{"product_id":"9798868831201","title":"Enterprise Intelligent Agents with LLMs A Practical Guide to Multi-Agent Enterprise Frameworks, LLM Ecosystems, and Real-World AI Deployment Strategies","description":"\u003ch1\u003eEnterprise Intelligent Agents with LLMs\u003c\/h1\u003e\u003ch2\u003eA Practical Guide to Multi-Agent Enterprise Frameworks, LLM Ecosystems, and Real-World AI Deployment Strategies\u003c\/h2\u003e\u003ch3\u003eAnvesh Reddy Minukuri | Venkata Gunnu | Shubham shah | Varshitha Yendapally\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eBusiness \u0026amp; Economics \/ Information Management\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp data-start=\"0\" data-end=\"707\"\u003eThe book begins by explaining how AI is shifting from simple text prediction to autonomous, reasoning-driven systems that can plan, act, and learn. Readers will understand the principles behind agentic architectures, the distinction between LLM-based apps and true AI agents, and how modern frameworks enable memory, tool use, and collaboration. This section establishes the foundation for designing intelligent, goal-oriented agents capable of operating within complex enterprise ecosystems.\u003c\/p\u003e\r\n\u003cp data-start=\"709\" data-end=\"1481\" data-is-last-node=\"\" data-is-only-node=\"\"\u003eFurther, the book transitions from theory to hands-on practice, showcasing frameworks such as LangChain, AutoGen, CrewAI, LangGraph, and Semantic Kernel. Each framework is explored through practical examples, code walkthroughs, and architectural insights that help readers build and deploy scalable agent systems in real-world settings. \u003cbr\u003e\u003cbr\u003eThe final section explores how agentic AI is transforming industries automating DevOps, enhancing customer support, and powering decision intelligence while addressing key challenges in security, governance, and ethical AI design. Complete with benchmarking guidance and future trends, this book is an indispensable guide for architects, developers, and enterprise leaders looking to operationalize AI agents at scale.\u003c\/p\u003e\r\n\u003cp data-start=\"709\" data-end=\"1481\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003cstrong\u003eYou Will:\u003c\/strong\u003e\u003c\/p\u003e\r\n\u003cul type=\"disc\"\u003e\r\n\u003cli class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in;\"\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eLearn how agentic AI differs from traditional LLM\/SLM systems and why it matters\u003c\/span\u003e\u003c\/li\u003e\r\n\u003cli class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in;\"\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eUnderstand step-by-step implementation of agent frameworks like LangChain, AutoGen, and CrewAI\u003c\/span\u003e\u003c\/li\u003e\r\n\u003cli class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in;\"\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003ePractise how to deploy agents in cloud-native, serverless, or hybrid environments\u003c\/span\u003e\u003c\/li\u003e\r\n\u003cli class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in;\"\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003emaster methods for evaluating, securing, and governing autonomous AI systems\u003c\/span\u003e\u003c\/li\u003e\r\n\u003c\/ul\u003e\r\n\u003cp style=\"line-height: normal;\"\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003e \u003cstrong\u003eThis book is for : \u003c\/strong\u003eData scientists, t\u003c\/span\u003e\u003cspan style=\"font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eechnical product managers and AI strategists exploring enterprise automation.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eAnvesh Minukuri  is an IT professional with 13+ years of experience, including 11 years in AI\/ML and Generative AI. He currently serves as VP, Senior Lead GenAI Engineer at JPMorgan Chase, specializing in NLP and enterprise GenAI applications. A passionate educator and innovator, Anvesh teaches seasonal pro-bono courses in AI\/ML and GenAI at Oklahoma State University, where he also advises the MIS Corporate Analytics Program. He has authored 100+ predictive models, filed patents in AI, and earned multiple awards for delivering innovative AI\/ML solutions that drive business outcomes. Anvesh actively leads firm-wide AI training programs and mentors’ students and professionals across academic and corporate settings. He holds a master’s degree in Data Mining \u0026amp; Predictive Analytics from Oklahoma State University and a bachelor’s in computer science from JNTU University, India. An MBA candidate (2024) at the Gies College of Business (UIUC), he is also a frequent speaker and contributor at industry events, including SAS Analytics 2023. The LLM \u0026amp; SLM Handbook (2025) is his latest contribution to the AI community.\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: 0in; line-height: normal;\"\u003e\u003cspan style=\"mso-bidi-font-size: 10.0pt; font-family: 'Arial',sans-serif; color: black; mso-font-kerning: 14.0pt; mso-bidi-font-weight: bold;\"\u003eVenkat Gunnu is a Senior Executive Director of Knowledge Management and Innovation at JPM Chase. He is an executive with a successful background crafting enterprise-wide data and data science solutions, GenAI, process improvements, and data and data science-centric products. Concept to implementation strategist with demonstrated success controlling multiple projects that elevate organizational efficiency while optimizing resources. Data-focused and analytical with a track record of automating functions, standardizing data management protocol, and introducing new business intelligence solutions.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"mso-bidi-font-size: 10.0pt; line-height: 115%; font-family: 'Arial',sans-serif; color: black; mso-font-kerning: 14.0pt; mso-bidi-font-weight: bold;\"\u003eShubham is a Software Engineer with expertise in machine learning, cloud technologies, and AI-powered solutions. I have experience developing and optimizing systems like Retrieval-Augmented Generation (RAG) models, integrating AI technologies like ChatGPT and Mistral for smarter, real-time information retrieval. Skilled in building scalable microservices and cloud-based architectures, I’m passionate about solving complex challenges, improving system performance, and driving technological innovation. Always eager to learn, collaborate, and stay ahead in the fast-evolving tech landscape.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"mso-bidi-font-size: 10.0pt; font-family: 'Arial',sans-serif; color: black; mso-font-kerning: 14.0pt; mso-bidi-font-weight: bold;\"\u003eVarshitha is a Full-Stack Lead Engineer with expertise in designing and building scalable, cloud-native applications and microservice architectures. I specialize in developing distributed systems, optimizing performance, and leading cross-functional teams to deliver resilient, production-grade solutions. I also bring experience in integrating large language models (LLMs) into real-world applications. Passionate about bridging cloud technologies with applied AI, I enjoy solving complex challenges, mentoring teams, and staying ahead in the rapidly evolving tech landscape.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e04 January 2027\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eApress\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eApress\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9798868831201\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003ePaperback \/ softback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":51223538565260,"sku":"9798868831201","price":44.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/lateknightbooks.com\/products\/9798868831201","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}