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Agentic AI in enterprise = harnessin...

Ranjan, Sumit.

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Agentic AI in enterprise = harnessing agentic AI for business transformation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Agentic AI in enterprise/ by Sumit Ranjan, Divya Chembachere, Lanwin Lobo.
其他題名:	harnessing agentic AI for business transformation /
作者:	Ranjan, Sumit.
其他作者:	Chembachere, Divya.
出版者:	Berkeley, CA :Apress : : 2025.,
面頁冊數:	xxv, 406 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction to Enterprise Agentic AI -- Chapter 2: Designing with the Well-Architected Agentic AI Framework -- Chapter 3: Architectural Patterns for LLM Adoption in Agentic AI -- Chapter 4: Enhancing LLMs for Agentic AI: RAG vs. Fine-Tuning -- Chapter 5: Mastering Prompt Engineering in Enterprise Agentic AI -- Chapter 6: Vector Databases in AI Applications in Enterprise Agentic AI -- Chapter 7: Ethical and Security Considerations in Enterprise Agentic AI -- Chapter 8: Case Studies: Agentic AI - Real-World Applications -- Chapter 9: AI AGENTS- Future Trends in Enterprise AI -- Chapter 10: Conclusion - The Age of Enterprise Agentic AI.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Industrial applications. -
電子資源:	https://doi.org/10.1007/979-8-8688-1542-3
ISBN:	9798868815423

Agentic AI in enterprise = harnessing agentic AI for business transformation /
Ranjan, Sumit.

Agentic AI in enterpriseharnessing agentic AI for business transformation /[electronic resource] :by Sumit Ranjan, Divya Chembachere, Lanwin Lobo. - Berkeley, CA :Apress :2025. - xxv, 406 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Enterprise Agentic AI -- Chapter 2: Designing with the Well-Architected Agentic AI Framework -- Chapter 3: Architectural Patterns for LLM Adoption in Agentic AI -- Chapter 4: Enhancing LLMs for Agentic AI: RAG vs. Fine-Tuning -- Chapter 5: Mastering Prompt Engineering in Enterprise Agentic AI -- Chapter 6: Vector Databases in AI Applications in Enterprise Agentic AI -- Chapter 7: Ethical and Security Considerations in Enterprise Agentic AI -- Chapter 8: Case Studies: Agentic AI - Real-World Applications -- Chapter 9: AI AGENTS- Future Trends in Enterprise AI -- Chapter 10: Conclusion - The Age of Enterprise Agentic AI.

This book delves into the transformative power of Enterprise Agentic AI, tracing its evolution from basic automation to intelligent agents capable of contextual reasoning, memory retention, and autonomous decision-making. It provides a strategic roadmap for enterprises looking to integrate Agentic AI seamlessly into their operations while ensuring scalability, efficiency, and security. Readers will explore architectural best practices, including cloud, hybrid, and on-premises deployment models, and gain insights into LLM optimization strategies like Retrieval-Augmented Generation (RAG) and fine-tuning. The book also covers advanced prompt engineering techniques, the role of vector databases in AI-driven applications, and governance frameworks to ensure ethical, transparent, and responsible AI adoption. Through real-world case studies, the book illustrates AI's impact across retail, healthcare, supply chain management, and customer engagement. It also examines the next wave of AI advancements, such as autonomous decision-making, AI-augmented leadership, and the evolving synergy between human expertise and intelligent agents in enterprise settings. By the end of this book, readers will have the knowledge and tools to design, deploy, and manage AI agents that are not only cutting-edge but also aligned with enterprise security, governance, and ethical standards. You Will: Understand how AI agents go beyond traditional models by incorporating contextual reasoning, long-term memory, and autonomous decision-making to enhance enterprise operations. Explore scalable deployment models (cloud, hybrid, on-premises) and best practices for integrating LLMs, vector databases, and prompt engineering into your AI workflows. Develop robust AI governance frameworks, conduct risk assessments, and implement security protocols to safeguard enterprise data while ensuring responsible AI adoption. Gain insights into transparency, accountability, and fairness in AI deployments, ensuring AI agents align with corporate values and global ethical standards.

ISBN: 9798868815423

Standard No.: 10.1007/979-8-8688-1542-3doiSubjects--Topical Terms:

653318
Artificial intelligence
--Industrial applications.

LC Class. No.: TA347.A78

Dewey Class. No.: 658.514

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