Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems by Dr. Ali Arsanjani

Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems by Dr. Ali Arsanjani

Paperback
$64.99
Sale price  $64.99 Regular price 
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Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems by Dr. Ali Arsanjani

Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems by Dr. Ali Arsanjani

$64.99
Sale price  $64.99 Regular price 
Format: Paperback
ISBN: 9781806029570.0
FormatPaperback

**Intact Gift Giving Condition book**

This book is your guide to building intelligent agents powered by LLMs. Transform GenAI experiments into production-ready intelligent agents with scalable AI systems, architectural patterns, frameworks, and responsible AI and governance best practices Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Build robust single and multi-agent GenAI systems for enterprise use Understand the GenAI and Agentic AI maturity model and enterprise adoption roadmap Use prompt engineering and optimization, various styles of RAG, and LLMOps to enhance AI capability and performance Purchase of the print or Kindle book includes a free PDF eBook Book Description Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI.

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