第1章 大模型——Agent的大脑 Ch01 LLM — Agent's Brain
从预测下一个词到涌现推理能力
From next-token prediction to emergent reasoning
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从预测下一个词到涌现推理能力
From next-token prediction to emergent reasoning
Agent = Model + Harness,ReAct循环与三年进化
Agent = Model + Harness, ReAct loop and 3-year evolution
从Prompt到Context到Harness,三年三次范式演进
From Prompt to Context to Harness, three paradigm shifts
ReAct循环、有向图、多Agent协作
ReAct loop, directed graphs, multi-agent collaboration
Function Calling、MCP协议与工具设计
Function Calling, MCP protocol and tool design
短期/长期记忆、RAG与知识编译
Short/long-term memory, RAG and knowledge compilation
代码沙箱、Docker容器与E2B云沙箱
Code sandboxes, Docker and E2B cloud sandboxes
状态管理、检查点与持久化
State management, checkpoints and persistence
三层安全防线与人类审批
Three-layer defense and human approval
用自然语言构建AI产品
Building AI products with natural language
LangGraph/CrewAI/AutoGen/Dify对比
Comparison of LangGraph/CrewAI/AutoGen/Dify
可观测性、评估与成本优化
Observability, evaluation and cost optimization
自进化Agent、具身智能与行业渗透
Self-evolving Agents, embodied intelligence and verticalization
四家 harness 对照,以及灵犀(Lumina)产品结构与落地
Four harnesses compared, plus Lumina (灵犀) product structure