《智能體AI銀河搭便車指南：從基礎到系統》

摘要

《代理型AI搭便車指南》是一本專為建構自主AI系統的從業者所撰寫的完整參考書。本書涵蓋從基本原理到生產部署的完整技術棧，並圍繞一個核心論述組織內容：打造優秀的代理型系統需要理解管線的每一層，而非僅聚焦於單一環節。開篇探討LLM基礎層——Transformer架構、GPU系統、訓練與微調（SFT、LoRA、MoE）、模型壓縮及推理優化——將這些視為必備基礎而非主要焦點。隨後深入對齊與推理層：基於人類反饋的強化學習（RLHF）、PPO、DPO及其變體、GRPO、獎勵建模，以及針對大型推理模型的強化學習（包含思維鏈與測試時擴展）。下半部分專注於代理型AI本身，主題包括：代理型訓練與基於軌跡的強化學習、檢索增強生成（RAG與代理型RAG）、記憶系統（上下文內、外部、情節性與語義記憶）、代理框架設計與上下文管理，以及代理設計模式的分類學。跨代理協調部分進行深入探討：模型上下文協定（MCP）、代理技能與工具使用、代理間通訊協定（A2A），以及涵蓋集中式、分散式與階層式拓撲的多代理架構。最後以代理開發框架、代理型UI設計、代理型任務的評估方法論以及生產部署作結。每一章皆將嚴謹的理論基礎與實作指引、程式碼範例及原始文獻參考緊密結合。

English

The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate -- transformer architecture, GPU systems, training and fine-tuning (SFT,LoRA, MoE), model compression, and inference optimization -- treated as essential foundations rather than the primary focus. It then develops the alignment and reasoning layer: reinforcement learning from human feedback (RLHF), PPO, DPO and its variants, GRPO, reward modeling, and RL for large reasoning models including chain-of-thought and test-time scaling. The second half is devoted to agentic AI proper. Topics include agentic training and trajectory-based RL, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design and context management, and a taxonomy of agent design patterns. Inter-agent coordination is covered in depth: the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) communication protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies. The book concludes with agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment. Each chapter pairs rigorous theoretical foundations with implementation guidance, code examples, and references to the primary literature.