An Illustrated Guide to AI Agents: Concepts and Code for Building Agents with Llms, Tools, and Memory (Paperback)
暫譯: AI Agents 圖解指南:使用 LLM、工具與記憶體建構 Agent 的概念與程式碼(平裝本)
Grootendorst, Maarten, Alammar, Jay
- 出版商: O'Reilly
- 出版日期: 2026-10-06
- 售價: $2,780
- 貴賓價: 9.5 折 $2,641
- 語言: 英文
- 頁數: 448
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798341662698
- ISBN-13: 9798341662698
-
相關分類:
Large language model
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相關主題
商品描述
Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand and effectively work with this emerging technology.
Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.
- Explore the core architecture of AI agents: tools, memory, and planning
- Understand reasoning LLMs, multimodal models, and multi-agent collaboration
- Learn advanced methods, including distillation, quantization, and reinforcement learning
- Evaluate real-world applications, strengths, and limitations of AI agents
商品描述(中文翻譯)
人工智慧正進入新的階段。AI agents 現在已能以日益提升的自主性進行推理、規劃與行動。從加速科學突破到支援創意工作,這些系統正迅速重塑各行各業與日常生活。本書提供理解這項新興技術並有效運用所需的概念基礎與實務洞見。
Maarten Grootendorst 與 Jay Alammar 透過數百幅清晰易懂的圖解,說明 AI agents 如何建構、如何思考,以及未來將走向何方。本指南適合專業人士、學生與充滿好奇心的學習者,不只停留在熱門話題的表面,更深入揭示這些系統內部實際發生的運作、其重要性,以及如何在真實世界的情境中應用相關知識。《An Illustrated Guide to AI Agents》是探索人工智慧下一個前沿領域不可或缺的參考指南。
• 探索 AI agents 的核心架構:工具、記憶與規劃
• 了解推理型 LLM、multimodal models 與 multi-agent collaboration
• 學習包括 distillation、quantization 與 reinforcement learning 在內的進階方法
• 評估 AI agents 在真實世界中的應用、優勢與限制