Agentic Ai 2 0
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Narrow And General Intelligence Embodied Self Referential Social Cognition And Novelty Production In Humans Ai And Robots
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Author : Karl Friston
language : en
Publisher: Frontiers Media SA
Release Date : 2026-01-26
Narrow And General Intelligence Embodied Self Referential Social Cognition And Novelty Production In Humans Ai And Robots written by Karl Friston and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-26 with Technology & Engineering categories.
A multi-disciplinary group of Topic Editors have been assembled to help take stock of the phenomenal success of narrow Statistical Artificial Intelligence and to examine new perspectives on achieving Artificial General Intelligence (AGI). A unique virtual roundtable was held on 5th November 2021 to accompany the Research Topic to critically examine the above themes while drawing on the considerable expertise of the Topic Editors and participants. If you missed it, do please visit this link to wait the YouTube recording: https://fro.ntiers.in/NGIYT The Research Topic is led and was discussed in the roundtable by: Karl Friston (Professor of Neuroscience, University College London): Free Energy Principle (FEP) and Bayesian Brain models, Predictive Coding and Deep Learning ANNs, Novelty exploration and FEP. Georg Northoff (Professor of Neuroscience and Mental Health, University of Ottawa Institute of Mental Health Research): Self-referential brain processes; Multi-modal integration of self and other; ‘Nothing for Free’ in General AI. Tony Prescott (Professor of Cognitive Robotics, University of Sheffield): Embodied Cognition, Sense of Self for intelligence and robot design, Bio-inspired Robotics, AI, agency and ethical issues. Emily Cross (Professor of Social Robotics, University of Glasgow and Macquarie University): Social robots; Neuroscience of motor-physical social interaction and coordination, Theory of mind and human-robot interaction. Sheri Markose* (Professor of Economics, University of Essex): Complex Adaptive Systems, Evolvability and viral software-based transposons in genomic intelligence, Offline ‘Self-Ref’/‘Self-Rep’ mirror systems for self-other nexus, Strategic novelty and Gödel Incompleteness. *Sheri Markose (Associate Editor) moderated the Roundtable. -- The remarkable success of Statistical AI (SAI) with Deep Learning and Artificial Neural Networks (ANNs) marks the current AI scene. The conditions that bring about the success of narrow AI militate against general intelligence. These include the non-trivial problem of extrapolating from given input data; curated environments that abstract from ‘in the wild’ circumstances with adversarial software agents that can hack and fake; narrowness of objectives and rewards that can lead to brittle outcomes and ‘bad robot’ problems. As is increasingly being understood, adaptability that goes beyond the standard optimization model with prespecified choice sets requires complex external control over ANNs and their plasticity. This limits their autonomy and capacity for novelty production. Most of all, models of optimization in SAI manifest a near absence of principles of decentralized control such as that of distributed ledger technologies, which can enhance robustness, trustworthiness and security of black box AI outputs in ensembles of AI agents. It is a long-held view that AGI aims to emulate the human brain which marks the apogee as a prototype for general intelligence. The offline embodied sentient self is known to be the basis of an empathic Theory of Mind and higher-order human cognition with open-ended search for adaptive homeostasis for preserving somatic identity. This runs into orders of magnitude of 1015 – 1030 that exceed the germline genome size many times over. This self-referential information processing, however, comes at a price of autoimmune disease and neuropsychiatric illness relating to the self-other nexus. Hence, our premise is that ‘nothing is for free' in general intelligence. Molecular and evolutionary biology in the post-Barbara McClintock era has identified the role of viral-based transposable elements (TEs), that scissor paste and copy-paste, for genomic evolvability (45% of the human genome) and real-time somatic neural plasticity. However, TEs have to be kept in check for potential malign activity. This neurobiology for evolvability, as indicated, like the regulatory principles behind selection of status quo and adaptation resulting in extended phenotypes, often manipulative of others, is yet to be fully understood. Just as the success of SAI need not be marred by a lack of evidence for whether the brain does backpropagation, AI that recognizes affective states in humans need not ‘feel’ the same. The big push in AGI has been in neuro-robotics which necessarily involves multi-modal embodied integration of the sensorimotor pathways of the body schema often mimicking principles from grid-like mappings found in the hypothalamus for spatial-temporal memory and navigation. Brain-AI interface can now harness commands from the brain to operate devices outside it as in neural prosthetics. Thus, computational neuroscience behind brain scanning and mind-reading has an exciting future. But with that potential ethical problems will emanate from involuntary mind control and bio-digital malware. This Research Topic aims to gather a series of original research articles, mini-reviews, reviews and novel perspectives covering, but not limited to, the following aspects of AGI : • Challenges for Artificial General Intelligence • Novelty exploration and Free Energy Principle • Embodied Multi-modal Integration of Self-Other-Environment in Robots • Self-reference, Self-Representation and 3-D Self-Assembly of digitized bio-materials • Moravec's paradox • Evolution without Objectives and Open-ended Novelty Search • Deepfakes and Generative Adversarial Networks (GANs) • Role of adversarial bio-software like transposons for evolvable intelligence • Neuro-memetic AI and Robots • Social Robots • Theory of mind and human-robot interaction • Bad Robot problem • Distributed Ledger Technologies for genomic regulatory networks and AI systems • Brain-AI -Machine Interface: Neural Prosthetics and Neural hacking • Ethical and regulatory issues from neural AI and Robots
Data Science Ai And Applications
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Author : Shivakumara Palaiahnakote
language : en
Publisher: Springer Nature
Release Date : 2026-01-01
Data Science Ai And Applications written by Shivakumara Palaiahnakote and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-01 with Computers categories.
The three-volume set CCIS 2681–2683 constitutes the post-conference proceedings of the First International Conference on Data Science, Artificial Intelligence and Applications, ICDSAIA 2025, held in Dhaka, Bangladesh, during July 18–19, 2025. The 99 full papers included in this book were carefully reviewed and selected from 190 submissions. They focus on latest advancements in data science, artificial intelligence (AI), and their applications across diverse sectors—including healthcare, education, finance, governance, agriculture, and sustainable development—highlighting its potential to solve pressing societal challenges and accelerate progress toward the Sustainable Development Goals (SDGs).
Navigating The Data Minefields
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Author : Scott M. Shemwell
language : en
Publisher: CRC Press
Release Date : 2025-06-13
Navigating The Data Minefields written by Scott M. Shemwell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-13 with Computers categories.
Volumes have been written on the need for high-quality data to support organizational decision-making. Most of those books appear to focus on the development and sustainment of data from the standpoint of those directly responsible for the management of data stores and the use of technology necessary to acquire, store, and secure data sets. Navigating the Data Minefields: Management’s Guide to Better Decision-Making provides executives and subject matter experts (SMEs) with a "reasonable" set of useful tools they can adapt to their specific organization and operating environment. While complexity can never be taken out of an integrated system, decision-making can be facilitated by using metrics that take into consideration the quality of the data used to make decisions, i.e., risk mitigation. Professionals who depend on large high-quality data sets, such as senior and mid-level management, engineering SMEs, data scientists, IT, systems engineers, and medical professionals, will want to have this book in their decision-making arsenal.
Ai Chatgpt Agent Gemini Nanobanana Manus Skywork Ai
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Author : 謝孟諺（Mr.GOGO）
language : zh-CN
Publisher: 財經傳訊（台灣廣廈）
Release Date : 2026-01-15
Ai Chatgpt Agent Gemini Nanobanana Manus Skywork Ai written by 謝孟諺（Mr.GOGO） and has been published by 財經傳訊（台灣廣廈） this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-15 with Computers categories.
讓 AI 成為你身邊最強的職場夥伴 一個人，也能擁有一支高效率虛擬團隊 用 AI 接手流程，把時間還給更重要的事 本書讓你真正學會「指揮 AI 工作」 AI 不再只是工具，而是一位真正能「工作」的數位員工。在 2026 年的今天，我們迎來職場史上最關鍵的轉折點：AI 從「會回答問題的工具」，正式進化成「會工作、會主動行動、能完成任務的數位員工」。如同書中序言所揭示，AI 已不只是幫助我們加快速度，而是能取代大量流程、理解需求、執行任務，甚至像老練助理一樣主動提醒、補充資訊、避免錯誤。本書以「打造你的 AI 虛擬工作團隊」為主軸，從行政助理、專案經理、行銷人員、創作者到企業決策者，完整示範如何用 ChatGPT Agent、Skywork、Manus AI、Lovart、Nanobanana、Julius AI 等工具，訓練出各種角色的 AI 數位員工，並讓這些「AI 同事」真正融入你的日常工作流程。 在傳統職場裡，我們經常被迫在有限的八小時中完成十幾小時的任務：文件處理、簡報製作、數據分析、專案管理、行銷內容產出、角色設計、程式確認……這些任務讓知識工作者長期深陷疲於奔命的壓力。本書要告訴你：這些都不必再由你親手完成。AI 數位員工的到來，就像突然擁有一支看不見、但隨時為你工作的團隊——它們不會疲倦、不會忘記、不會分心，只要你給予目標，它們就能產出成果。這不只是一場效率革命，更是你個人職涯生產力徹底升級的起點。 告別單打獨鬥，擁抱 AI 團隊協作 五大職位 × 五種數位員工角色：打造專屬你的 AI 工作團隊 本書最核心的價值，就是讓讀者掌握「不同職位需要不同類型的 AI 員工」的完整方法。第二章中，讀者將學會如何利用 PDNob + ChatGPT Agent 打造「行政助理型 AI」：它能處理複雜 PDF、校對合約、生成郵件回覆、安排會議，甚至連結 Gmail 自動管理收件匣。第三章則帶你進入「專案經理型 AI」的世界，Skywork 能自動生成簡報、研究架構；Proactor AI 能在會議中立即錄音、摘要、擷取行動項目、提出風險提示，如同一位「主動式會議副駕駛」。 第四章與第五章展現 AI 在「內容產出 × 創意工作」中的驚人能力： Gemini + Manus AI 讓行銷人員只需一小時就能完成三國市場文案、提案簡報、行銷漏斗網站； Lovart + Nanobanana 讓設計師從「手工製圖」轉型為「創意總監」，能在極短時間產生多風格視覺、建立一致 IP 角色並產出漫畫與貼圖。 第六章介紹企業決策者的兩名重量級 AI 幕僚 ChatArt 與 Julius AI，使主管能在一小時內完成原本需兩週的市場研究與財報分析。 超越單點工具，建構自動化工作流 從工具到團隊：讓 AI 自動運作、彼此串聯、形成工作閉環 本書最大的亮點，是教你如何讓 AI 不只是單點使用，而是「彼此協作」。真正的生產力爆發，不是來自單一工具，而是讓多位 AI 數位員工「接力完成完整專案」。例如，一位行銷人員的完整工作流可能是這樣：ChatGPT Agent 整理資料 → Gemini 生成多語文案與簡報 → Manus AI 做漏斗頁面 → Lovart 製作主視覺 → Nanobanana 做角色素材 → Julius AI 分析成效。這是一條完整的「AI 生產線」，也是本書最具革命性的核心精神：讓 AI 團隊做事，而不是你做事。 本書也提供「個人、小型團隊、中型企業、大型企業」的 AI 團隊配置表（附錄 A），讓讀者明確知道該從哪個組合開始、預算如何規劃、能替代多少人力。此外本書也清楚說明 AI 的限制，提醒讀者要成為「AI 的 CEO」：AI 會有幻覺、偏見與理解限制，而你要做的是檢查、調整與做出最終判斷——不是盲目信任，也不是害怕使用。 未來職場：一場關於「人」的文藝復興，你將成為掌控 AI 的主管 本書在第七章揭露 AI 崛起將帶來的真正變革：人類並非被取代，而是被解放。AI 將代替你完成可計算、可預測的左腦工作，讓人類重新專注在溝通、判斷、創意、領導等右腦領域。未來的高價值人才，不是「做很多事的人」，而是「會指揮 AI 做事的人」。因此，本書實質上是一本「AI 管理學」教材，教你如何拆解任務、下達指令、審核成果，並讓 AI 變成你的虛擬部門。 你會從執行者，變成： – AI 工作流程的設計者 – AI 員工的訓練主管 – AI 輸出品質的審核者 – AI 結果背後的決策者 從工具使用 → 流程設計 → 團隊協作 → 職涯升級，本書是陪你完成整個 AI 轉型的完整指南。它讓你真正具備面對下一個十年的能力：掌握 AI，而不是被 AI 牽著走。 本◇書◇特◇色 讓AI真正融入每日工作 許多 AI 書籍只介紹工具功能，但本書不同，它以「職場角色」為核心，帶領讀者看見 AI 如何真正融入每日工作。行政助理可用 PDNob + ChatGPT Agent 解決文件負擔；專案經理依靠 Proactor AI + Skywork 讓開會到簡報完全自動化；行銷人員靠 Gemini + Manus AI 完成文案、簡報、行銷網站；設計師透過 Lovart + Nanobanana 建立一致角色與高質感視覺；決策者則由 ChatArt + Julius AI 組成策略幕僚與資料科學團隊。讀者將第一次清楚看到：「我今天的工作，有多少可以由 AI 替我做？」而本書提供的分類、拆解與流程模板，讓讀者可以立即把 AI 導入自己的日常工作。 超越單點工具，建構自動化「AI 生產線」 市場上大多數 AI 書籍教的是「如何用一個工具」，但真正的效率革命來自「流程自動化」。本書最強的地方，就是示範如何讓 AI 員工彼此接力：你可以讓 AI 員工分工接力，一個負責整理資料、一個生成內容、另一個設計視覺，還有專責製作網站與進行數據分析的成員。本書提供完整「職位 × 工作流模板」，包括行銷、設計、專案、行政與決策等角色。讀者不需要猜測工具如何串接，也不需要從零開始，而是可以直接「套用」一條完整的 AI 生產線。 不只學使用，更要學領導：駕馭 AI 的終極心法 本書最珍貴的部分，就是不只教你「如何使用 AI」，更教你「如何避免錯誤、如何判斷、如何領導 AI」。書中不僅深入解析 AI 的限制（如資料偏見、幻覺、隱私與情感缺失等問題），更進一步指出未來職場所需的新能力——AI 策略設計力、提示詞拆解力、工作流配置力、結果審核力。這些技能將成為未來十年最稀缺的人才條件。本書讓讀者不只提升效率，更提升「不可取代性」，真正成為能帶領 AI 團隊的現代職場領袖。
Achieve Advance Flourish
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Author : Fred Sanfilippo
language : en
Publisher: Springer Nature
Release Date : 2025-08-21
Achieve Advance Flourish written by Fred Sanfilippo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-21 with Medical categories.
This informative reference will help full-time, part-time, and affiliated medical school faculty navigate the inspiring, evolving, and complex challenges of achieving success in their academic positions. The easy-to-read format poses frequently asked questions and provides answers that convey valuable insights, useful references, and practical advice. Designed for use by faculty at all stages of their career, this handbook addresses issues about selecting a career in academia, starting out in one’s first position, advancing successfully, skill development, transitioning to other positions, and retirement. It provides guidance about the development of the personal and professional skills needed to thrive in these roles, and summarizes advice about creating programs, collaborations, and successful teams. Helpful tips about career planning and successfully advancing in the promotions and tenure process are enumerated. Recognizing that most faculty will have to face challenges at some point in their career, this handbook also outlines useful recommendations for dealing with difficult colleagues, crisis management, and work-personal life balance. Given the rapid evolution of knowledge and organizations in healthcare, science, and medicine, this reference emphasizes the importance of adapting to change and embracing the opportunities for innovation and future impact. Highlighting both the increasing challenges of these roles and the inspiring opportunities available through these careers, the authors provide comprehensive information to help medical school faculty achieve impact in their disciplines, advance their careers, and flourish both personally and professionally.
Building Agentic Ai System With Rag 2 0
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Author : MADISON. HAYES
language : en
Publisher:
Release Date : 2025
Building Agentic Ai System With Rag 2 0 written by MADISON. HAYES and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
2025 2
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Author : 哈佛商業評論全球繁體中文版
language : zh-CN
Publisher: 遠見天下文化出版股份有限公司
Release Date : 2025-02-01
2025 2 written by 哈佛商業評論全球繁體中文版 and has been published by 遠見天下文化出版股份有限公司 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-01 with Antiques & Collectibles categories.
領導人應善用自己的「比較優勢」 領導工作千頭萬緒，常讓人不知從何開始。其實，領導人，特別是身負變革重任的領導人，要將自己寶貴的時間，投入沒有人能像自己做得這麼好的事，也就是自己具有「比較優勢」的事項。 寶僑（Procter & Gamble）前執行長賴夫利（A.G.Lafley）便是一個將自己的「比較優勢」發揮得淋漓盡致的例子。賴夫利在2000年和2015年間兩度擔任寶僑執行長，帶領寶僑轉虧為盈。他把一般認為執行長必須親自處理的工作，包括與投資人互動和對外活動等，交給擅長的同事來處理。他自己則親身參與當時寶僑最需要的——創新，因為他很清楚寶僑當時已因為不創新而失去成長的動能，而創新是他擅長的。他堅持參與與消費者的互動，並建立了將消費者洞見轉化為商品的完整流程；同時他也明白寶僑需要從外部引進更多創新，因此在該公司的「連結＋開發」（Connect+Develop）網站上徵求新構想。賴夫利運用他的比較優勢成功地將寶僑轉型（見〈事「不必」躬親的領導人〉）。 另一個區分領導力高下的是，處理複雜問題的能力。複雜問題的特色包括：不可預測、缺乏明確的解決方案，以及涉及各種不同利害關係人。舉例來說，2015年，印度的監管機關指出，雀巢在該國銷售額占30％的美極速食麵，它的鉛和味精含量都超標；儘管雀巢自家的測試顯示產品安全無虞，但媒體很快將此問題炒作成全國性的議題，威脅到雀巢在印度的業務。印度雀巢首先釐清問題的本質，他們了解到，這問題部分是因為民族主義者不樂見外國品牌太受歡迎，印度雀巢於是調整解決的策略到政治社會的層次。接著雀巢建立蒐集內外部意見的機制，以掌握不可預測的變化。最後，雀巢擴大和利害關人互動的範圍，包括監管機關、消費者、零售商、媒體和輿論領袖，終於讓美極得以重返印度市場（見〈問題是「繁複」還是「複雜」？〉）。 許多企業都在尋找「學習領導人」（可能是學習長或人資長）來帶領全公司學習。然而，企業處於不同情況，需要嫻熟不同學習風格的學習領導人。一般而言，學習領導人有三種風格（或能力），第一種像是「監護者」，他們善於帶領員工做工具性的學習，敦促員工補足新技能。第二種是「挑戰者」，他們講求人本主義，提倡個人化學習，讓組織得到更多元的觀點，找尋到改變的機會。第三種是「連結者」，注重的是，運用學習的機會把員工連結起來，他們認為這種交流帶來的價值不亞於教育內容的本身。領導人應該先自問，你的組織目前的願景是什麼，然後去找到最適合的學習領導人來帶領員工學習（見〈探索三種組織學習之道〉）。 自生成式AI問世以來，企業即非常關注它的發展。然而，數據專家戴文波特（Thomas Davenport）提醒，企業不要因此忽略了推出已久的「分析式AI」。企業應善用這兩種AI，特別是生成式AI可以充當分析式AI的對話介面，讓原本沒有AI能力的人也能加入，企業會更容易推行以數據為基礎的轉型（見〈生成式AI與分析式AI，比一比！〉）。 總編輯 鄧嘉玲
Building Agentic Ai System With Rag 2 0
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Author : Theo Marris
language : en
Publisher: Independently Published
Release Date : 2025-06-24
Building Agentic Ai System With Rag 2 0 written by Theo Marris and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
Agentic AI is transforming how intelligent systems operate-moving beyond static responses to dynamic, tool-using, goal-driven behavior. At the heart of this evolution is Retrieval-Augmented Generation 2.0 (RAG 2.0), a new architectural pattern that fuses long-term memory, contextual reasoning, multi-agent coordination, and modular tool use for building advanced AI systems that act, learn, and adapt over time. This book delivers a practical blueprint for applying RAG 2.0 to real-world agentic workflows across enterprise, healthcare, education, and automation sectors. Written by a seasoned AI practitioner and technical author specializing in LLM architectures, this guide is grounded in the latest research, including SafeRAG best practices, LangChain, LlamaIndex, Pinecone integration patterns, DSPy, GraphRAG, and AGI-aware agent design. Every chapter reflects current industry trends, community-driven implementations, and field-tested methodologies that have emerged from the leading AI labs and open-source communities. "Building Agentic AI System with RAG 2.0" is your complete roadmap to designing, implementing, and deploying powerful, scalable, and intelligent agents using the next generation of Retrieval-Augmented Generation techniques. Covering everything from system pipelines and memory management to prompt chaining, multi-agent orchestration, hallucination control, and ethical deployment, this book equips developers, architects, and AI enthusiasts with actionable insights and full-stack expertise. Whether you are building AI copilots, enterprise search assistants, autonomous agents, or educational tutors, this guide will accelerate your journey from experimentation to production readiness. Explore cutting-edge topics including vector databases and hybrid retrieval strategies, adaptive memory structuring, multi-modal extensions (GraphRAG & VideoRAG), safe deployment architectures, long-term personalization techniques, and cost-effective optimization. Detailed case studies demonstrate agentic AI in action across finance, clinical decision support, education, and more. Practical node-based examples using LangChain, LlamaIndex, and DSPy are provided throughout-designed to ensure hands-on application. This book is written for AI developers, data scientists, software engineers, ML ops practitioners, and anyone building advanced AI systems with LLMs. Whether you're transitioning from basic LLM use to advanced agent orchestration, or leading technical teams in deploying autonomous reasoning frameworks, you'll find clear guidance, practical architecture blueprints, and real-world use cases to elevate your skills. Stop building fragile prototypes and start engineering future-proof, scalable AI systems. The RAG 2.0 framework enables long-term performance, lower hallucination risk, and flexible integration across tools and memory-so your applications remain relevant, reliable, and continually evolving with new data and user feedback. This book is built for today's LLM stack and tomorrow's intelligent agents. Unlock the full potential of AI agents today. Buy "Building Agentic AI System with RAG 2.0" now and take the next step toward mastering Retrieval-Augmented Generation, declarative agent design, and production-grade agentic architecture. Start building intelligent, scalable systems that reason, remember, and act-on your terms.
Agentic Artificial Intelligence
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Author : John Cook
language : en
Publisher: Top Tier Press
Release Date : 2025-12-12
Agentic Artificial Intelligence written by John Cook and has been published by Top Tier Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-12 with Computers categories.
🚀 Turn Artificial Intelligence into Your Smartest Business Partner 📈 What if AI could do more than just follow commands? What if it could think, plan, and act alongside you—amplifying your decisions, accelerating results, and unlocking new levels of innovation? Agentic Artificial Intelligence is your definitive playbook for working with the next generation of AI—systems that go beyond automation to become proactive, goal-driven partners in progress. This is where theory ends and transformation begins. Forget the buzzwords. This book delivers a clear, actionable roadmap for understanding, building, and collaborating with agentic AI—tools that analyze, decide, and execute with autonomy. Whether you’re a leader, entrepreneur, or innovator, you’ll learn how to turn AI from a concept into a decisive advantage. In this book, you’ll master how to: ✅ Unlock the agency advantage — Discover why agentic AI is redefining the limits of productivity, innovation, and strategy. ✅ Master the anatomy of an agent — Learn the core elements that give AI goals, memory, reasoning, and adaptability. ✅ Navigate the autonomy spectrum — Find the right balance between human guidance and machine independence. ✅ Build seamless human + agent teamwork — Delegate intelligently, accelerate performance, and maintain oversight. ✅ Implement ethics and safeguards — Create intelligent systems that build trust, not risk. ✅ Access the exclusive “AI in Action” resource — A downloadable toolkit with real-world examples, templates, and frameworks to help you apply every concept immediately. This isn’t theory, it’s a roadmap to the future of intelligent collaboration. You’ll gain the mindset, methods, and mastery to make AI a partner that works with you, not just for you. If you’ve ever wondered, “How can I make AI work for me?” — this is your answer. The future of work belongs to those who lead with intelligence—human and artificial. Don’t just adapt to the AI era. Command it. Get your copy of Agentic Artificial Intelligence today—and start building your smartest business partner.
Building Agentic Ai Systems
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Author : Anjanava Biswas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-04-21
Building Agentic Ai Systems written by Anjanava Biswas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-21 with Computers categories.
Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader Key Features Understand the foundations and advanced techniques of building intelligent, autonomous AI agents Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems Explore crucial aspects of trust, safety, and ethics in AI agent development and applications Book DescriptionGain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks. Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents. Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.What you will learn Master the core principles of GenAI and agentic systems Understand how AI agents operate, reason, and adapt in dynamic environments Enable AI agents to analyze their own actions and improvise Implement systems where AI agents can leverage external tools and plan complex tasks Apply methods to enhance transparency, accountability, and reliability in AI Explore real-world implementations of AI agents across industries Who this book is for This book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.