrails is crucial for agents due to their autonomous nature and potential for compounding errors.
7) 📈 **Successful Applications:** Customer support and coding agents are highlighted as particularly successful applications of LLM agents.
8) 🧱 **Building Blocks:** The fundamental building block is an augmented LLM enhanced with retrieval, tools, and memory. The Model Context Protocol is suggested as a method for integration.
Summary:
Anthropic's article details best practices for building effective large language model (LLM) agents. It emphasizes simplicity, starting with basic prompts and only increasing complexity (like using agentic systems) when necessary. The article outlines several workflows (prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer) and discusses the design and implementation of agents, highlighting the importance of well-defined toolsets and thorough testing. Successful agent implementation prioritizes simplicity, transparency, and a well-crafted agent-computer interface (ACI).
The most successful LLM agent implementations utilize simple, composable patterns rather than complex frameworks. Workflows, where LLMs and tools are orchestrated through predefined code paths, are suitable for well-defined tasks. Agents, where LLMs dynamically control their processes and tool usage, are better for flexible, model-driven decision-making at scale. The article provides examples of each workflow and emphasizes the importance of starting with direct LLM API calls before considering frameworks. Augmenting LLMs with retrieval, tools, and memory is crucial for building effective agentic systems. The article also stresses the importance of prompt engineering for tools, suggesting that as much effort should be invested in creating good agent-computer interfaces (ACI) as in human-computer interfaces (HCI). Two successful applications of agents are highlighted: customer support and coding agents. Both benefit from the ability to combine conversation, action, clear success criteria, feedback loops, and human oversight.
Key Points:
1) 💡 **Prioritize Simplicity:** Begin with basic prompts and only add complexity (agentic systems) when simpler solutions fail.
2) ⚙️ **Choose the Right Approach:** Use workflows for predictable, well-defined tasks; use agents for flexible, model-driven decision-making.
3) ⛓️ **Workflow Types:** The article details five key workflows: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer, each suited to different task types.
4) 🤖 **Agent Design:** Agents are LLMs using tools based on environmental feedback in a loop; prioritize simplicity, transparency (showing planning steps), and a well-crafted ACI.
5) 🧰 **Tool Engineering:** Invest significant effort in tool design and documentation; consider the model's perspective and create clear, easy-to-use interfaces.
6) 🧪 **Thorough Testing:** Extensive testing in sandboxed environments with appropriate guardrails is crucial for agents due to their autonomous nature and potential for compounding errors.
7) 📈 **Successful Applications:** Customer support and coding agents are highlighted as particularly successful applications of LLM agents.
8) 🧱 **Building Blocks:** The fundamental building block is an augmented LLM enhanced with retrieval, tools, and memory. The Model Context Protocol is suggested as a method for integration.
AI Summary (Chinese)
Title: 建立有效的代理
Summary:
本文档详细介绍了 Anthropic 建立有效的大型语言模型 (LLM) 代理的最佳实践。它强调简单性，从基本提示开始，仅在必要时才增加复杂性（例如使用代理系统）。本文概述了几种工作流程（提示链、路由、并行化、调度器-工作者和评估器-优化器），并讨论了代理的设计和实现，重点介绍了明确的工具集和彻底测试的重要性。成功的代理实现优先考虑简单性、透明性和精心设计的代理-计算机接口 (ACI)。
最成功的 LLM 代理实现利用简单的可组合模式，而不是复杂的框架。在预定义的代码路径中协调 LLM 和工具的工作流程，适用于定义明确的任务。代理，其中 LLM 动态控制其流程和工具使用，更适合大规模的灵活的、模型驱动的决策。本文提供了每种工作流程的示例，并强调了在考虑框架之前先进行直接 LLM API 调用。用检索、工具和内存增强 LLM 对构建有效的代理系统至关重要。本文还强调了工具提示工程的重要性，建议在创建良好的代理-计算机接口 (ACI) 上投入的精力应与人-计算机接口 (HCI) 一样多。本文重点介绍了两种成功的代理应用：客户支持代理和编码代理。两者都受益于能够结合对话、行动、明确的成功标准、反馈循环和人工监督。
要点：
1) 💡 **优先考虑简单性：** 从基本提示开始，只有当更简单的解决方案失败时才增加复杂性（代理系统）。
2) ⚙️ **选择正确的方法：** 使用工作流程处理可预测的、定义明确的任务；使用代理处理灵活的、模型驱动的决策。
3) ⛓️ **工作流程类型：** 本文详细介绍了五种关键工作流程：提示链、路由、并行化、调度器-工作者和评估器-优化器，每种工作流程都适合不同类型的任务。
4) 🤖 **代理设计：** 代理是使用基于环境反馈循环的工具的 LLM；优先考虑简单性、透明性（显示计划步骤）和精心设计的 ACI。
5) 🧰 **工具工程：** 在工具设计和文档上投入大量精力；考虑模型的视角，并创建清晰易用的接口。
6) 🧪 **彻底测试：** 在沙盒环境中进行广泛的测试，并设置适当的防护措施，对于代理至关重要，因为代理具有自主性，并且可能出现累积错误。
7) 📈 **成功的应用：** 客户支持代理和编码代理被强调为 LLM 代理的特别成功的应用。
8) 🧱 **构建模块：** 基本构建模块是通过检索、工具和内存增强的增强型 LLM。本文建议使用模型上下文协议作为集成方法。