附录 B:参考文献
本附录列举全书引用的学术论文、技术规范和行业报告,按主题分类。开源工具和学习资源见附录 C。
学术论文与研究
智能体基准与评估
GAIA: A Benchmark for General AI Assistants
Mialon, Fourrier 等(Meta, Hugging Face 等机构),2023
https://arxiv.org/abs/2311.12983 | archive.org
涵盖三个难度等级的约 466 个任务,用于评估智能体推理和工具使用能力
WebArena: A Realistic Web Environment for Building Autonomous Agents
Zhou 等(CMU),2023
https://arxiv.org/abs/2307.13854
812 个现实网站自动化任务,覆盖电商、社交论坛、协作软件开发与内容管理四域
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Jimenez 等(Princeton & UChicago),2023(ICLR 2024)
https://arxiv.org/abs/2310.06770 | archive.org
2294个真实GitHub问题,评估代码理解和修改能力
AgentBench: Evaluating LLMs as Agents
Liu 等(Tsinghua University 等),2023
https://arxiv.org/abs/2308.03688
跨 8 个领域的多类别基准任务
SkillOpt: Executive Strategy for Self-Evolving Agent Skills
Yang 等(Microsoft Research 等),2026
https://arxiv.org/abs/2605.23904
将自然语言 Skill 文档作为冻结智能体的外部可训练状态,通过 scored rollout、受控编辑和 held-out 验证门禁优化可复用 Skill
提示词工程与优化
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Google, 2022
https://arxiv.org/abs/2201.11903
基础论文,展示逐步推理如何改善LLM能力
ReAct: Synergizing Reasoning and Acting in Language Models
Google & Princeton, 2023
https://arxiv.org/abs/2210.03629
推理与行动结合的智能体框架原理
安全性与对抗性
Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Greshake, Abdelnabi 等(CISPA, Saarland University 等),2023
https://arxiv.org/abs/2302.12173
间接提示注入攻击的系统分类与实际危害分析,涵盖数据窃取、蠕虫传播等攻击向量
Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)
Ruan 等(University of Toronto、Vector Institute、Stanford 等),2024(ICLR 2024)
https://arxiv.org/abs/2309.15817
用 LM 模拟工具执行环境,评估智能体安全风险;36 个高风险工具 + 144 个测试用例
Agent-SafetyBench: Evaluating the Safety of LLM Agents
Zhang, Cui 等,2024
https://arxiv.org/abs/2412.14470
349 个交互环境、2000 个测试用例,覆盖 8 类安全风险和 10 种常见失败模式
长期记忆与推理
In-Context Learning and Induction Heads
Anthropic, 2022
https://arxiv.org/abs/2209.11895
理解LLM如何利用上下文进行学习
技术文档与规范
Anthropic官方文档
Claude API Documentation
Anthropic, 2026
https://platform.claude.com/docs/en/home | archive.org
Claude模型的API使用、限制、最佳实践
Model Context Protocol (MCP) Specification
Anthropic 发起,Linux Foundation 托管,2024-2026
https://modelcontextprotocol.io/specification/latest 和 https://github.com/modelcontextprotocol/modelcontextprotocol | archive.org
工具定义和交互的开放标准协议;最新版本以官方 specification/latest 页面为准
Claude Code Documentation
Anthropic, 2026
https://code.claude.com/docs/en/overview 和 https://code.claude.com/docs/en/permissions
Harness框架特定文档,含权限、路径校验、护栏细节
国际标准
NIST AI Agent Standards Initiative
NIST CAISI(Center for AI Standards and Innovation),2026
https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative | archive.org
美国国家标准与技术研究院发起的 AI 智能体标准化工作,涵盖互操作性和安全等方面
IEEE Standards for Autonomous Systems
IEEE, 2024
https://standards.ieee.org/initiatives/autonomous-intelligence-systems/standards/
自主系统的行为、安全、可靠性标准
开源框架文档
LangChain Documentation
LangChain, 2023-2026
https://docs.langchain.com/oss/python/langchain/overview
Agent、工具、链式推理、记忆管理等
LlamaIndex (formerly GPT Index)
Jerry Liu & team, 2023-2026
https://www.llamaindex.ai
数据连接与检索增强生成(RAG)
AutoGen: Enabling Next-Gen Large Language Model Applications
Microsoft, 2023
https://microsoft.github.io/autogen
多智能体框架和对话工程
行业报告
State of Agent Engineering
LangChain, 2026(调研于 2025-11/12,1,340 名受访者)
https://www.langchain.com/state-of-agent-engineering
57% 的组织已在生产运行智能体系统;智能体工程的成熟度和挑战分析
Magic Quadrant for AI Application Development Platforms
Gartner, 2025(该领域首份 MQ)
https://www.gartner.com/en/documents/7188230
智能体框架和工具的市场定位和评估
工程实践博客
Building workflows for agents with Skills and Interpreters
Hunter Lovell(LangChain),2026-05-29
https://www.langchain.com/blog/interpreter-skills
Deep Agents 的 Interpreter Skills:将确定性子流程封装为可导入的 TypeScript 模块,由
SKILL.md声明何时调用、由解释器在 Harness 内执行,兼顾工作流确定性与智能体自主性
Harness design for long-running application development
Prithvi Rajasekaran(Anthropic),2026-03
https://www.anthropic.com/engineering/harness-design-long-running-apps
长程智能体应用的 Harness 设计实践;定义“上下文焦虑”(context anxiety),主张以上下文重置等手段支撑长时自主任务(本书 2.2、4.5、8.3.4、14.3 引用)
获取方法:大多数论文可通过 arXiv、Google Scholar、官方网站免费获取。开源项目均可通过 GitHub 访问。商业工具通常提供免费试用。
最后更新于
